instruction
stringlengths 40
28.9k
|
|---|
I am trying to send some data over to my PC from the Arduipilot, I used a Normal USB connection to send over a recurring string like this:-
const AP_HAL::HAL& hal = AP_HAL_BOARD_DRIVER;
void setup()
{
hal.uartA->begin(38400);
}
void loop()
{
hal.scheduler->delay(20);
hal.console->println("Recd_String");
}
AP_HAL_MAIN();
I receive the string just fine when I open a serial monitor with baud of 38400 bits/sec. But, when I remove the USB port and plug in the 3DR radio module to the ardupilot and the PC, it gives me garbage. I know that the 3DR radios use MAVLink communication protocol, but I was wondering if it's possible to change this protocol and use a normal SPI so that I receive the data in the same format I receive when connected via USB.
If this is not possible, is there a way to convert this garbled data from the radio module to a useful string.
It would be greatly appreciated if someone can help me with this.
|
Does anybody know how to configure the node ID of an Ingenia Pluto DC Servo Drive?
I've got a request out to their support team, but perhaps somebody here is already familiar with these drive boards.
I do have Ingenia MotionLab 2.7.2, but it does not ship with documentation and the MotionLab user manual on the site is out of date (I had previously been looking through the hardware documentation, but it turns out the info was in MotionLab documentation; although the instructions for previous versions no longer seem to apply to 2.7.2).
|
I'm looking to make or buy something resembling an LED, that would be thin (about 0.5mm or less) and cheap (<0.1$ in mass production).
Any suggestions?
|
I'm designing a simple autopilot software on top of Ardupilot, my goal is to possibly interface an Raspi on top of ArduPilot Mega (APM). I am stuck on setting up a simulation environment using either V-Rep or Gazebo.
The quadcopter will have basic sensors plus advanced sensors. basic sensors talks directly with ArduPilot, while advanced sensors talks with my own autopilot software. I am trying to wrap my head around a feasible setup to test the software while using ArduPilot Mega in the Hardware-In-The-Loop. I am planning on having three stages of Simulation:
Stage 1. Simulate quadcopter physics in Gazebo/V-Rep, run ArduPilot software and my autopilot software in a VM (not sure if it's even do-able)
Stage 2. Simulate quadcopter physics in computer, run my autopilot software in a VM, and run APM in a hardware-in-the-loop fashion.
Stage 3. deploy my autopilot onto Raspi and interface with APM then run both hardwares in Hardware-in-the-loop fashion.
|
I implemented a bootstrap Particle filter on C++ by reading few Papers and I first implemented a 1D mouse tracker which performed really well. I used normal Gaussian for weighting in this exam.
I extended the algorithm to track face using 2 features of Local motion and HSV 32 bin Histogram. In this example my weighing function becomes the probability of Motion x probability of Histogram. (Is this correct).
Incase if that is correct than I am confused on the resampling function. At the moment my resampling function is as follows:
For each Particle N = 50;
Compute CDF
Generate a random number (via Gaussian) X
Update the particle at index X
Repeat for all N particles.
This is my re-sampling function at the moment. Note: the second step I am using a Random Number via Gaussian distribution for get the index while my weighting function is Probability of Motion and Histogram.
My question is: Should I generate random number using the probability of Motion and Histogram or just the random number via Gaussian is ok.
|
I'm working on a project where mains voltage will sometimes be disconnected, and the system will have to run on battery for as long as possible before doing a (safe) shutdown. The desired behavior is exactly like a laptop battery system:
When mains voltage is connected, charge the battery and power the system from mains
When mains voltage is disconnected, power the system from the battery
Prevent the battery system from supplying current when the batteries are discharged below a certain voltage (to prevent damage).
Is there a name for this type of system, or a name for the feature(s) that I should be looking for when I look at chargers? (If it matters, this system will be 12V, so I'm looking at 14.8V Lithium battery options.)
|
I want to implement my own GPS navigation for a quad-copter.
I can calculate and filter the GPS coordinates (latitude and longitude in degrees).
I believe the easiest approach for me would be, to calculate the change of the heading of the quad-copter from the current attitude to the destination point and let it fly straight on after turning.
However I am not sure about the 2D representation of the latitude/longitude-GPS coordinates (for a round earth to a 2D map system when calculating the heading change). How big is the expected error? Or is there none?
|
A robotic usually consists of joints with sections of possibly varying width connected together. Considering we know how much each is bent and the length of each section, and their location in 3D space (not local coordinates) at time zero; how do we determine how much each joint should rotate to goto position B from position A. Both A and B are defined in world cartesian coordinates.
Now each joint can move in terms or all at once, so should all joints move simultaneously or in turns?
|
I am trying to better understand the dynamics of forward flight in multirotors.
Assuming I have a quadcopter with 4 motor/propeller combinations capable (each) of a propeller pitch speed of, say, SpeedMax= 100 mph.
In forward horizontal flight, the quadcopter will pitch down at a certain angle, let's say AlphaP, from horizontal. If AlphaP is, say, 45 degrees, and drag is neglected, wouldn't the quadcopter be capable of a max theoretical speed of sin (45)* SpeedMax ~ 70Mph?
Also, seems to me AlphaP cannot go all the way to 90 degree (quadcopter flying like a plane), as at that point the propellers would not produce any upward thrust to maintain the copter aloft given there is no wing loading as available in a plane. If drag was to be neglected, what factors would the optimum AlphaP be depended on, and what would that angle be, for maximum speed?
|
I want to know how to write and run the correct code. I understand that I need to download OpenCV (which I have), but when I try to compile sample code - for example, Blob Detection - it doesn't compile. I am just very confused on the process of what you need to do to get something to show up on the screen.
I know my question is really vague, but I have such a bad understanding of Computer Vision that I don't really know how to describe my problem. Hopefully discussing more will be able to help me.
Please help me! I have been searching the Internet for 2 hours now and I am just lost in a sea of information...
|
I have a system in which I have two separate subsystems for estimating robot positions.
First subsystem is composed of 3 cameras which are used for detecting markers the robot is carrying and which outputs 3 estimates of the robot's position and orientation.
The second subsystem is a system which is located on the robot and is measuring speed on the two points of the robot. By numerically integrating those two I can get an estimate on the robot's position and orientation (because I am tracking two points at once).
The first system is less accurate but the second system drifts. First system gives output about once a second while the second one gives output much more frequently (100-200 times per second).
I assume there must be a better approach than to just reset the position with the first system's estimate (as it is not 100% accurate), but to also use the accumulated position from the second sensor system and fuse that with the new data from the first system. Also, there is a question how to fuse 3 estimates of the first system? There must be a better way than pure average as it might happen that the two estimates are exactly the same and the third one is completely different (meaning that it is probably more wrong)?
Do you have any fusion algorithms to recommend to use in such a system? I know about Kalman filter, but I am having trouble figuring out how to use it as the two systems output data at different frequencies.
I hope the question is clear enough, what is the best approach to fuse the estimates into a more correct and accurate estimate?
Thanks
|
I have a Raspberry Pi (Model B) attached to a Roomba. I'm using this part to bring the unregulated 17V+ power of the Roomba down the 5V/1A of the Pi. The problem is that the Pi will randomly reboot and cause peripherals (such as the bluetooth adapter) to freak out and not work. We can sometimes drive it around for a little while before it reboots, other times it happens almost immediately.
|
I am trying to build an autonomous multi coloured lines following robot.
The parts I have bought so far include:
Arduino Uno 3.
3 Colour Sensors (taos tcs 230).
L293DNE motor driver.
Robot chassis including 2 dc motors, 2 wheels and 1 caster wheel.
I am trying to figure on how to connect all these components together (for example: arduino to the colour sensor, l293d to motor). How do I connect it in order for the motor to rotate in both directions? Do I need to solder anything?
|
I'm considering building an absolute, indoor robot-positioning system based on ultrasound Time Of Flight. Transducers will be ordinary, narrow-band, 40 kHz ones.
Based on your experience, what is the best exactitude and precision we can achieve with such a system?
I'm aware that the answer to such a question will depend on many factors, both hardware and software (if applicable), but I'm not asking about the performance of one solution or another, but about the intrinsic limitations of the ultrasound technology.
|
More in the line of robotics observing their environment, I'm trying to implement a proximity sensor that can sense objects in front of it to a least up to $-30^ \circ \space$ to $\space+30^ \circ $ of it's direction of propagation.
There are only two ways I can think of
Multiple Infrareds. Con: more spacious
Fast-Motor. Con: expensive in money and time-complexity wise
I'm currently using a Proximity Sensor with up to 10ft distance capability
|
I am building a 6 wheeled rover, one set of 3 wheels will work together and other set of 3 wheels will run together, so current in each side may vary from 3A-15A(blocked rotor) and 6V i want to make a H-bridge(for controlling direction and speed(i ll use PWM) so i will require two such H-bridges. what is the copper track thickness i should use in proteus for making the design or else shall i go for manual soldering replacing tracks with wires. Can anybody suggest a design which is relatively easy to design with some protection circuit in it (for MCU pins isolation) or suggest any suitable motor controllers from TI or any company which can be apt to my problem
|
I'm thinking about building a SCARA Arm to lift moderate loads(5lbs) with a high degree of accuracy. I want a relatively quick and inexpensive Z axis gantry, and I was thinking about using a lead screw with dual linear rail. Trouble is I'm not certain the linear velocity will be fast enough.
What's the best method of choosing the lead screws and the associated nut, given a desired linear velocity of 10inches/second and a NEMA stepper motor driving it?
|
I'm trying to get a 6DOF pose solution for an object that'll be between 10 and 50 cm from a fixed point. I want to avoid putting too much special hardware on the object, but extra hardware on the fixed side is fine.
