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Imagine programming a 3 wheel soccer robot. What type of controller would you use for spinning it? P? PID?
The goal for this controller is that it should make the robot stand in a defined angle ( 0 degree ) and turn back if rotated by hand or other robot.
I use stepper motors for my robot and not servos so I need to implement this in my software!
I have written a sample P type controller already and the movement is fairly good. But I would like to make it better if possible. The code is as follows:
void spinSpeed(int devidedValue, int addedValue, int correction) {
if(degree<correction && degree>-correction) {
motorSpeed = 0;
} else {
if(degree > 0) {
motorSpeed = ((degree)/(devidedValue) + (addedValue));
} else {
motorSpeed = ((degree)/(devidedValue) - (addedValue));
}
}
}
correction is a range , in which robot has no movement.
degree is a number between -127 and 128 which is returned from the compass.
motorSpeed is a number between 0 and 255 which is applied to the PWM.
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I've got some hobby servos (Power HD 1501MGs) and I'd like to be able to control them (via an Arduino) so they will either go to the angle I set, or put them in a 'free running' mode, where the load will take them wherever it goes.
Is this even possible, or am I just going to end up stripping the gears?
My first thought is to simply kill the power to the servo, but the force required to move them in that state is more than I'd like.
If it is possible, am I looking at a hardware change, or could I do it in software?
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http://www.oricomtech.com/projects/leg-time.htm lists three gaits:
the tripod
wave, and
ripple.
Can these be improved, or can their relative pros and cons be altered, and are there other gaits worth considering?
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I am looking for a starting point for my project, preferably using popular systems (ones there is a lot of support for). I have an Arduino Uno, a Raspberry Pi, and a lot of willpower :) Anyone here built a project using the systems above?
Observation: I'd like to start with a simple line-following vehicle and build up afterwards.
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I'm trying to implement a nearest-neighbor structure for use in an RRT motion planner. In order to do better than a linear brute-force nearest-neighbor search, I'd like to implement something like a kd-tree. However, it seems like the classical implementation of the kd-tree assumes that each dimension of the space can be split into "left" and "right". This notion doesn't seem to apply to non-Euclidean spaces like SO(2), for instance.
I'm working with a serial manipulator arm with fully rotational links, meaning that each dimension of the robot's configuration space is SO(2), and therefore non-Euclidean. Can the kd-tree algorithm be modified to handle these kinds of subspaces? If not, is there another nearest-neighbor structure that can handle these non-Euclidean subspaces while still being easy to update and query? I also took a look at FLANN, but it wasn't clear to me from their documentation whether they can handle non-Euclidean subspaces.
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My company will soon be starting a brand new robotics project, and we are still trying to decide whether we should design and code a robotics software platform from scratch, or if there are any good existing ones.
It would be very useful if there was a software platform which was commonly used among both academics and industry so that our robotic system was generally compatible with others, and so that people were already familiar with it.
We would like the software platform to be able to:
Integrate new robotic hardware components easily.
Already contain a wide array of useful data processing and visualisation tools
Make efficient use of computing hardware
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What is the best software (despite the price) for designing the circuits and PCB boards for robots?
I mean having lots of components, different designing methods, best accuracy, ...
I myself use the Altium Designer which I think answers my needs, but maybe there are better ones in the market i don't know about!
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Most often tuning the Kalman filter noise matrices is done by trial and error or domain knowledge. Are there more principled ways for tuning all the Kalman filter parameters?
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I'm working with a Wild Thumper 6 wheel chasis that is designed for use with an RC controller. However I'd like to have a mapping to a keyboard for control as well. Can you suggest a set of keys and behaviors that you've used to deal with the continuous value control normally offered by a joystick or pair of joysticks? The standard wasd keys + an accelerate/decelerate pair? I'd also take a pointer to a videogame that you think does this well.
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What is the best option to use for the shooting system of a soccer robot?
I have already implemented a solenoid-based system for shooting and it works perfectly.
However, I'd like some other methods to check if they are better than mine.
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I am beginning work on a larger scale, 250-350 lbs wheeled robot and looking to use both optical and other means of object avoidance. I am concerned with a robot this large causing issues with running into things, including people as it has a top speed of 15mph and that would cause issues with safety. I am starting out with a remote control but am looking to have the robot become self contained. I have been loosely following the DARPA driver-less car project but will not have anywhere near the fiscal or power budget that they do for sensors and computers. Am I thinking to far afield with my idea of having a self-contained robot in the 250-300 lbs range that does not break the bank on optical object avoidance? Any comments or experiences will be greatly appreciated.
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There are many sites which explain briefly this problem and even propose combinations. I however would like a much more detailed explanation. What is going to give my quad the most agility? Do I need bigger motors/props in a heavy quad to achieve the same level of agility than in a lighter quad?
EDIT:
Here is what I have understood on the subject:
A quadcopter doesn't need high revving motors as there are 4 propellers providing thrust and high revving motors require more battery power.
Larger propellers give more thrust per revolution from the motor.
The question is focused more on the general characteristics of various combinations but some specific questions do spring to mind:
For a given combination what would be the effect of upgrading propeller size in comparison to installing higher revving motors?
What changes would need to be made to lift a heavier quad?
How can I achieve more agility in my quad?
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I want to localize a mobile robot equipped with a 2D laser scanner in a known indoor environment. The map is a 2D occupancy grid, but is not perfect.
What algorithms are appropriate for mobile robot localization?
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As in the title, I'd like to implement gesture recognition on my robot and I'm looking for the pros and cons between the Kinect and the Xtion - and also if there are any other sensible options available.