I've been looking into two general methods:
fiducial markers There are several software packages with different types of markers, but I haven't been able to find any information about them regarding precision or accuracy in short-range pose sensing.
ultrasonics I've found some commercial systems that do 6DOF pose sensing (e.g. hexamite), but they're expensive and require you to put transmitters on the object.
|
CAN301/402 provides Max Motor Speed (0x6080,0x00) and Max Profile Velocity (0x607F,0x00). In profiled motions, the maximum speed is limited to the lower of these two values. In non-profiled motions, the maximum speed is limited to Max Motor Speed.
What is the intended purpose of Max Profile Velocity, rather than only providing Max Motor Speed and using that everywhere instead?
|
Let's take a 6 DOF robotic structure. It's consisting of the 3 DOF global structure for the position - and the 3 DOF local structure for the orientation of the endeffector.
If the last 3 axis (of the local structure) are coincident in one point, the inverse kinematics can be solved analytically by decomposing it into a position- and orientation-problem.
But is it possible to solve the inverse kinematics analytically if the last 3 axis are NOT coincident in one point? I've read several papers that claim that due to high non-linearity of the trigonometric functions and motion complexity in 3D-space, a 6 DOF serial chain cannot be solved analytically.
Does anybody know if this is right?
|
We want to find available WiFi networks near.
So in tutorial there is command scan, which is send from CoolTerm program from PC.
Now we want to write program to Arduino which will do same operation, how it can be done?
|
Probabilistic localization approaches like Kalman or Monte Carlo benefit from an accurate prediction step. The more accurate the prediction step, the more accurate is the belief of the robots pose. In most approaches probabilistic motion models are applied, mainly because robot dynamics are more difficult to model. Still some approaches rely on dynamic models in order to increase the accuracy.
Therefore, I was wondering if it’s reasonable to utilize a robotic simulator like V-REP or Gazebo for the prediction step. The advantages I see in doing so are the following:
the robots kinematic is solved by default, simply through modeling it in the robotic simulator
the robots dynamics are taken into account
nonlinear behaviors like slippage or collision can be modelled up to a certain extend
the robots workspace is taken into account, by modeling its environment (if the robot drives against a wall previous models would predict it behind the wall, which won’t happen in a robotic simulator)
With the shown advantages I hope to achieve a more accurate prediction.
However there might be some problems using a robotic simulator. For a start it has to ensure real time behavior and there will be delay in the prediction due to the communication with the simulator.
I was looking for some papers which pick up on that idea but couldn’t find any. Are there any approaches similar to my idea? If not, are there any reasons why nobody is using a robotic simulator for the prediction? What are your opinions about my proposal?
|
I'm aware of the PrimeSense camera powering the Kinect. Are more advanced sensor types available now in the < $500 range? For example, has there been any sort of game-changer in structured light techniques? Do decent flash lidar cameras exist now?
|
I have created robot using a robot chassis kit from hobbyking. At first when testing the robot connected to USB power source and the wheels lifted above the ground everything seemed to be OK. Then, when I tried to power the robot with batteries I encountered a problem with starting the movement.
The robot hardly starts to move even when I power it with 100% of power - sometimes I have to push it a little bit in order to start driving.
As a newbie I don't know whether it is a power source (battery) or motors problem.
There are 4 motors with torque of 800gf.cm min in the chassis.
The gear ratio is 48:1 and to power the motors
I used two serially connected Li-ion batteries and dc-dc regulator which limits the voltage output to 5V.
The power is regulated with dual H-bridge motor driver.
According to specifications, the maximum free running current for a single motor is 250mA and I have read that the stall current is 3-8x running current.
Anyway, the problem is that the robot has problems with starting-up driving and I don't know how whether the motors are even powerful enough to move the robot or it is a power source problem or perhaps the obstacle could be solved with appropriate power regulation (ramp).
How can I solve this problem?
|
What is the maximum distance (of say , a car ) you could measure using an ultrasonic sensor that would be compatible with arduino? Is there any sensor(ultrasonic or not) that could measure the distance of a car , say upto 50 meters that can be used with arduino?
|
I'm looking for a part that will do a particular function. It has to be able to move along one axis and tell me the force that is being exerted on it.
Kind of how like a piston moves inside an engine (one axis of movement) except that something will be pushing at the top of the piston and I need to know how hard its pushing. Another difference is that the piston won't be constantly moving back in forth, but needs to be able to receive commands like. move x centimeters forward and then remain stationary at its new position.
I know to make this it would involved a sensor and something that can exert force but what is the name of the described machine?
Edit #1 - Response to Matthew Gordon
The piston would have to move between 0-6 centimeters. The form factor would be small, ideally smaller than the palm of your hand. (Smaller=better) The forces it would have to deal with are comparable to the forces exerted on a bicycle by its chain. I'm a math/cs person not engineering so I don't know technical terms for these kinds of things off the top of my head. It would have to be real time sensor reading, but the volume of data could be processed by a phone. Would have to working in conjunction with wireless communication, probably Bluetooth, but I'd have to look into the latency requirements to be sure.
|
When one wants to model a kinematic chain and in particular define the frames attached to each body, it is common to use the Denavit-Hartenberg parameters.
What are the advantages of this representation?
I can understand the interest of having a normalized representation but does it impact the algorithms performance?
The algorithm is not trivial to implement, what gain can we expect from this instead of, for instance, just fixing reference frames by hands (i.e. arbitrarily) like this is done in many robotics formats such as URDF.
|
I'm trying to calculate the inverse kinematic for an 6 dof manipulator.
Task:
A target point $p_{target} = (x,y,z)^T$ and the orientation $o_{target} = (a, b, c)^T$ are given and I want to get the angle configuration $q = (q_1, q_2, q_3, q_4, q_5, q_6)^T$ for my robot.
Method:
For that I try to implement the Jacobian method (with the transposed jacobian matrix) with this guide and followed the pseudocode at slide 26. But instead using the pseudoinverse of the Jacobian matrix I used the transposed one.
I'll try to compute the Jacobian matrix numerically and analytically, but didn't get a solution (endless loop) for any of them. Here's how I retrieve the Jacobian:
Numerically:
private void calculateMatrixNumerically()
{
var deltaTheta = 0.01;
var newAxisConfiguration = new AxisConfiguration(
this.currentAxisConfiguration.joints[0] + deltaTheta,
this.currentAxisConfiguration.joints[1] + deltaTheta,
this.currentAxisConfiguration.joints[2] + deltaTheta,
this.currentAxisConfiguration.joints[3] + deltaTheta,
this.currentAxisConfiguration.joints[4] + deltaTheta,
this.currentAxisConfiguration.joints[5] + deltaTheta
);
var ePos = this.kinematic.CalculateDirectKinematic(newAxisConfiguration);
for (var column = 0; column < this.Columns; column++)
{
this.M[0, column] = (this.currentPos.Point.X - ePos.Point.X) / deltaTheta;
this.M[1, column] = (this.currentPos.Point.Y - ePos.Point.Y) / deltaTheta;
this.M[2, column] = (this.currentPos.Point.Z - ePos.Point.Z) / deltaTheta;
this.M[3, column] = (this.currentPos.Orientations[0].A - ePos.Orientations[0].A) / deltaTheta;
this.M[4, column] = (this.currentPos.Orientations[0].B - ePos.Orientations[0].B) / deltaTheta;
this.M[5, column] = (this.currentPos.Orientations[0].C - ePos.Orientations[0].C) / deltaTheta;
}
}
Analytically:
private void calculateMatrixAnalytically()
{
var peMatrix = calculateJointPositions();
var zMatrix = calculateZ();
for (var column = 0; column < this.Columns; column++)
{
double[] p_p = new double[3];
for(var row = 0; row < zMatrix.Rows; row++)
{
p_p[row] = peMatrix.M[row, this.Columns-1] - peMatrix.M[row, column];
}
this.M[0, column] = zMatrix.M[1, column] * p_p[2] - zMatrix.M[2, column] * p_p[1];
this.M[1, column] = zMatrix.M[2, column] * p_p[0] - zMatrix.M[0, column] * p_p[2];
this.M[2, column] = zMatrix.M[0, column] * p_p[1] - zMatrix.M[1, column] * p_p[0];
this.M[3, column] = zMatrix.M[0, column];
this.M[4, column] = zMatrix.M[1, column];
this.M[5, column] = zMatrix.M[2, column];
}
}
/// <summary>
/// Calculate the positions of every joint.
/// </summary>
/// <returns>The Matrix with the joint coordinate for each joint.</returns>
private Matrix calculateJointPositions()
{
Matrix jointPositions = new Matrix(3,6);
Position pos;
for (var joint= 0; joint< this.currentAxisConfiguration.joints.Count(); joint++)
{
pos = this.kinematic.CalculateDirectKinematic(this.currentAxisConfiguration, joint);
jointPositions.M[0, joint] = pos.Point.X;
jointPositions.M[1, joint] = pos.Point.Y;
jointPositions.M[2, joint] = pos.Point.Z;
}
return jointPositions;
}
private Matrix calculateZ()
{
// (z0^T-z1^T-z2^T-...-z6^T)
var ksEnd = Kinematics.TranslateRobotToWorld();
var zMatrix = new Matrix(3, 6);
for (var column = 0; column < this.currentAxisConfiguration.joints.Count(); column++)
{
for (var row = 0; row < zMatrix.Rows; row++)
zMatrix.M[row, column] = Math.Round(ksEnd.M[row, 2], 7);
ksEnd = ksEnd.Multiply(
Kinematics.TranslateCoordinateSystem(
Robo.theta[column] + this.currentAxisConfiguration.joints[column],
Robo.d[column],
Robo.alpha[column],
Robo.a[column])
);
}
return zMatrix;
}
Here is the implementation of the Pseudocode:
do
{
jacob = JacobiMatrix.GetJacobi(currentPosition, currentAxisConfiguration);
jacobiTranspose = jacob.getTransposeMatrix();
// deltaE = (x2-x1, y2-y1, z2-z1, a2-a1, b2-b1, c2-c1)
deltaE = Position
.GetDistanceVector(currentPosition, targetPosition);
deltaThetas = jacobiTranspose.Multiply(deltaE).
Scale(beta);
for (var axis = 0; axis < deltaThetas.Rows; axis++ )
currentAxisConfiguration.joints[axis] += deltaThetas.M[axis, 0];
currentPosition = this.CalculateDirectKinematic(currentAxisConfiguration);
} while (Math.Abs(Position.Distance(currentPosition, targetPosition)) > epsilon);
where $beta = 0.5$ and $epsilon = 0.0001$
Problems:
The transformation matrice should be fine, because it behaves good for the forward kinematic.
In my opinion the Jacobian matrix must be somehow wrong. I'm not sure if it is correct how I put the orientation data in the numerical calculation. For the analytical computation I didn't have any clue what could be wrong.
I would be thankful for any advice. An explicit example for calculating the Jacobian would also be very helpful.
|
I am trying to test a sensor circuit I'm working on. Essentially, I am using RealTerm to send commands to the microcontroller and it is returning the value read by the sensor.
When logging to a file in RealTerm, I noticed the commands being sent were showing up as well as the data being returned. I was wondering if anyone knew a way to record only the incoming data using RealTerm, and not the outgoing commands. Any suggestions would be greatly appreciated. Unfortunately, there is no way around using RealTerm specifically because of a company policy.
|
I'm attempting to build a segway robot using a gyrosensor and accelerometer.
I'm having trouble getting the robot to remain standing, for some reason, and I can't identify the problem.
Here's what I know:
The gyroscope API for the lejos NXT platform is here:
http://www.lejos.org/nxt/nxj/api/
By using timestamps and angular velocity, the project attempts to infer the angle of the robot. The API suggests that in order to be accurate, it must be polled 100 times per second (or every 10ms on average).
The problem is that simply polling the gyrosensor takes 4ms.
Polling the accelerometer takes 10ms.
The dimensions of the robot:
Height: 28cm
wheel circumference : 13.25cm
Radius of a wheel, given the circumference: 2.1cm
The accelerometer is mounted on the top of the robot (at approximately 28cm from the ground, 26cm from axis of rotation)
In order to keep the correction amount linear (as opposed to trying to correct an arbitrary angle) , I translate the angle of the robot to a distance to travel along the ground to "right" the robot. This might be a bit naive, and I'm open to suggestion here. Basically it's just the horizontal distance calculated using a right-angle triangle with the angle of the robot at the top and hypotenuse of 28cm.
If that's not clear, it's essentially the horizontal distance from the top of the robot and the bottom of the robot.
Right now my main concern is the amount of drift the gyroscope seems to be experiencing. Given the fact that with the NXT java software package, it's nearly impossible to poll 100 times per second, the amount of error accumulated by the gyroscope is fairly large.
Finally, I've implemented a PID control system. The thing I'm not clear about with respect to this system is the integral and derivative of error must be calculated given a set of values. Say, the last 20 error measurements recorded.
If the amount of past errors recorded is a variable, and the PID constants are variable, and the speed of the wheels is a variable, it seems this problem begs for some kind of automated optimization. But how to do it? If I set the speed to 120 RPM (roughly the max of the NXT servos) and take the past 20 errors for calculating the integral and derivative of the error, will it be possible to optimize the PID constants successfully? Or must all 5 variables be tuned together?
Thanks ahead for any insight on the problem.
|
I have a differential drive robot that works fine (good PD parameters) driving at say 1 m/s. Now, if it speeds up (to 1.2 m/s) it starts wobbling again. What would be a good strategy for a controller that is able to cope with the whole speed range of 0 - 4 m/s?
edit 14th of April:
The robot is a line follow robot but I do not see how this would be related to my question since a robot following a trajectory would have the same problem.
I recently talked to other developers of differential drive robots and they are facing similar issues e.g. they told me that they need to adjust PID parameters once the battery is not fully charged hence the robot drives at a different speed.
I do not know if you guys are into youtube, but if your are really interested in my robot this link would be helpful: https://www.youtube.com/watch?v=vMedNPhXlEo
PID parameters are: P 0.31, D 0.59, I 0.00
PID controller programmed using C:
// note: the inner wheel turns backwards for narrow curves
// cte is -128..128 depending on the robots position
// relative to a trajectory / black line
/** Execute the PID controller and update motor speeds */
void PID()
{
int32_t steer;
int32_t cte;
cte = 128 - get_segment_center(0);
// Compute PID equation
steer = (int)(
-P * (float)cte
-D * (float)(cte - diff_cte) / (float)PERIOD_MS
-I * (float)int_cte
);
if (steer < -5)
{
// turn left
turn = -1;
uXbot_move(MAX_SPEED + steer, MAX_SPEED);
}
else if (steer > 5)
{
// turn right
turn = 1;
uXbot_move(MAX_SPEED, MAX_SPEED - steer);
}
else
{
// go straight
turn = 0;
uXbot_move(MAX_SPEED, MAX_SPEED);
}
diff_cte = cte;
int_cte += cte;
}
|
I have a four wheel DC rover with two optical wheel encoders. I'm executing rover turns by controlling the direction of wheel motion on either side of the rover. To turn left, all wheels on the left rotate backwards while all right wheels rotate forward. This allows to rover to remain relatively in the same position while turning. The directions are reversed to do a right turn.
How can I use these two sensors to execute as close to a 90 degree turn as possible without fusing additional sensor data?
|
How can I track a fixed point $P=(x_P, y_P)$ from a moving robot?
Coordinates of $P$ are relative to the state/pose of the robot (x axis looks forward the robot and y axis is positive on the right of the robot).
Suppose that the initial robot state/pose is at $S_{R}=(x_R, y_R, \theta_R)$.
The next frame (namely after $\Delta t$) with the applied control $(v, \omega)$ the robot is at state $S_{R'}=(x_{R'}, y_{R'}, \theta_{R'})$.
Where (I set the axes as OpenCV):
$x_{R'} = x_R + v cos(\theta_R) \Delta t $
$y_{R'} = y_R + v sin(\theta_R) \Delta t $
$\theta_{R'} = \theta_{R} + \omega\Delta t$
The question is: which are the coordinates $(x_P', y_P')$ of the same point $P$ relative to $S_{R'}$?
As visible in the picture, I know the transformation from the initial state to the next state of the robot and the coordinate of P in reference to the initial state
$$
t = \begin{pmatrix}
cos(\theta_{R'}) & -sin(\theta_{R'}) & x_{R'}\\
sin(\theta_{R'}) & cos(\theta_{R'}) & y_{R'}\\
0 & 0 & 1\\
\end{pmatrix}
$$
Please correct me if I made some mistakes!
Thank you, any help is appreciated.
|
I've been writing some quad copter software and I am not sure what the best way is to map the throttle and PID inputs to ESC power.
My throttle range is 0-1 and my PID outputs are 0-1. My ESC's have a range of 1060us to 1860us.
I have mapped the motor speeds like this:
_motorPower[0] = map((_rcConstrainedCommands[3] + _ratePIDControllerOutputs[1] + _ratePIDControllerOutputs[2] + _ratePIDControllerOutputs[0]), 0.0, 4.0, 1060, 1860);
_motorPower[1] = map((_rcConstrainedCommands[3] + _ratePIDControllerOutputs[1] - _ratePIDControllerOutputs[2] - _ratePIDControllerOutputs[0]), 0.0, 4.0, 1060, 1860);
_motorPower[2] = map((_rcConstrainedCommands[3] - _ratePIDControllerOutputs[1] - _ratePIDControllerOutputs[2] + _ratePIDControllerOutputs[0]), 0.0, 4.0, 1060, 1860);
_motorPower[3] = map((_rcConstrainedCommands[3] - _ratePIDControllerOutputs[1] + _ratePIDControllerOutputs[2] - _ratePIDControllerOutputs[0]), 0.0, 4.0, 1060, 1860);
This works but if my quad is perfectly level (i.e. the PID outputs are 0) and I apply full throttle (1.0) then map this to ESC power I will only get quarter power (1260us).
How should I be doing this so that if my throttle is on max then I get max power? If my throttle is half (0.5) then I should get half power plus the PID values etc.
Can anyone help me with this?
Thanks
Joe
|
Can someone please post the datasheet for the colour sensor mentioned above. All i can find is for TCS3200
|
I am preparing for my first quadcopter build and need to know how to tell what motors/ESC's/propellers will work with each other. I also would like to know how to tell what the motors would be capable of carrying/how much thrust they have. I would like to put a camera on this copter. I cannot find anywhere a straight answer to this question.
The ones I currently think are the ones I want are:
ESC:
https://www.hobbyking.com/hobbyking/store/__25365__Turnigy_Multistar_30_Amp_Multi_rotor_Brushless_ESC_2_4S.html
Motor: https://www.hobbyking.com/hobbyking/store/__28112__Turnigy_D3530_14_1100KV_Brushless_Outrunner_Motor_US_Warehouse_.html
Propeller: 11inch
This copter needs to be able to carry a camera (~go pro)
TLDR: How does one match ESC's/Motors/Propellers, and how to tell if they can get the job done?