I'm thinking of the following, but open to other suggestions:
Accuracy
Price
Driver quality
Power draw
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I'm looking to potentially build an autonomous robot that will frequently venture off road, and remain autonomous for up to 6 hours at a time. I've found limited information however about the best tyre tread for this purpose, what could be most suitable?
I'm especially looking for a tread pattern that won't need regular cleaning, to save setting this up automatically (a tread that gets "clogged" very quickly clearly won't be that effective at tackling tough terrain autonomously.)
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Is there a good, popular and reliable algorithm I can use by taking input from a gyroscope and using this to control two independant wheels to keep such a balanced robot reliably upright? I'm looking for an algorithm that will let me use it to drive a robot around as well as keep it upright when stationary. The ability to deal with inclines and people nudging it would also be a bonus, but not essential.
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I'm looking to potentially build an underwater glider, a type of submarine that's slow but can operate on extremely low power draw. However, in order for it to work effectively I've found several sources hinting that the dimensions of the components, especially the wings, are critical to its success.
However, I've found very sparse information about what these dimensions should be! I'm happy to do a bit of trial and error if it comes down to it, but to save some work does anyone have any information on what the critical dimensions should be?
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I'm looking to build an underwater glider robot that will need to remain autonomous for long periods of time, perhaps even months. Power draw should be minimal, and I'm thinking of including some form of charging device (such as a solar charger) however I'd also like the battery capacity to be large enough so I don't hugely need to worry about this. Large current draw isn't really needed, but the battery does need to hold its charge effectively for long periods of time. Considering this is an underwater vehicle, weight and size are also a concern.
Cost isn't too much of an issue, as long as it's within reason of a hobbyist project.
I am looking to understand the pros and cons of each technology (Lead acid, LiPo, NiCad, fuel cell?), so I can decide what type of battery would be best suited to my purpose. As such, I'm looking at battery technology rather than looking for a specific shopping recommendation.
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It's common for components on some types of robots to experience large environmental stresses, one key one being vibration. Is this something I need to worry about with typical electronics and other sensitive components, or not really? If it is, then how do I secure such components?
I've heard of two main philosophies behind this, the first being that you should use a damping system such as with springs to absorb the shock. The second is that you should keep everything rigidly in place so it can't move, and therefore can't hit against anything else and break.
Which one should I follow, or if the answer is "it depends" what should I use as a guide as to best protect sensitive components?
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Obviously GPS is the most obvious and accessible technology for obtaining a locational "fix" for a robot at any particular time. However, while it's great sometimes, in other locations and situations it's not as accurate as I'd like, so I'm investigating whether there's a relatively easy way to improve on this accuracy (or not, if that turns out to be the case.)
I've considered the following options, but found limited information online:
Would using a much better antenna help, especially for low signal areas? I'm thinking yes to this, but if so how would I construct such an antenna and know that it's an improvement? Are there any good guides on how to do this? I could use a ready made antenna if they're not too expensive.
Would using multiple separate receivers in tandem help, or would they likely all be off by a similar amount, or would I not be able to extract a meaningful average with this approach?
What sort of characteristics should I look for when choosing a good GPS receiver to help accuracy?
Is there anything else I should consider which I've missed?
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Ultrasound sensors are incredibly cheap these days which makes them a popular choice for many hobbyist robotic applications, and I'd like to use a bunch of them (say 10) around a robot with an algorithm to build a rough map of an area (as the robot explores it.) I'm not interested in dealing with moving objects at this stage, just pinpointing stationary ones, and I'll be using GPS for location. I realise that other components such as a laser scanner would produce much more accurate results, however such devices are also astronomically more expensive.
Does an algorithm exist for this purpose?
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Hobby servos are generally not sufficient for real robotics for a number of reasons: Quality, precision, range of motion, torque, etc.
Meanwhile, industrial servos, such as ABB, Emerson, GE, etc, are generally both heavy and expensive, and not suitable for small-humanoid scale actuation. Similarly, building your own servo from motors, gearboxes, and encoders, is akin to trying to design your own CPU just to control a motor -- too much detail getting in the way of real work.
There exists an in-between level of servos -- reasonably priced, reasonable performance, and reasonably controllable -- in the form of the competing brands of Dynamixel and HerculeX servos.
The smallest offerings in those lines generally are not strong enough for real-world interaction, but the next step up hold a lot of promise. For the Robotis Dynamixel line, this is the RX-24F servo (priced between the cheap AX-12F and the next step up the MX-28R.) Asking around, it seems that the specs and interface on that servo is great, but that it shuts down from thermal overload if you actually try to run it at rated load -- something that I'd expect from a hobby servo, but not a robotics servo.
Now, stepping up to the MX-28R doubles the price. Thus, if the RX-24F heat flaw could be fixed, it would be positioned at a nice price/performance point.
Does anyone have experience in providing additional cooling for this servo? Anything from thermal-gluing heat sinks to the case, to drilling holes and running cooling fluid tubing to any hot parts on the interior would be reasonable approaches. However, before I spend significant time and effort investigating this, I'd like a second opinion -- is it possible, does anyone have experience doing this, is it worth it?
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What characteristics can I look for which could be reliable early warning signs that a DC motor on my robot, say one used for the drive, could be failing? I'm looking for an answer that deals in terms of sensors rather than manual inspection, so a circuit could be constructed to warn of a potential failure before it happens.
I have a few ideas such as an increase in current draw or decrease in rotation speed / voltage, but I want to guard against false warnings caused by reasonable wear and tear, or just the robot struggling on tough terrain.