(ESC - Electronic Speed Control)
|
So, i'm making my second ever hobby robot(I'm 15) and am planning on soldering my own connectors for the battery, sensors, Arduino, etc. It will be a small mobile robot. Anyways, I was wondering what a good gauge of stranded hookup wire would be good for that purpose. Thanks!!
|
For a class project, I'm working with a weight stack:
I'm trying to simultaneously measure:
the position of a moving weight stack
the value of the weight based on a calibrated/preloaded position in the stack, not via load sensor. (e.g. think a stack of plate weights where the sensor knows in advance that 1 plate = 10lbs, 2 plates = 20lbs, etc.)
The weight stack and the base camp chip/sensor/laser would be within two feet of the weight stack, so I don't need anything overly strong. My requirement is that it is small/unobtrusive and cost effective. I've looked into a few options, but I'm not an engineer so I'm not sure if I am on the right track.
How would you do this? Is there any research that I could check out?
|
So, I'm planning out my second hobby robot(I'm 15). I am planning on using a 7.4V LiPo battery and my idea is to solder on a 3-pin header to connect to the electronics. Anyways, should I solder to crimp terminals and then attach it to the pack? Or should I solder directly to the battery leads Keep in mind I have a decent background in hobby electronics and am just starting with robotics. My soldering skills are also decent!
Thanks!!
|
I'm trying to find a good beginners platform to use ROS with, and I came across the iRobot Create. To my surprise, they do not sell in Europe. Why is that?
|
I'm developing/tuning a software PID for a quadcopter. For now I'm only trying to stabilise the pitch angle using the front and back motors, and I'm only looking at Kp. The motors have a control resolution: input variations need to reach a threeshold to have any effect at all.
The process output does reach the setpoint, but not precisely enough for my requirements. There is no steady-state error (aka droop), the hunting range is centered on the setpoint, just too wide for my requirements. Also the instability is not an oscillation, but more of a random drift which needs to be large enough before the PID attempts to correct it.
With a lower Kp the output needs to diverge from the setpoint significantly before the error is big enough for the PID to attempt to correct it.
With a higher Kp the PID oscillates.
I could not find a reasonable compromise.
I'm thinking about applying the cuberoot function (or similar) to the error before feeding it to the PID: that way small errors should be significant enough for the PID to attempt to correct them, and large errors would be reduced and might not trigger oscillations. I suppose someone must have been through this before: is this a good solution? Are there any better alternatives?
This is not a steady-state error (aka droop) or oscillation issue as far as I can tell: please don't suggest using Ki or Kd
EDIT: I have clarified the problem description and suggested using cuberoot rather than logarithm which was a bad idea indeed.
Thanks,
Marc.
|
Most of the blogs/website say we need a minimum of four channels for a quadcopter (pitch, roll, throttle, yaw):
One channel for throttle
A second channel for turning right and left.
A third channel for pitching forward and backward.
A fourth one for rolling left and right.
But looking at the RC transmitter, I see that at a time you can change a maximum of two sets data (left and right joy stick). Even if you want to send Rudder and Throttle information at the same, can it not be sent in the same packet?
My understanding is two channels should be sufficient to control a quad copter. Please provide more clarity on this.
|
I am wondering what technology should I use to transmit data (enough for controlling the robot and receiving video) over dozens of kilometers and mountains ?
|
For a robot, say path planning in particular, what are the pros and cons of choosing classical control theory or optimal control (LQR for example) ?
|
I asked a similar kind of question some time ago (Neuromorphic Engineering and Robotics)
Since then, many things have come to the point of revelation. A road-map for neuromorphic computing was revealed recently; It proposes the analog way of computation, to solve advanced computer vision problems. IBM and Qualcomm are also working on the similar project though on the digital side. Memristor technology is slated to come very soon.
The question I am asking here is How is the Robotics community working to adopt the technology? This question opens the domain for other pressing questions which have been answered cryptically since the 1980s.
Are neuromorphic computers good for mission critical precise robots, like that on Mars? Can we use neuromorphic systems on Avionics systems? How is neuromorphic processing going to help us solve the problems on NLP, and knowledge processing? Aren't quantum computers very similar to neuromorphic computers in ideology? If neuromorphic robots gain traction, will digital hardware still be required?
It would be really nice if someone could explain all points, because answers in various but sparsely related research papers are very cryptic.
|
I want to develop a toy project that will allow me to move object around the house. Because I am interested in the programming of the robot and not actually build it, I would like to use some sort of programmable "starter kits" (like lego mindstorm) to get started. While I do not have everything figured out yet, here is a list of specs that I expect my ideal kit should have:
The ability to lift object (Object as big as 4'' or 10 centimeters)
The ability to distinguish objects by their colors. It should have some stort of color sensors.
Obviously it should be able to move on a smooth surface.
Obstacle detection. Should have sensors for obstacle detection
Extra: Maybe remotely controllable.
Can someone please suggests the cheapest kit I should use for this?
Thanks
|
I am learning about Kalman filters, and implementing the examples from the paper Kalman Filter Applications - Cornell University.
I have implemented example 2, which models a simple water tank, filling at a constant rate. We only measure the tank level, and the Kalman filter is supposed to infer the fill rate.
According to the model, the fill rate is a constant, so I assumed that over time, the Kalman filter would converge more and more accurately (and with less and less noise) on the correct fill rate. However, the amount of noise in the fill rate never seems to reduce after the first few iterations:
This graph shows how the fill rate part of the state vector changes over the course of 1000 iterations of the simulation.
Adjusting the Measurement Variance Matrix seems to have very little effect on the fill rate noise.
Also, the Kalman gain vector and State Variance matrix seem to be constant throughout the simulation. I assumed that the State Variance would reduce as the filter became more and more confident in its state estimate.
Questions:
- Is this graph what I should expect to see?
- Should the Kalman Gain vector and State Variance matrix change over time in this situation?
|
I am using the clickbutton library from Arduous and am having some problems implementing it. As it stand now the code just runs the servo clockwise and I'm not sure what I did wrong. Basically I want the servo if pressed for a short period of time to move according to an exponential function, and if pressed according to a long period of time to move at a regular pace.
#include <Servo.h>
#include "ClickButton.h"
Servo myservo; // create servo object to control a servo
// CONSTANTS
// PINS
const int crServo = 12; // sets pin 12 as servo
const int buttonPinCW = 2; // sets pin 2 as button; CW => clockwise => FOCUS FAR
const int buttonPinCC = 3; // sets pin 3 as button; CC => counterclockwise => FOCUS NEAR
const int ledPinB = 4; // sets pin 10 as LED
const int ledPinG = 5; // sets pin 10 as LED
const int ledPinR = 6; // sets pin 10 as LED
const int t = 1; // slow down
// SERVO PROPERTIES
const int crSpeedDefault = 100; // is the stay still position, motor should not turn
const int crSpeedCW = 107; // turns the motor full speed clockwise
const int crSpeedCC = 87; // turns the motor full speed counter-clockwise
// SET BUTTON STATES
ClickButton buttonCW(buttonPinCW, LOW);
ClickButton buttonCC(buttonPinCC, LOW);
void setup()
{
myservo.attach(crServo); // attaches the servo on pin 12 to the servo object
//pinMode (buttonPinCW, INPUT); // sets button as input
//pinMode (buttonPinCC, INPUT); // sets button as input
pinMode (ledPinB, OUTPUT); // sets led as output
pinMode (ledPinG, OUTPUT); // sets led as output
pinMode (ledPinR, OUTPUT); // sets led as output
myservo.write(crSpeedDefault); // default servo to crSpeedDefault
startup();
}
int startup() {
//blinker(2, ledPinB);
//blinker(1, ledPinG);
//blinker(1, ledPinR);
}
void blinker(int count, int pin) {
for (int x = 0; x < count; x++)
{
digitalWrite(pin, HIGH);
delay(1000);
digitalWrite(pin, LOW);
delay(1000);
}
}
void loop()
{
buttonCW.Update();
buttonCC.Update();
int buttonCWClicks = 0;
int buttonCCClicks = 0;
if (buttonCW.clicks != 0) buttonCWClicks = buttonCW.clicks;
if (buttonCC.clicks != 0) buttonCCClicks = buttonCC.clicks;
//buttonStateCW = digitalRead(buttonPinCW);
//buttonStateCC = digitalRead(buttonPinCC);
// clockwise rotation
if (buttonCW.depressed == true) {
if (buttonCWClicks == 1) {
digitalWrite(ledPinR, HIGH);
float speed = crSpeedCW;
Serial.print("CLOCKWISE-ROTATION \n");
for (int i = 0; i < t * 5; i++) {
speed += ((float)crSpeedDefault - speed)/ 10;
Serial.print(speed);
Serial.print("\n");
myservo.write((int)speed);
delay(100);
}
myservo.write(crSpeedCW);
}
else if (buttonCWClicks == -1) {
digitalWrite(ledPinR, HIGH);
myservo.write(crSpeedCW);
}
}
else if (buttonCC.depressed == true) {
if (buttonCCClicks == 1) {
digitalWrite(ledPinG, HIGH);
float speed = crSpeedCC;
Serial.print("COUNTER-CLOCKWISE-ROTATION \n");
for (int i = 0; i < t * 5; i++) {
speed += ((float)crSpeedDefault - speed) / 10;
Serial.print(speed);
Serial.print("\n");
myservo.write((int)speed);
delay(100);
}
myservo.write(crSpeedCC);
}
else if (buttonCCClicks == -1) {
digitalWrite(ledPinR, HIGH);
myservo.write(crSpeedCW);
}
}
else {
myservo.write(crSpeedDefault);
digitalWrite(ledPinR, LOW);
digitalWrite(ledPinG, LOW); // turn the LED off by making the voltage LOW
}
}
|
So I am building a differential drive robot and I want it to autonomously drive in a straight line on an uneven surface. I know I need a position and velocity PID. As of now, I am deciding on which sensors to buy. Should I use optical encoders, accelerometers, or something else?
I wanted to go with accelerometers due to the error encoders would face due to slippage, but I am not sure.
Some enlightenment would help!
|
I'm facing a problem while building my quadruped robot which is figuring out the efficient power supply needed for the 12 servos. I'm using 12 MG995 tower pro servos powered by 2 lithium batteries 2x3.7v (about 8 volts) with 2200 mA . I really don't know if that enough for the servos or something else is needed to be added(i hardly fitted the 2 batteries into the robot's body)
any suggestions please?
|
Recently I began to build a car-like robot and I stumbled upon dead reckoning. I use one motor for steering and one for traction. I want to be able to get the position of the robot. From what I have read 2 encoders should be used. But I am curious if you can use only one encoder on the motor shaft to get distance and a gyro + accelerometer to get the orientation of the robot.
|
Every time I see a PID control for a motor, it involves an Encoder, so the algorithm knows the real position of the motor or wheel.
But with the robot I have, I cannot use an encoder. I only have ONE optocoupler per wheel which can count how many degrees the wheel has moved. I can use it to increment a counter, but the counter ALWAYS increment (if the wheel moves forward or if the wheel moves backward).
The first moment I saw it as an inconvenient was when I studied the Arduino PID Autotune Library. In the first figure, I would not see decrements on the INPUT.
My objective is to move a little two-wheels robot small segments driven by simple trajectories separated over time by a complete stop (straight 10 cm, stop, move right 90 degrees, stop, straight until detect obstacle...)
Could you suggest me some kind of ideas?
The first idea I have is to transform the PID position control to a speed control (which is more convenient for the feedback loop I have) and keep a counter of the traveled distance to inform the speed control when to stop.
|
First a bit of background, I am planning to make a highly maneuverable airship controlled by four thrust vectored propellers. I don't want to rely on a rudder and forward momentum for turns but instead be able to maneuver with direct prop thrust. I want to be able to point each prop anywhere within a half sphere/dome. So two axis, 360 degrees traversal for the forward/back/up/down and 180 degrees for the left/right. The nearest thing I can think of is a ball turret similar to this ball turret but instead of a gun, have a motor and propeller. The turret can rotate infinitely through 360 degrees, but the gun rotates through 90 degrees.
My first thought was for a servo for both axis, but they are limited in range and I would like the 360 axis to be able to rotate continuously. This would allow for the turret to rotate to the desired angle using the shortest path.
My question is, What do I need to be able to rotate the turret and still know what angles the turret is currently pointing?
|
I have not yet built this so this is basically a theoretical question. I am still wrestling with some C code to manage i2c communication etc.
When I originally said "I have not built this", I meant that the robot is in what could be called a "design phase". For the sake of my question lets assume for a moment that the whole robot consists of just one IMU sensor. It moves magically (no motors that create a lot of noise in the sensor measurements). With theoretical I mean I am interested in the mathematics and algorithms involved in solving this problem. What I call IMU sensor provides raw accelerometer, gyro, and magnetometer measurements.
Lets say our tiny robot travels on a snooker table (3569 mm x 1778 mm). I believe this is sufficiently small to call it 2d. **Now, sensor fusion should be much easier (faster, consume less resources) than in 3d, right? **
I would like to measure the velocity and if possible the position.
With velocity I mean at a given point of time I need to know the current velocity of the robot moving over the snooker table. Velocity is in the range of 0 - 5 m/s.
With position I mean at a given point of time I need to know the current position of the robot on the snooker table (x, y, heading).
I hope this will be possible since the robot should be able to identify some landmarks and use this information to reduce position errors.
When I originally said "I hope this will be possible" I meant to express that I already am aware of the fact that real sensor data is noisy and errors accumulate quickly. Using landmarks I will / or will not be able to manage to reduce the error in the position estimates. But this is NOT part of my question.
I am about to improve my linear algebra knowledge. So I am confident to manage some matrix multiplications, inversions and such.
My question is for some ideas, references on measuring velocity and position in 2d using an IMU sensor like this one.
P.S. A little side question: I just figured that this question is probably too theoretical for robotics.SE. If you know any forum that is more focused on mathematical / algorithmic side of robotics please let me know.
|
When I look at my 160A motor controller, it has a port that is called "CTL."
What does CTL stand for? Is that a sort of protocol like RS232?
|
I'm trying to do a quadcopter with some friends and we have a problem. It goes forward instead of hovering in place. We made a video to explain it, you can see it here.
As you can see, the quadcopter flight and go forward when I don't touch the controller. I need to correct it to go backward and it goes forward again.
We use the kk2.1.5.
The HobbyKing KK2.1.5 Multi-Rotor controller is a flight control board for multi-rotor aircraft (Tricopters, Quadcopters, Hexcopters etc). Its purpose is to stabilize the aircraft during flight. To do this it takes the signal from the 6050MPU gyro/acc (roll, pitch and yaw) then passes the signal to the Atmega644PA IC. The Atmega644PA IC unit then processes these signals according the users selected firmware and passes control signals to the installed Electronic Speed Controllers (ESCs). These signals instruct the ESCs to make fine adjustments to the motors rotational speed which in turn stabilizes your multi-rotor craft.
We made some test. As you can see in the video, we placed the battery backward to be sure there is no weight against. When we check values in the debug mode, all values are at 0 when nothing is pressed.
|
Here is the disassembled stepper motor that I'm working with:
Contains the photo of the motor, and the label that's on the bottom of the motor.
I need to identify this stepper motor that was retrieved from scrap for a project. Budget constraints force us to use the scrap motor. I tried to drive this using a L298 H Bridge, but I couldn't find the right bit sequences to get this running smoothly. I also tried to search for a specifications sheet in the internet with the label, unsuccessfully.
I'm using either an RPi or Arduino board to run this.
I just need a pin diagram and the specifications of the motor, if you guys have seen this type before.
|
I am planning to create a motor turret described in this question. But to simplify the problem, I'm thinking of a wind turbine with a generator in the main head that can rotate freely through 360 degrees to face the wind. How would I prevent the power and sensor wires coming from the head and down the shaft from twisting?
|
How would one go about making a number of drones fly in a preset pattern or formation. for example have them rotating around a point.
something like this.
https://www.youtube.com/watch?v=ShGl5rQK3ew
|
How long can a vex pneumatic piston be?
|
My current class assignment is to program a robot through a course that includes two moving obstacles – other robots moving at constant speed around a region the one robot must get to. Since the other robots are moving at a constant pace alongside a predictable path, my robot can just stop at the border of the region, wait until the others pass by and then proceed. The robot can use a 2D laser range scanner to sense its surroundings.
Given these restrictions, what is the simplest object tracking algorithm I could use? I am thinking of something along these lines:
Collect two laser readings (2D point clouds) A and B with a suitable time gap between them;
Apply DBSCAN to A and B, producing the cluster lists A' and B';
Generate a list P of pair-wise matches of the clusters in A' and B', maybe using the Hungarian algorithm;
Discard from P any pairings whose difference falls within a threshold;
Calculate direction and magnitude of movements from the distance between the centers of mass of each cluster pair.
The reason for choosing DBSCAN and the Hungarian algorithm is that I already have both implemented and in use elsewhere; and the difference between clusters could be measured as the distance between their centers of mass.
Do you think this solution would work for my problem? Do you have any suggestions on better and/or simpler ways to solve it?
|
Edited: I have a differential drive robot that needs to drive down a hall and stay in the center. I have 4 ultra sonic sensors, 2 on each side. Currently, I thought of implementing pure pursuit and having a lookahead distance etc. but I am not sure if this is the correct approach. I have all the lower level PIDs for motor control working, I just need some help in choosing a path planning algorithm? Will pure pursuit work to my needs? OR do people have any suggestions.
|
I'm building a circuit to control the flow of flour. The basic idea is to open that actuator (Possibly valve?) to let a specific amount go through and then close it. The tube should have a diameter of 1cm max.
I wonder what is the right actuator to use? Maybe a valve is the right one? Any other solution? Also, it would be great if you pointed out some suitable actuators that I can buy online.
|
I would like to build 2D EKF-SLAM in openGL. I've implemented the entire virtual environment in which there is a robot that moves in 2D and there are some landmarks(feature-based map). I have the motion and observation models. Also, I've implemented the sensors with Gaussian noise. Now, I would like to use MRPT to build SLAM. At this point, I don't want to use data association that is the robot moves and detects its pose and landmarks and discard the previous data which means I only concern with the current state vector. My question is Is it possible to build SLAM without data association? Please suggest me some articles so that I can enrich my background about only this issue.
|
I'm building a robot with differential drive. I've reached the point when I can drive it around on remote control and I'm trying to get the localization working. Now I would like to exactly measure parameters of the robot.
Model of the robot I'm using has two wheels spaced $b$ meters, each wheel has a distance per encoder tick $s_L$ and $s_R$ and variance standard deviation of the driven distance $\sigma_L$ and $\sigma_R$.
When moving, distances are random variables from the following distributions: $d_L \sim t_{L}s_{L}N(1, \sigma_L^2)$ and $d_R \sim t_{R}s_{R}N(1, \sigma_R^2)$. Later this model might expand a little bit.
What is a good way to measure the parameters?
I found a way to measure $b$, $d_L$ and $d_R$ (added that as an answer), but I have no idea how to measure the standard deviations.
The model will be used as a prediction input in MCL, so I don't need covariance matrix for localization.
|
I'm building the biggest robot I've ever done. The hardware I have so far is as follows:
HCR-Platform from DFRobot as a base
x2 12V 146Rpm DC motor with two phase hall encoder
x7 Sharp 2Y0A21 IR sensors
x6 URM37 ultrasonic sensor
x4 IR ground sensor
Microsoft Kinect
Right now I'm only using a RoMeo board (arduino 328 compatible) to drive the motors and process the PIDs for the wheels + steering and also access to all the sensors (except for the kinect). I have a BeagleBone Black running linux that is intended to be the main brain connected to the RoMeo using the RS232 port and do the processing of the Kinect + wifi access.