Obviously such a system will never be foolproof, but are there any points I can look out for?
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With two wheeled robot like this one, I have managed to stabilize it while keeping it stationary. This was done using a digital feedback control system by reading the position of the wheels to determine position, and the natural back electromotive force from the wheel motors was used in the feedback loop to determine velocity. It was kept stable with a PID controller, which was designed using a root locus algorithm to keep it stable and modulate the performance parameters (such as percent overshoot, settling time, etc.). I wanted to attempt to keep it stable while simultaneously propelling it forward, but I couldn't figure out how to go about designing a linear controller that could do that. Is it possible to both propel the robot forward and keep it stable using a feedback controller on the wheels, or is a gyroscope necessary?
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I'm part of a FIRST Robotics team, and we're looking into using Mecanum wheels for our robot.
What are the advantages and disadvantages of using Mecanum wheel versus regular ones? From looking through Google, it looks like Mecanum wheels give more mobility but don't have as much traction. Are there any other advantages or disadvantages?
Compared to regular wheels, are Mecanum wheels less efficient or more efficient in any way? And if so, is there a quantifiable way to determine by how much?
Are there equations I can use to calculate efficiency (or inefficiency) and/or speed of moving forwards, sideways, or at arbitrary angles?
A picture of a robot with mecanum wheels:
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I've got this driver: http://www.pololu.com/catalog/product/1182
... a A4988 stepper motor driver carrier
I'm attempting to control a connection between the RESET and SLEEP pins with logic ( code ) running on my Arduino. The motor runs perfectly when these two pins are connected however I'd like to control when the stepper is powered off from my Arduino ( and thus not generating extra heat )
I'd like to:
designate a pin to control the connection between these two pins
use a "digitalWrite" to the above pin with a HIGH or LOW to
switch power on and off from the stepper
NOTE: The data sheet mentioned that for the driver to be powering the stepper both RESET and SLEEP needed to be in switched on ( HIGH )
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For a robotic gripper arm we are designing for factory floor use on very small components, we propose to use electrically activated Shape Memory Alloy (SMA) wire harnesses for actuation.
The device being designed is akin to Pick & Place machines used for circuit assembly, but moves over an aircraft-hanger sized work surface on wheels. It manipulates irregular shaped and porous objects between 0.5 cu.cm and 8 cu.cm each - hence the traditional vacuum P&P mechanism does not appeal. Also, individual objects in the assembly line have varying hardness and weights.
Our design constraints are:
Ensuring minimal to zero vibration and sound
Using minimal volume within the mechanism (batteries are at the wheelbase, providing stability, so their weight is not a concern)
Fine variation of gripper pressure
We believe SMA meets the first two constraints well, but need some guidance on achieving constraint 3, i.e. different levels of pressure of the gripper controlled electronically.
My questions:
Can PWM of a current above the activation threshold (320 mA for 0.005 inch Flexinol HT) provide variable, repeatable actuation force?
Would we need pressure sensors on each fingertip and a closed loop control for grip, or can the gripper be calibrated periodically and maintain repeatable force?
Is there any well-documented precedent or study we should be referring to?
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Design goal is to have a mobile robot that operates on 3 large casters, essentially 2 to 4 inch diameter steel ball bearings, that are motorized. No other mechanism would touch the surface. The robot should thus be able to move in any XY direction on a flat surface, with steering being achieved by varying the speed and rolling direction of these wheels. The robot has no designated "front" side, so it does not need to (and should not have to) bodily turn, in order to move off in any given direction.
Conventional wheels or tracks are not the preferred approach.
Looking for suggested mechanical layouts of multiple rubber wheels, pressing down onto the steel ball from within the castor housing, to drive the ball in any direction. A single wheel on a stepper, rotated around the vertical axis using a sail-winch servo, is one approach under consideration. Would this be ideal, or are there any serious flaws in this approach?
Alternatively, is there any other suggested method of driving such a steel ball in any arbitrary direction under electronic control?
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Many robots and other mechanical devices produce the signature whirring noise as they move, some produce less. What makes the difference? What restrictions a silence requirement places on a robot?
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I am looking for a servo drive to control a brushless DC motor, with at least 10A, 30V rating. However, I want to know if any exist which take sinusoidal hall sensor signals directly.
I already know there are servo drives taking hall sensor pulses (with 6 different phases), but that is trapezoidal control.
Note: a servo drive includes the driving electronics (no additional transistors required).
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I would be very interested to ask for a list of repos of free open code, applicable to 8-bit avr-s and having relation to robotics - object avoidance, process controllers, battery management, etc. This would be of huge help for me, preventing me from wasting weeks and months to invent the wheel.
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I have a number of interrupt service routines on an AVR. These include interrupts for USART serial communication, timers, and SPI communication.
For all of these, I use circular queues (using a start and end pointer, without boundary checking).
Some problems start to occur if the AVR starts to get overloaded. The circular queues will lose chunks of data. To solve this, I can reduce the load on the AVR (for example, by skipping some cycles in the timer). However, this is a manual process, where it is reduced if the AVR appears to have problems. This is partly because I do want relatively consistent timer periods.
However, even at 70% average processor load, the queues can fill up randomly by chance.
In case of spurious overloading at times, how can I make this more adaptive to avoid queue overflows?
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If you have used connectors for signal wiring for any length of time, you may find that they are unreliable.
Specifically, I find these to be unreliable when used for a long time, with a number of disconnections and re-connections:
This is due to the loss of springy-ness of the crimped metal end on the wire, which causes contact problems.
Which connectors (with rapid connection time) are reliable for multiple re-connections for simple signal wiring?