I started thinking about connecting the sensors to the Beagle board directly so I don't need to waste time sending commands to the arduino board to read the sensors and that yielded the first issue, beagle board works on 3.3V instead of 5V used on the sensors.
After this I thought to create a board with voltage divisors to connect the sensors there and then using a ribbon cable to connect this new board to the beaglebone. I couldn't find any 2x23 IDC male connector to create the "interface cable" between the two boards so the beaglebone option is out as I don't want to have tons of jumper cables all over the place.
This morning I thought about all this again and I researched about GPIO boards over USB and found three from Numato, only one works on TTL 5V and has 8 pins so I would need a few of them to use all the sensors so unless I design my own board this option is out too.
At this point I'm quite confused in terms of what's the best hardware I could use to drive this beast. Right now I think I should use a Intel NUC with linux for the Kinect, wifi and usb link to a custom made sensor board. This custom made board will work on TTL 5V, provide a power bus for the sensors and will interface all the "low level" sensors using USB as link. I also thought about an FPGA for the custom board but I'm not sure if this would help me or if it's worth the effort of learning how to use it.
What are your thoughts on this? Any idea on how this issues are solved on "complex" robots?
|
According not only to this link, but in my humble opinion to common sense as well, cable-based haptic devices have got lower inertia and are less complicated mechanically. I also believe that controlling them is not that big deal - the inverse kinematics should be quite straightforward. Moreover, the play should be easy to compensate - if there occurs any at all, since the cables are tensioned. Cables should also be easy - ? just a guess from me - to be equipped with strain gauges or to become strain gauges themselves, allowing to enhance control capabilities of a device.
Where am I wrong? Why is that the links-based systems (e.g. PhaNTom or Falcon, although that latter has got cable transmission), especially with impedance control, are the only I seem to be able to buy? Is it because of cable elongation (creep)? Or too constrained workspace (esp. angles)?
|
I want do make following installation (blowing bottle tops as music instrument):
V - valve
B - bottle
___________________
|mini air compressor|
-------------------
|
______|_______
| | | | | |
V V V V V V
| | | | | |
B B B B B B
And I want to use and rc servo as electro valve (throttle) to control air flow for each bottle. Is there any other way to do that?
|
The Rock framework already includes a lot of software libraries. However, I would like to add an existing external library so that I can use it for my component development. What is the preferred way of doing that?
|
What's the name of the "big meccano" used in the photo below to construct all the cabinets and racks?
It appears to be an aluminium cut-to-length system of 4-way rails. I've seen it used many times and assume it has a well-known brand-name to those that know.
Photo taken from theverge.com and was a feature about how Audi are building a new car.
|
I have a 7 dof manipulator (Kuka LBR4+) and would like to calculate the joint torques needed to keep the arm in a static equilibrium. In most books the transposed jacobian is used to map the forces applying on the end effector to the joint torques.
$\tau = J^T\cdot F$
That however doesn't take the mass of the links into account. Is there a way to calculate the needed torques for a given configuration so that, assuming an ideal case, by setting these torques the arm will be in a static equilibrium?
cheers
EDIT:
For everybody interested, i found a solution to this problem in Introduction to Robotics - Third Edition by John J. Craig on Page 175-176. It is done with the aid of the iterative Newton-Euler dynamics algorithm. The actual trick is, to set all velocities and accelerations to zero except for the base acceleration. The base acceleration will be $^0 \dot v_0 = G$, where G has the magnitude of the gravity vector but points in opposite direction. This is equivalent to saying that the base of the robot is accelerating upwards with 1 g and this upward acceleration causes exactly the same effect on the link as gravity would.
|
A project has been given to me at work, with no schematics or idea of where it was going. I need to fill 5 30ml bottles at a time with a food-grade liquid.
Based on the parts I have, I think the design was going to use a air agitated pressure pot tank which is used for spraying paint, which would work if we weren't using food grade liquid, so right off the bat I cant use that.
The main parts that I can use are an Allen Bradley micrologix plc, 2 pneumatic cylinders, a couple solenoids, start and stop buttons.
My question is: to fill a 30ml bottle with this liquid, would a positive displacement pump with a vfd be the best way of slowing the pdp down enough to fill the 5 30ml bottle at a time?
I do have a little experience with this particular plc so the ladder logic is not the issue, its the figuring out the specs for the pump and the motor. Any input would be very helpful also any links would be great. At this point im trying to determine if this is a huge waste of time and money or should I just go buy a filling machine for $3-5000.
|
So I need to output a varying voltage off an Arduino Mega in a range of 17 to 32 millivolts, which I've attempted to do by sending a PWM signal off the board into a low-pass filter which steps down the voltage.
This works, but the problem is that Arduino's analogWrite function accepts a value of 0 - 255 to represent the duty cycle of the PWM which isn't precise enough. A value of 1 yields around 20 millivolts and a value of 2 yields around 40 millivolts. Is there some way to have a duty cycle that is more precise than the 0 - 255 range like 0 - 1023 (I think even this isn't really precise enough)? Or is there a better way to get precise voltage output?
The mega is running on and outputting a max voltage of 5 volts, and the low pass filter contains an 11 kiloohm resistor and a 1 microfarad capacitor.
|
What are some good cheap, silent, motors for mannequin robots and what kind of controller should I use?
I'm creating mannequin robots that require 24 motors: 2 neck, 4 shoulder, 2 elbow, 4 wrist, 2 waist, 4 hip, 2 knee and 4 ankle motors. The mannequins will be bolted via a horizontal post from the lower back to a wall. This means they can run, dance, and pose. I may also do something with the horizontal beam so the robots can do side flips, and moves like the twist, jumps and crouches. They will not stand up, but be bolted as I've described to a wall. They could even spin around completely to face the wall.
I've tried using 6v hobby servos for the joins. These are too weak for lifting fibreglass mannequin body parts. They are too loud. They also make noise when poses are held, and they drop and smash when power is shut off. I have been using a handheld remote control for testing with only 5 channels. The robot must be programmable though. Lets say the objective is to make a mannequin dance to 'Thriller' like Michael Jackson.
I am open to using kinnect technology as the controller (so that a dancer can simply dance in front of my robot, who can copy and remember) but I'm also open to controllers that allow me to force the mannequin into a pose at specific time codes in the song. If necessary, I am also willing to program the poses using some kind of lighting desk type controller (such as tech crews use in rock concerts to sync everything to go with the music).
I have noticed that a power drill or winch is very loud whereas my fan is very quiet. I live in an apartment in which neighbours can hear footsteps from other apartments. I would not dare turn on a drill at 4am because it would wake up everyone in the whole building, but I would have no guilt in turning on a fan. I need my robot to be as quiet as a fan. The voltage does not really matter for this project. I'm happy to use up to 240v from the wall socket.
Please let me know which motors and controllers are best for my mannequin robots, taking cost into account. Thanks so much for any help :)
|
I'm new to embedded devices and am trying to understand how to use i2cget (or the entire I2C protocol really).
I'm using an accelerometer MMA8452, and the datasheet says the Slave Address is 0x1D (if my SAO=1, which I believe is referring to the I2C bus being on channel 1 on my raspberrypi v2).
From the command line, I enter
sudo i2cget -y 1 0X1d
It returns
0X00
I think that means I'm attached to the correct device.
So now, I'm trying to figure out how do I get actual data back from the accelerometer?
The i2c spec says
i2cget [-y] i2cbus chip-address [data-address [mode]]
So I have tried
sudo i2cget -y 1 0x1D 0x01
where 0x01 is the OUT_X_MSB. I'm not sure entirely what I'm expecting to get back, but I figured if I saw some data other than 0x00, I might be able to figure that out.
Am I using ic2get wrong? Is there a better way to learn and get data from i2c?
The datasheet for my accelerometer chip is at http://dlnmh9ip6v2uc.cloudfront.net/datasheets/Sensors/Accelerometers/MMA8452Q.pdf
|
I'm working on my first robot project. I previously used a 12V 6Ah sealed lead acid battery, but recently I aquired some 15 ASUS Li-Ion battery packs, each of them 14.8V and either 2200 mAh or 4400 mAh. The laptops have been discarded, and some of the battery packs seem to be dead.
The battery packs have an 8 pin connector. Inside, I assume there's a bunch of 18650-cells and some electronics.
My robot can handle 14.8 V directly.
How can I use these batteries? How can I charge them without the laptops? I'm a little put off by the idea of taking the 18650-cells out of the packs and rebuilding my own battery pack and charging system, but if that's what's needed I have to do it.
The packs are marked ASUS A41-A3 for the 2200 mAh ones, and ASUS A42-A3 for the 4400 mAh ones.
|
I was wondering if you could reccomend possible solutions for locating a robot within a multilevel house. What seems obvious to me is that need an altitude sensor to derive the story the robot, and a compass sensor to derive the heading. However I was wondering what I could use to locate the robots xy position in the house. If this requirement is unclear, imagine that I have to map a dot representing my robot position to an image of the current floor from the top.
My original idea was to use GPS, however as I need submeter accuracy that would be incredibly expensive. I also considered Monti-Carlo localization, however that requires no obstruction between sonar sensors and walls. It is also a significant task programically. I had an idea to place 3 wireless beacons of some sort on the vertexes of an equilateral triangle surrounding the house, then triangulate my position using distance from each beacon. However, I have no idea how I would go about this hardware-wise. Do any of these ideas seem viable, and if so do you have suggestions on how to implement them? Otherwise, can you reccomend an easier or cheaper alternative? My platform is essentially an arduino hooked up to sensors and motor drivers connected to java on a laptop over serial. Thanks.
|
We have builded a quadcopter that use the flight manager kk2.1.5 with the latest firmware. When we increase the throttle, it flight. When we keep the hand on the stick we are able to maintain it but when we don't touch to the throttle, it goes up and down.