This excludes screw terminals and connectors with screws (eg. D-subminiature connectors), because they are not simple plug-in connectors.
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What is a suitable model for two-wheeled robots? That is, what equations of motion describe the dynamics of a two-wheeled robot.
Model of varying fidelity are welcome. This includes non-linear models, as well as linearized models.
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Emergency stops are obviously a good idea on most robots, how should they be wired? What systems should be killed immediately, and what should stay working?
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Range sensors (for example sonar, infrared, and lidar) are notoriously noisy. How can I characterize the noise characteristics to include these in a probabilistic localization sensor model?
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In graph-based planning (say, A*), states are connected to their neighbors. How should one decide whether to connect to the 4 neighbors or the 8 neighbors? What are the pros and cons of each approach?
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I am attempting to upload a custom firmware to a Lego Mindstorms NXT and am having issues.
First of all, I'm attempting to use nxtOSEK, which would let me run C++ programs on it. The problem is, everytime I put it into firmware update mode, the download doesn't seem to actually occur.
What I mean by this is that, according to the output in my terminal (both Mac and Windows), the download was successful, however when the NXT reboots, I still see the normal logo (not nxtOSEK).
So, what I'm doing is first holding down the Reset button for a few seconds, then hitting the orange button, giving me that tic-tic-tic sound. Then I run the firmware update (either using the Windows NextTool or Mac OSX GUI NextTool) and attempt the download. I get a success message, yet the robot is still using the old firmware.
What could be the cause of this problem and how can I solve it?
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When designing a standard 4 or 6 wheel robot, is it better to have the weight distributed primarily in the center of the robot, or over the wheels, or is there no difference?
Specifically, which weight distribution will make the robot less likely to tip over?
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Edit: I don't know why, but this question seems to be confusing many people. I am aware of when/where/why/how to use real-time. I am interested in knowing whether people who have a real-time task would actually care enough to implement it in real-time or not.
There's no need to mention why real-time operations are important for a robot. My question is however, how much is it actually used in robotics?
Take this question for example. Only one answer mentions any platform with real-time capabilities, and it is far from the top too. ROS apparently, being a very popular platform which is not real-time.
In the real-time world however, RTAI1 seems to be the only workable free real-time platform of use. It is however, limited to Linux (no problem), badly documented and slowly developed.
So, how much is real-time behavior sought after among robotics developers? The question is, how much are developers inclined to write real-time applications when real-time behavior is actually needed? If not much, why?
For example, reflexive behavior based on tactile data, cannot go through ROS because it would lose its real-time property. But do people really come up with a real-time solution or use ROS anyway, ignoring the real-time property?
1 or similarly Xenomai
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I Have an ATmega16 mc which is master on the i2c and a ATMega8 mc which is slave on the i2c.
I have connected the two mcs' sda and scl ports to each other alongside a pullup resistor.
Now I want to read a register from the ATMega8 using the ATMega16.
The problem is that I don't want to assign all the variables manually. Is there any libs or headers that will do this thing for me?
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A beginning graduate student in robotics asked me the areas of mathematics that he should brush up on (prerequisites) to begin a masters research program in robotics. What are some good materials/books that are indispensable for a research student? Which ones should we suggest in order that the student develops a solid foundation in robotics?
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What kind of sensors and algorithms are the mobile robots of Kiva Systems equipped with?
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I have a motor which drives a string connected to a load cell. I would like to implement a closed loop controller to control the load applied by the motor to the string.
How do I go about determining the required loop frequency in order to create a stable control system? Is it something like the Nyquist frequency, where the loop speed should be at least twice the highest frequency inherent in the mechanical system?
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For the 3d printer RepRap Prusa there are several rails (smooth rods) that guide the printer head on the different axises. The printer head uses several linear bearings to glide along the rails.
There isn't any specification on what kind of material would be best suited for this purpose with the linear bearings. My first assumption would be for stainless steel because it won't corrode (rust) on the surface, but I'm not sure if this is true for all printers (whether they are 3D printers or not) as a different material may allow the linear bearings to glide more easily. Aluminum would have been my second choice but I have the same reservations of which grade would be least resistant.
This resource has some limited information but does not help with which would be best suited for this particular application.
What material is best suited for this purpose?
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I am not sure if this has been tried before but I am trying to use Kinect and detect gestures made by the Nao robot.
I have made a Kinect application, a gesture based picture viewer and it detects humans fine(Obviously it does!) What I wanted to try was (lazy as I am), to see if I could use some (say, voice) command to tell the Nao to do a Swipe Right gesture and have my application identify that gesture. The Nao can easily identify my command and do some gesture. The problem however is, when I put the Nao in front of the Kinect sensor, the Kinect does not track it.
What I want to know is, are there some basics behind Kinect's human body motion tracking that essentially fails when a robot is placed in front of it instead of a human?
PS: I have kept the Nao at the right distance from the sensor. I have also checked if the entire robot is in the field of view of the sensor.
EDIT: This has been posted on stackoverflow and on msdn by me so as to target a large audience as this problem has not been encountered by anyone in the past.
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Whenever building an aquatic bot, we always have to take care to prevent leakages, for obvious reasons. Now, holes for wires can be made watertight easily--but what about motors? We can easily seal the casing in place (and fill in any holes in the casing), but the part where the axle meets the casing is still left unprotected.
Water leaking into the motor is still quite bad. I doubt there's any way to seal up this area properly, since any solid seal will not let the axle move, and any liquid seal (or something like grease) will rub off eventually.