You can see an example on this video.
We have tried different values for PID but we don't know what is the best for us. We have a large quadcopter with medium propellers (may be too small).
Does the weight of the quadpcoter or the width of the propellers can be a factor? What can be the problem?
|
I am building a Quadcopter using the Sparkfun Razor IMU which outputs the Roll, Pitch and Yaw axes values at 50 Hz, which limits the operations of the controller(implemented on Arduino IMU), to 50 Hz mx itslef. Please tell me if flashing the ESCs(EMax 40A) with the Simon K firmware can do me any good.
I'll be grateful. :)
|
I am confused about how adding a D (which adds a zero to the complete system) decreases the speed of the system. But when we normally add a zero to the system, it causes the system to overshoot.
The same goes for the I part of the PID. Normally when we add a pole to the system, it has less overshoot, but at the same time the integrator increases the overshoot!
How can I make sense out of this inverse relation?
|
I am working on a project where a dslr camera will be rotated on a tripod on 2 axes. I'm definitely using nema 17 motors as those are what I have. The motors will rotate 30 degrees every 5 seconds in normal usage so speed is not a requirement. The weight of the camera is 1170g and I'm using 3d printed parts for the remainder of the mount. I tried running nema 17 stepper motors off a Adafruit Motor Shield V2 but the whole thing overheats (battery, driver, and motor). By the way, the motor will be controlled by an Arduino. I need to find another motor controller to use. I looked on ebay and things like this came up for 20 dollars which seems too good to be true.
My question is what motor driver should I use for this project as I have little experience with them outside of Arduino shields
|
How do i use the Nicolas Ziegel approach when the root locus plot of my system never becomes marginally stable , for any gain (unless it is negative).. ??
How do i estimate my ultimate gain value????
|
I am trying to understand the effects of P, I and D constants in a PID controller on a system.
As far I've understood, P and I make the system 'faster', and D makes it 'slower'(which I read in books), but I don't actually understand what makes it go 'fast' or 'slow'.
How an integrator causes overshoot and all things like that. It makes sense that the P part causes overshoot, since it adds a gain. But what is the integrator doing? I want some kind of mathematical understanding on how all these parameters affect the system.
I know how they work individually, but I'm having a hard time understanding, how it affects the system as a whole. For example, how does a Zero added to the system lead to decrease in overshoot, but when normally adding a zero to a system would create more overshoot.
|
Many websites say that analog servo motors work on 50Hz and digital servo motors work on 300Hz. My question is, does this difference apply only to the inner control loop of the servo or does the user of the digital servo actually provide 300Hz PWM signal? To rephrase, are all (most) servos including digital ones controlled with 50Hz PWM, or are digital ones specifically controlled with 300Hz PWM? Thanks
|
Why doesn't a PID only consisting of ID exist?
|
I have a kk2.1.5 and I fly with the self-level on. In the kk2, there are two menus. One to set PI-settings and another with selflevel-settings. Both enable to set P-Gain and I-Gain.
Is it important to have good PI-settings when the self-level is on, or is setting good values in selflevel-settings sufficient?
|
I have built a quad copter completely from scratch (electronics, mechanics and software).
I am now at the point where all my sensor data looks correct and when I tilt the quad copter the correct motors increase and decrease.
I have been trying to tune the PIDs for a couple of days now, in rate mode it stays level and rotates at roughly the correct degrees per second when I give it a command.
In stability mode a lot of the time it just spins around the axis and when I did get it stable it kept rotating from upright to upside down and then maintaining an upside down flat position. I have come to the conclusion that I am either doing something completely wrong or I have some + - signs mixed around somewhere.
Would anyone who is knowledgeable about quad copter control code be able to take a look at what I have done and how it works as I'm really struggling to work out what needs to change and what I should try next.
My flight control code is posted below, the other relevant classes are hardware.h and main.cpp
#include "mbed.h"
#include "rtos.h"
#include "hardware.h"
//Declarations
float Constrain(const float in, const float min, const float max);
float map(float x, float in_min, float in_max, float out_min, float out_max);
void GetAttitude();
void Task500Hz(void const *n);
void Task10Hz();
//Variables
float _gyroRate[3] ={}; // Yaw, Pitch, Roll
float _ypr[3] = {0,0,0}; // Yaw, pitch, roll
float _yawTarget = 0;
int _notFlying = 0;
float _altitude = 0;
int _10HzIterator = 0;
float _ratePIDControllerOutputs[3] = {0,0,0}; //Yaw, pitch, roll
float _stabPIDControllerOutputs[3] = {0,0,0}; //Yaw, pitch, roll
float _motorPower [4] = {0,0,0,0};
//Timers
RtosTimer *_updateTimer;
// A thread to monitor the serial ports
void FlightControllerThread(void const *args)
{
//Update Timer
_updateTimer = new RtosTimer(Task500Hz, osTimerPeriodic, (void *)0);
int updateTime = (1.0 / UPDATE_FREQUENCY) * 1000;
_updateTimer->start(updateTime);
// Wait here forever
Thread::wait(osWaitForever);
}
//Constrains value to between min and max
float Constrain(const float in, const float min, const float max)
{
float out = in;
out = out > max ? max : out;
out = out < min ? min : out;
return out;
}
//Maps input to output
float map(float x, float in_min, float in_max, float out_min, float out_max)
{
return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min;
}
//Zeros values
void GetAttitude()
{
//Take off zero values to account for any angle inbetween the PCB level and ground
_ypr[1] = _ypr[1] - _zeroValues[1];
_ypr[2] = _ypr[2] - _zeroValues[2];
//Swap pitch and roll because IMU is mounted at a right angle to the board
//Gyro data does need swapping - done within freeIMU class
float pitch = _ypr[2];
float roll = _ypr[1];
_ypr[1] = pitch;
_ypr[2] = roll;
}
void Task500Hz(void const *n)
{
_10HzIterator++;
if(_10HzIterator % 50 == 0)
{
Task10Hz();
}
//Get IMU data
_freeIMU.getYawPitchRoll(_ypr);
_freeIMU.getRate(_gyroRate);
GetAttitude();
//Rate mode
if(_rate == true && _stab == false)
{
//Update rate PID process value with gyro rate
_yawRatePIDController->setProcessValue(_gyroRate[0]);
_pitchRatePIDController->setProcessValue(_gyroRate[1]);
_rollRatePIDController->setProcessValue(_gyroRate[2]);
//Update rate PID set point with desired rate from RC
_yawRatePIDController->setSetPoint(_rcMappedCommands[0]);
_pitchRatePIDController->setSetPoint(_rcMappedCommands[1]);
_rollRatePIDController->setSetPoint(_rcMappedCommands[2]);
//Compute rate PID outputs
_ratePIDControllerOutputs[0] = _yawRatePIDController->compute();
_ratePIDControllerOutputs[1] = _pitchRatePIDController->compute();
_ratePIDControllerOutputs[2] = _rollRatePIDController->compute();
}
//Stability mode
else
{
//Update stab PID process value with ypr
_yawStabPIDController->setProcessValue(_ypr[0]);
_pitchStabPIDController->setProcessValue(_ypr[1]);
_rollStabPIDController->setProcessValue(_ypr[2]);
//Update stab PID set point with desired angle from RC
_yawStabPIDController->setSetPoint(_yawTarget);
_pitchStabPIDController->setSetPoint(_rcMappedCommands[1]);
_rollStabPIDController->setSetPoint(_rcMappedCommands[2]);
//Compute stab PID outputs
_stabPIDControllerOutputs[0] = _yawStabPIDController->compute();
_stabPIDControllerOutputs[1] = _pitchStabPIDController->compute();
_stabPIDControllerOutputs[2] = _rollStabPIDController->compute();
//If pilot commanding yaw
if(abs(_rcMappedCommands[0]) > 0)
{
_stabPIDControllerOutputs[0] = _rcMappedCommands[0]; //Feed to rate PID (overwriting stab PID output)
_yawTarget = _ypr[0];
}
//Update rate PID process value with gyro rate
_yawRatePIDController->setProcessValue(_gyroRate[0]);
_pitchRatePIDController->setProcessValue(_gyroRate[1]);
_rollRatePIDController->setProcessValue(_gyroRate[2]);
//Update rate PID set point with desired rate from stab PID
_yawRatePIDController->setSetPoint(_stabPIDControllerOutputs[0]);
_pitchRatePIDController->setSetPoint(_stabPIDControllerOutputs[1]);
_rollRatePIDController->setSetPoint(_stabPIDControllerOutputs[2]);
//Compute rate PID outputs
_ratePIDControllerOutputs[0] = _yawRatePIDController->compute();
_ratePIDControllerOutputs[1] = _pitchRatePIDController->compute();
_ratePIDControllerOutputs[2] = _rollRatePIDController->compute();
}
//Testing
_ratePIDControllerOutputs[0] = 0; // yaw
//_ratePIDControllerOutputs[1] = 0; // pitch
_ratePIDControllerOutputs[2] = 0; // roll
//Calculate motor power if flying
if(_rcMappedCommands[3] > 0.1 && _armed == true)
{
//Constrain motor power to 1, this means at max throttle there is no overhead for stabilising
_motorPower[0] = Constrain((_rcMappedCommands[3] + _ratePIDControllerOutputs[1] - _ratePIDControllerOutputs[2] + _ratePIDControllerOutputs[0]), 0.0, 1.0);
_motorPower[1] = Constrain((_rcMappedCommands[3] + _ratePIDControllerOutputs[1] + _ratePIDControllerOutputs[2] - _ratePIDControllerOutputs[0]), 0.0, 1.0);