I was thinking of putting a second casing around the motor, maybe with a custom rubber orifice for the shaft. Something like (forgive the bad drawing, not used to GIMP):
This would probably stop leakage, but would reduce the torque/rpm significantly via friction.
So, how does one prevent water from leaking into a motor without significantly affecting the motor's performance?
(To clarify, I don't want to buy a special underwater motor, I'd prefer a way to make my own motors watertight)
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Often we use microcontrollers to do things in our robots, but need to make some calculations in decimal. Using floating point variables is very slow, because a software floating point library is automatically included (unless you have a high-end microcontroller). Therefore, we generally use fixed point arithmetic.
Whenever I do this, I just use an integer, and remember where the decimal place is. However, it does take some care to ensure that everything is consistent, especially when calculations involve variables where the decimal point is in a different place.
I have implemented a fixed point atan2 function, but because I was trying to squeeze every last drop of limited precision (16 bits), I would often change the definition of where the decimal point is, and it would change as I tweaked it. In addition, I would have some constants, as a quasi look-up table, which themselves have an implied decimal point somewhere.
I want to know if there is a better way. Is there a library, or set of macros, that can simplify the use of fixed point variables, making multiplication and division between mixed variables easier, and allowing declaration of decimal numbers or constant expressions, but automatically converting to the desired fixed point representation at compile time?
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At our lab, we have a several "Kurt" type robots (about the size of a Pioneer, six wheels, differential drive). The built-in gyroscopes are by now really outdated; the main problem is that the gyroscopes have a large drift that increases as the gyro heats up (the error is up to 3°/s). We mainly use the IMU (inertial measurement unit) to get initial pose estimates that are later corrected by some localization algorithm, but even doing that the large initial pose error caused by the IMU is often annoying.
We've temporarily used an Android phone (Galaxy S2) as a replacement IMU, and the results are so much better compared to the old IMUs. However, I don't like depending on a WiFi connection between IMU and the control computer (a laptop running ROS/Ubuntu), so we're looking to buy a new IMU.
What IMU should we choose? What criteria are important to consider for our application?
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Tuning controller gains can be difficult, what general strategies work well to get a stable system that converges to the right solution?
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Using an IMU a robot can estimate its current position relative to its starting position, but this incurs error over time. GPS is especially useful for providing position information not biased by local error accumulation. But GPS cannot be used indoors, and even outdoors it can be spotty.
So what are some methods or sensors that a robot can use to localize (relative to some frame of reference) without using GPS?
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I have a time-series of force data of robots interacting with environment objects with various textures. I would like to develop models of various textures using the time-series data to classify textures into smooth, rough, moderate, etc. categories. For this purpose, will Hidden Markov Models be sufficient or should i use Conditional Random Fields? If I decide to classify into more categories and the distinction between each of are categories are very subtle, in that case what would be a good choice? Will force-data be sufficient to capture all the information I need to classify textures into these categories?
Thanks for your replies :)
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I have a joint actuated by an antagonistic pair of Pneumatic Muscles.
There are two valves per muscle, one to fill and one to empty each muscle. The joint has an angle sensor, and each muscle also contain an air pressure sensor.
What is a suitable control algorithm set up?
A PID controller controlling the valve orifice sizes?
A PID controller controlling the mass flow rate?
A PID controller controlling the pressure using two PID pressure controllers?
A Fuzzy Logic controller?
A Neural Network?
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Robotics has always been one of those engineering fields which has promised so much, but is taking a long time to deliver all the things that people imagine.
When someone asks: "How long before we have [X] type of robots?" Are there any resources we can call upon to try to calculate a rough answer. These resources might include:
Rate of progress of computational power, and some estimate of how much will be needed for various types of AI.
Rate of progress of electrical energy storage density, and some estimate of how much will be needed for various types of robot.
Rate of progress of actuation systems, and some estimate of what would be needed for various types of robot.
Lists of milestones towards various types of robot, and which ones have been achieved and when.
Are these types of studies performed, and are the results published?
Added:
In response to Jakob's comment, I am not looking for opinions or discussions on this subject. What I am looking for are published studies which might shed light on this question.
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Most of the linear actuators I've seen are nonuniform and/or slow. Those using a cam or crankshaft-like mechanism (and nearly anything hydraulic/pneumatic) cannot be moved at a constant speed without some programming. Those using a screw-like mechanism are uniform, but slow.
Aside from a rack and pinion/rope wound around a stick, what other fast, uniform linear actuators exist? By uniform, I mean that the speed is uniform (Or the distance moved is linearly dependant on the angle rotated by the motor)
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In SLAM frontends which use the Iterative Closest Point (ICP) algorithm for identifying the association between two matching point clouds, how can you determine if the algorithm is stuck in a local minimum and returns a wrong result?
The problem is defined as matching two pointclouds which are both samples of some arbitrary surface structure, and the sampled areas have an overlap of 0-100% which is unknown. I know the Trimmed ICP variant works by iteratively trying to determine the overlap, but even this one can be stuck in a local minimum.
A naive approach would be to look a the mean square error of the identified point pairs. But without some estimate of the sampling this seems a risky thresholding. In the manual for the Leica Cyclone they suggest manual inspection of the pair error histogram. If it has a Gaussian shape the fit is good. If there is a linear fall-off the match is probably bad. This seems plausible for me, but I've never seen it used in an algorithm.
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I have a simple servo system that uses a PID controller implemented in an MCU to perform the feedback. However, the properties of the system change dynamically, and so the PID parameters can never be tuned for all circumstances.
My robot is a light weight arm with back-drivable electric motors, similar to this one:
The arm performs several tasks, including picking up heavy weights, pushing and pulling objects across the desk. Each of these tasks requires different PID tuning parameters which I cannot easily predict.
What I would really like is for some higher level function which can carefully adjust the parameters in response to the arm's behaviour. For example, if it notices that the arm is oscillating, it might reduce P and increase D. Or if it noticed that the arm wasn't reaching its target, it might increase I.
Do such algorithms exist? I would be happy even if the algorithm didn't perfect the parameters immediately. E.G. the arm could oscillate a few times before the parameters were adjusted to their new values.
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Smart phones these days typically come with a gyroscope, accelerometer, compass, camera, and GPS sensor all on board. They also usually have a connection to the internet with Wifi and mobile data networks. I've seen many cases of using a phone as a remote control for a robot, but to me, it seems like the phone itself is a perfect lightweight computing and sensing platform for an autonomous robot.
The main obstacle I see is interfacing with actuators. Being able to control motors to steer even a table-top robot, or control servos, for example. Connecting and communicating to a microcontroller could be an obstacle as well.
As a robot hobbyist, I'd like to know how I can overcome these and other obstacles to be able to harness the power of my smart phone with my robotics projects.
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I'd like to start making robots and tinkering with microcontrollers. Where do I start, and what do I need?
I'd like to make my own robots. I'm comfortable with programming (assembly and C) so I've got that part covered, but my electronics/circuits knowledge is a little weak. I have no idea what material to start with and which tools I need, nor how to put stuff together.
For the microcontroller, I'm thinking about going with the Pololu Orangutan LV-168 or the Arduino Duemilanove, although I'm leaning more towards the Orangutan because of the built-in LCD and pushbuttons (which I would expect to use, especially for debugging and user interaction). Am I on the right track? It seems to me like the number of I/O ports is small, but is that the case in practice?
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In my application, my robot has the following physical setup:
Differential drive mechanics with feedback (wheel encoders)
Commercially available webcam mounted with a known transform to the base of the robot (RGB, no depth)
The robot will be navigating through a structured, indoor type environment (think office, home, or university), and I would like to be able to determine the navigable paths through the environment using my vision sensor.
What is the best way to approach the problem of finding safe paths to travel when given a single vision sensor?
Edit: I think that I am more interested in the vision processing techniques than the actual path-planning mechanics.
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In ROS, I cannot get [ERROR] logs to print in red when I use python. How can I make them appear in red instead of black?
For example, the following Python:
rospy.logerr("No analog input received.")
produces this output in black:
[ERROR] [WallTime: 135601422.876123] No analog input received.
whereas the following C++:
ROS_ERROR("No analog input received.");
produces the following output in red.
[ERROR] [135601551.192412]: No analog input received.
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Can ROS run on a Raspberry Pi?
ROS is resigned to run on a network of machines, with different machines, even different cores on the same machine doing different jobs. Can one of those machines be a Raspberry Pi?
I am considering using an R-Pi as the EtherCAT master on a mobile robot, communicating with the main PC over WiFi, using a dongle.
Can an R-Pi even run ROS at all?
Would an R-Pi have enough processing power to do some 1kHz servoing?
Would it be possible to run some servoing on the host through the WiFi connecion?
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With a 600 mm (2 foot) motor-to-motor quadcopter, what frequency does my output-sense-calculate-output update loop need to stay stable?
I'm estimating a total takeoff weight of very roughly 2 pounds ( 0.9 kg ),
which I expect to be mostly motors and batteries.
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I've seen 3 approaches to mounting batteries on a multicopter:
All the batteries rigidly mounted near the center of the airframe
All the batteries in a bag hanging under the center of the airframe
Each rotor has its share of the batteries rigidly mounted near/under it. (For example, a quadcopter with 1/4 of all the batteries mounted underneath each motor).
Which design is the best, and why?
If there is no one best design, what are the advantages/tradeoffs between the designs?
Is there some other design I'm overlooking that is better in some way?
(This question focuses on multirotor flying machines.
For ground vehicles, see " Is it better to have weight distributed over the wheels or the center of the robot? ").
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A common scenario is to have a PC that sends commands to a microcontroller via RS232. My PC program is sending commands (each of which are composed of multiple bytes) as fast as it can to a small robot. The microcontroller on the robot is a Parallax Propellor.
I have noticed that if I don't process bytes quickly enough on the microcontroller side of things, it can very quickly overflow the default buffers in the popular serial port drivers that are available for the Propellor. (The buffers are generally anywhere from 16 to 256 bytes). I can arbitrarily increase these buffers or create my own larger circular buffer, but I would like to have a more methodical approach to determining appropriate size requirements and/or the minimal amount of time I can wait before pulling bytes out of the serial port driver buffer.
At 1st glance:
115200 == 115.2 bits per millisecond == ~12.8 bytes per millisecond (assuming 1 stop bit)
1) Is that a valid way to calculate timing for serial transmissions?
Also, given my specific setup:
PC Program <--> Bluetooth Serial Profile Driver <--> Bluetooth Transceiver <-*-> BlueSMIRF Wireless Modem <--> Parallax Propellor Program
2) What is the maximum amount of data I can send for a given period of time consistently without eventually running in to problems?
Maybe I'm over complicating things, but it seems like there are potentially multiple buffers involved in the transmission chain above. How do others commonly deal with this? Do they throttle the PC sending to a known safe rate? Implement flow control? If implementing flow control, how does that affect bandwidth and response times?