_motorPower[2] = Constrain((_rcMappedCommands[3] - _ratePIDControllerOutputs[1] + _ratePIDControllerOutputs[2] + _ratePIDControllerOutputs[0]), 0.0, 1.0);
_motorPower[3] = Constrain((_rcMappedCommands[3] - _ratePIDControllerOutputs[1] - _ratePIDControllerOutputs[2] - _ratePIDControllerOutputs[0]), 0.0, 1.0);
//Map 0-1 value to actual pwm pulsewidth 1060 - 1860
_motorPower[0] = map(_motorPower[0], 0.0, 1.0, MOTORS_MIN, 1500); //Reduced to 1500 to limit power for testing
_motorPower[1] = map(_motorPower[1], 0.0, 1.0, MOTORS_MIN, 1500);
_motorPower[2] = map(_motorPower[2], 0.0, 1.0, MOTORS_MIN, 1500);
_motorPower[3] = map(_motorPower[3], 0.0, 1.0, MOTORS_MIN, 1500);
}
//Not flying
else if(_armed == true)
{
_yawTarget = _ypr[0];
//Set motors to armed state
_motorPower[0] = MOTORS_ARMED;
_motorPower[1] = MOTORS_ARMED;
_motorPower[2] = MOTORS_ARMED;
_motorPower[3] = MOTORS_ARMED;
_notFlying ++;
if(_notFlying > 500) //Not flying for 1 second
{
//Reset iteratior
_notFlying = 0;
//Reset I
_yawRatePIDController->reset();
_pitchRatePIDController->reset();
_rollRatePIDController->reset();
_yawStabPIDController->reset();
_pitchStabPIDController->reset();
_rollStabPIDController->reset();
}
}
else
{
//Disable Motors
_motorPower[0] = MOTORS_OFF;
_motorPower[1] = MOTORS_OFF;
_motorPower[2] = MOTORS_OFF;
_motorPower[3] = MOTORS_OFF;
}
//Set motor power
_motor1.pulsewidth_us(_motorPower[0]);
_motor2.pulsewidth_us(_motorPower[1]);
_motor3.pulsewidth_us(_motorPower[2]);
_motor4.pulsewidth_us(_motorPower[3]);
}
//Print data
void Task10Hz()
{
int batt = 0;
_wirelessSerial.printf("<%1.6f:%1.6f:%1.6f:%1.6f:%1.6f:%1.6f:%1.6f:%d:%1.6f:%1.6f:%1.6f:%1.6f:%1.6f:%1.6f:%d:%d:%d:%d:%1.6f:%1.6f:%1.6f:%1.2f:%1.2f:%1.2f:%1.2f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f:%1.8f>",
_motorPower[0], _motorPower[1], _motorPower[2], _motorPower[3], _ypr[0], _ypr[1], _ypr[2], batt, _ratePIDControllerOutputs[0], _ratePIDControllerOutputs[1], _ratePIDControllerOutputs[2], _stabPIDControllerOutputs[0], _stabPIDControllerOutputs[1], _stabPIDControllerOutputs[2], _armed, _initialised, _rate, _stab, _gyroRate[0], _gyroRate[1], _gyroRate[2], _rcMappedCommands[0], _rcMappedCommands[1], _rcMappedCommands[2], _rcMappedCommands[3], _yawRatePIDControllerP, _yawRatePIDControllerI, _yawRatePIDControllerD, _pitchRatePIDControllerP, _pitchRatePIDControllerI, _pitchRatePIDControllerD, _rollRatePIDControllerP, _rollRatePIDControllerI, _rollRatePIDControllerD, _yawStabPIDControllerP, _yawStabPIDControllerI, _yawStabPIDControllerD, _pitchStabPIDControllerP, _pitchStabPIDControllerI, _pitchStabPIDControllerD, _rollStabPIDControllerP, _rollStabPIDControllerI, _rollStabPIDControllerD);
}
The whole program can be seen on my mBed page at http://mbed.org/users/joe4465/code/QuadMK6/
If you need any more info or something explaining let me know.
If anyone can point me in the right direction or has any idea of what I should try next it would be very much appreciated.
Thanks
Joe
|
My FRC team will recently upgrade from CompactRIO to RoboRIO.
CompactRIO only supports up to Java 1.4.
What version of Java does RoboRIO?
|
I am trying to understand how a PID controller moves the poles and zeros of an transfer function.
I've been playing a bit with it, but aren't able to see some kind of connection.
I mean that P and I rises the Overshoot which would mean that the damping ratio gets smaller, thereby should away from the real axis.
and D should should do the opposite, but it doesn't seem to be true with the examples i've used.. am i doing something wrong??
Well i kind of just want a general knowlegde of how it affect second order systems.
|
I'm currently working on a school project about simulating robots scanning an area, but it has been a struggle to find what strategy the robots should use. Here are the details:
I am given a certain amount of robots, each with a sensing range of $r$. They spawn one after another. Their task to scan a rectangular area. They can only communicate with each other when they are within communication range.
I am looking for the best strategy, (i.e. time efficient solution) for this.
Any reply or clue to the strategy will be appreciated.
|
I have this close loop transfer function:
It overshoots, but why? The poles are placed such that that the damping = 1, so why the overshoot?
|
I identified my system and now I am trying to tune PI regulator since I think I do not need D.
I came across this graph while Matlabing and I do not know what does it mean.
I am using pidtune() to get my P and I values. (I think computation is all correct, I made model in simulink to confirm). Anyway see my picture and arrow is pointing at what I do not understand. Why is my system going below zero first?
It is supposed to be water flow regulator.
Transfer function:
$$ \frac{-0.311s + 0.05548}{s^2 + 0.06882s + 0.0007626}$$
Continuous-time PI controller in parallel form:
$$K_p + K_i * \frac{1}{s}$$
With $K_p = 0.256$, $K_i = 0.000342$
|
I am trying to implement a controller for an inverted pendulum using LQR (with MATLAB command lqr(A,B,Q,R)). The problem is that the motors are relatively weak, so I tried to increase R, but simulations show that the effort is still very high. How can I reduce the effort?
|
From what I understand, you can create a map using sensors and storing values in an array. For example, the array can have 0's for places not visited, and 1's for places visited, or 0's for open spaces and 1's for occupied spaces, etc.
The map I'm thinking of making will have about 1000 x 2000 members. This means I need an array of 2 million members. That seems like a lot. If I have eight attributes about each member (like temperature, light level, sound level, etc.), then I have 16 million pieces of information to capture.
Is this correct, or is there a better way to create a map for a robot using software?
|
Is it possible to determine PID parameter using pole placement.
I mean by solving the ch. eq. of close loop transfer functions which consists of either P,PI,PD or PID controllers??
Because i've tried it, an eventhough i am getting my poles at the locations I want the systems does not act as I assumed.
an example. I want my system to be overdamped and to have settling time less than 1 sec. which means that i want my poles to lie on the real axis, and to be less than -4.
$$G(s) =\frac{10.95 s + 0.9574}{s^2 + 0.09149 s + 6.263*10^{-6}}$$
With P = 0.1, I= 0.617746, d = 0.0147173
I get a close loop system which is
$$G_cl(s) = \frac{0.1612 s^4 + 1.109 s^3 + 6.86 s^2 + 0.5914 s}{ 0.1612 s^4 + 2.109 s^3 + 6.952 s^2 + 0.5914s}$$
But looking at it's step response I see it creates overshoot, which i cannot justify due to an step input...
|
Sorry if this is out of robotics, but this seemed the closest.
A motor has to be used to rotate three cranks. They are in the same plane, but each should not run at the same time.
Only one that occupies a central position, corresponding to the motor, should rotate. The discs are mounted in rectangular frame that can be slid to bring the required disc to the central position.
Can anyone suggest a method to achieve this? (Basically coupling and uncoupling the motor shaft from whatever disc is in the centre.)
|
I was recently reading that those artificial muscles had the highest power/weight ratio while electric motors only have a ratio of less than 100:1. As electrical engineer I have never worked with pneumatics before and do not have a big idea about air pumps.
My question is, how much will this ratio change if we consider an autonomous system.
On one side, the McKibben actuators + air pumps and valves + energy source and on the other side, electric motors + energy source. As an example, let suppose that somebody found a way to model and control the artificial muscles as good as the electric motors and make two autonomous walking leg models, which one would have a higher resulting power/weight ratio ?
|
I am interested in learning how to build a dynamic quadcopter, and hope to be fairly proficient with Arduino/raspberry-pi. What resources and/or practices might you recommend?
|
I'm trying to develop a control system to a quadcopter and one of my options is to use a PID controller (which I think is the most used method).
From what I've read, the commom strategy is to run a PID algorithm to each axis. My question is: how the PID output is converted to PWM signals?
I'm asking that because the three axes and the four rotors depend on each other. I mean, if I slow down a couple of rotors which are opposite to each other then the quadcopter will move down in a vertical axis. But if I speed one of them and slow down the other, the quadcopter will rotate in a different axis.
So we cannot isolate each axis and associate them with a single rotor or a pair of those. And because of that, how can a PID output (which is associated to an axis) can be converted to PWM signals to the rotors?
Is that a MIMO system?
|
https://www.youtube.com/watch?v=MPhhYlIq294
I have found this robotic arm on the internet, and I was wondering if someone can tell me what parts I need to build it or similar. The video says it can lift a small cat.
I need to be able to program it, so I'll need a controller.
Please recommend specific motor, controller links that you recommend. Thanks :)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.