(If it matters, my experiment is to use a joystick on the PC to control multiple servos with instant reaction to the joystick movements. So every small movement of the joystick results in multiple commands being sent to the microcontroller. The commands are not just simple positional commands though, they also involve acceleration/deacceleration of servos over time and this is the reason that the microcontroller spends a significant amount of clock cycles before processing new bytes.)
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I would really like a six-axis force/torque sensor for my robot, but I just can't afford one. I was thinking about making one of my own.
I have experience using strain gauges, but I can't work out how to arrange them so as to create a six-axis force/torque sensor.
Is this something I could feasibly make myself?
How do they work? What is the theory behind them?
I'm curious to know how they work, even if it's not feasible to make one myself.
Added:
Just to be clear, I'm talking about force / torque sensors, like this ATI Nano 17:
I am not talking about accelerometers or gyros, or MEMS IMUs.
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I have a robot that uses brushed motors in its servo system. These are Maxon 3W motors, with 131:1 planetary gearboxes. The motors are controlled by a PIC microcontroller, running a 1kHz PID controller. The servos are for a low speed high torque application. There is significant backlash between the sensor and the motor.
Maxon offer 12W brushless motors which are the same size. These are better in many ways: double the torque, better heat dissipation, higher efficiency.
The problem, obviously, is that they require more complex drive electronics. Also, I have heard a couple of people mention that brushed motors are better for servo applications, though they never explained why.
Has anyone else implemented this kind of system?
Are there any gotchas when using brushed motors for servos?
Is it possible to servo it at low speeds if I only have the 3 integral digital Hall sensors, and no encoder? (I would prefer not to add an encoder because of the money and space cost)
It torque ripple likely to be a problem?
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I'm really new to robotics, however I am a programmer familiar with several different languages. I don't have a ton of money to spend and I was wondering what is a really good starter kit.
My criteria is for the kit to be inexpensive and powerful, in that its functionality is extensible -- something that would allow the builder to be creative and possibly invent new ways to use it, not just a glorified model kit.
Being extendable to smartphones is a plus.
I'm not looking for something easy or introductory, just something powerful, flexible, and cost effective.
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I want to use an RF beacon to localize my quadcopter for autolanding, when GPS is not precise enough, for example, when my driveway is only 10 feet wide, and the GPS is only showing 20-30 ft. accuracy (with a proverbial lake of lava on either side). The quadcopter would use the GPS to fly to the rough location until it had a strong enough signal off the beacon, when it would begin to use that signal to come to a landing in a precise location, referenced off said beacon. Can someone please explain to me the concepts and theories behind building the beacon and it's accompanying receiver (suitable for connection to an Arduino via any digital or analog method) and achieving, say, a 4" or better horizontal and vertical accuracy within a 50' sphere? Minimally, the quad should have range and altitude, i.e. "I am 10 feet away from the beacon and 2 feet above it". How much added complexity would it take to make the robot fully position aware about the beacon, i.e. "x ft. South, y ft. West and z ft. above it", where the coordinate system is determined by the beacon and not linked to any sort of geographic coordinate system? If the beacon is mounted on a, say, 10 ft pole, are there any changes to be made versus having it on the ground and presuming that all activity takes place above it's x-y plane?
Last note-
This thing would prefferably operate in the 72MHz band, please presume that where I'm operating, there are not other devices operating on the same band.
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I have seen waveforms for driving a brushless motor.
I guess this is the waveform used for the simpler block commutation. But if I want to do sinusoidal waveforms, what does the PWM signal look like now? Is there a need to carefully synchronise the edges on the three phases?
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Do mobile and/or autonomous robots become more or less effective the bigger they get? For example, a bigger robot has bigger batteries, and thus bigger motors, whereas a smaller robot has the exact opposite, making it need less energy, but also have smaller motors. Is there any known theorem that models this?
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I've seen many motors having capacitors attached in parallel in bots. Apparently, this is for the "safety" of the motor. As I understand it, all these will do is smoothen any fluctuations--and I doubt that fluctuations can have any adverse effects on a motor. Apparently these protect the motor if the shaft is being slowed/blocked, but I fail to see how.
What exactly is the function of such a capacitor? What does it prevent, and how?
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I have two Unmanned Aerial Vehicles (planes) which work well. They can fly to various waypoints automatically using GPS.
Now I would like them to fly together in formation. I would like them to fly side-by-side fairly close. This is too close to reliably use GPS to guarantee that they keep the correct relative positions safely, and so I am looking for another way.
Somehow the UAVs need to be able to measure their position and orientation in space relative to the other one. How can I do this? Is there some kind of sensor which can do this? It would need to have the following properties:
6 axes (position and orientation)
Range 0m - 5m, (from between plane centres, but planes won't actually ever touch wingtips)
Works in day or night and all weather conditions
Light weight (This is for 1.5m wingspan RC planes, so max extra weight of about 100g)
Probably need about 50Hz - 100Hz refresh rate, but might get away with less, using the IMU to fill in the gaps
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Some years ago, there where some projects that provided hardware and software to perform modifications on standard hobby servos to convert them to digital servos, with all the advantages that come with it.
OpenServo is a little outdated, and does not seem to be worked on anymore, and there is no hardware to buy.
Sparkfun has its own version of the OpenServo, which at least is available for buying.
Do you know if there are other mods, or even complete low cost digital servos? I am mostly interested in position feedback, and servo chaining.
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I am designing an unmanned aerial vehicle, which will include several types of sensors:
3-axis accelerometer
3-axis gyroscope
3-axis magnetometer
horizon sensor
GPS
downward facing ultrasound.
A friend of mine told me that I will need to put all of this sensor data through a Kalman filter, but I don't understand why. Why can't I just put this straight into my micro controller. How does the Kalman filter help me about my sensor data?
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I wish to build a robotic arm that can lift a useful amount of weight (such as 3-6 kg on an arm that can extend to approx 1.25 meters). What actuators are available to accomplish this? The main factors and design points are:
Not Expensive
5 to 6 DOF
to be mounted on a yet to be designed mobile platform
battery powered
stronger than hobby servos (at least for the 'shoulder' and 'elbow' joints)
not slow to actuate
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As somebody who is spending the majority of his time programming in JavaScript, what's the best route to get into small-robotics without needing to deviate too much from my current language focus?
Are there any project kits or tools that make use of the JavaScript language that might make the field more approachable for developers like myself? I would even be interested in virtual environments where all code is executed in a simulation.
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The forward kinematics of a robot arm can be solved easily. We can represent each joint using Denavit–Hartenberg transformation matrices.
For example, if the $i^{th}$ joint is a linear actuator, it may have the transformation matrix:
$T_i = \left[\begin{matrix}
1&0&0&0\\
0&1&0&0\\
0&0&1&d_i\\
0&0&0&1
\end{matrix} \right]$
where the extension length is defined by $d_i$
whereas, a rotating link may be:
$T_i = \left[\begin{matrix}
1&0&0&L\\
0&\cos\alpha_i&-\sin\alpha_i&0\\
0&\sin\alpha_i&\cos\alpha_i&0\\
0&0&0&1
\end{matrix} \right]$ where $\alpha$ is the angle, and $L$ is the length of the link.
We can then find the position and orientation of the end effector by multiplying all the transformation matrices: $\prod{T_i}$.
The question is, how do we solve the inverse problem?
Mathematically, for a desired end effector position $M$, find the parameters $d_i$, $\alpha_i$ such that $\prod{T_i} = M$. What methods exist to solve this equation?
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What's the best kind of spline that can be used for generating trajectory that can be adapted during execution time?
The use case is having a differential drive which has to move towards a point (x,y,theta) without stopping during the movement (e.g. no, turn toward the goal, straight move to the goal position, turn to the goal orientation). The robot is provided with a laser scanner for detecting dynamic obstacles which have to be avoided.
What's the best kind of controller in this case?
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I've been doing a lot of reading lately about Subsumption Architecture and there are a few different ways people seem to advocate.
For instance some people use a global "flag" variable to have a task take control. Others use the endTimeSlice() and allow the arbiter to really choose. And I think this is correct.
I have this small section of RobotC code that I'm working on for a line following robot but am not sure I am doing it right as currently the track method will always take over the find method. The correct flow should be that find should guide the robot to the line using a spiral path to find the line. Once the line is found track should take over.
task evade(){
if(SensorValue(forwardSonarSensor) > threshold){
//box the obstruction
}
}
task find(){
if(SensorValue(lightSensor) > threshold){
//spiral the robot
}
}
task track(){
if(SensorValue(lightSensor) < threshold){
//go straight
}else{
//execute turns to follow the line
}
}
task main(){
while(true){
StartTask(evade,9);
StartTask(track,8);
StartTask(find,7);
wait1Msec(250);
}
}
I've just used some comments here rather than the actual code to keep it brief. Are my if statements not good enough as conditions because when the robot is off the line, track() takes over. Is this due to the else statement within track? If so, how to have track() perform turns when it looses the line without taking over from forage at the start of the program?
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I have built a few simple X/Y/Z CNC machines. I've learned about G-Code, motor control, firmware and open loop systems. I see machines like rovers, big dog and factory arms that seem incredibly complex by comparison, yet they don't seem that magical any more.
What are the important skills to pick up from working with CNC machines? What's the next logical thing to learn? What things would CNC machines never teach me?
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Is anyone able to help me out getting IPC-bridge working on my ubuntu lucid installation (with matlab 2012a)? I'm not being able to finish the last step on here (Compiling the messages folders): https://alliance.seas.upenn.edu/~meam620/wiki/index.php?n=Roslab.IpcBridge#Installation
I'm able to rosmake the ipc_bridge_ros, however when i enter this "roscd ipc_roslib && make", it seems meX does not recognize the commands. Here is what i get (screen shot): http://img13.imageshack.us/img13/6031/screenshot20121108at191.png
NOTE: i'm going to use IPC-bridge so that i can control a pioneer 3DX and implement a Fast-slam algorithm in matlab.
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I'm building a 4 legged robot (quadruped) with 3 Degrees of freedom per leg.
The goal of my project is to make this robot able to learn how to walk.
What learning algorithms will I need to implement for it to work?
I'm using an Arduino Uno for the microcontroller.
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We are using a Koro robot for our PC based automation solution. But sometimes the robot is getting the command but refuses to respond. Then I get a serial communication timeout error. The error is happening for a random type of commands and it is also not happening all the time making the troubleshooting difficult.
I doubt the driver problem. How do you approach this problem.?
Thanks
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I have a platform with two tracks and two motors. Each one uses an electronic speed control with "double tap to reverse". Each ESC takes an input pulse train frequency from at 1500 neutral +/-700.
I'm interested in learning if there are algorithms or a list of commands I can use to control how such platform executes maneuvers. For example:
Lock one thread and have the platform rotate by using the other one
Have two treads rotate in opposite directions
Execute a U turn
I'm struggling with expressing in code how such maneuvers should be executed. There's a "dead" zone around the 1500 pulse train frequency where the ESC output is too weak to cause the platform to move. The double tap to reverse also makes it tough to understand for how long each track should be turned off.
Thank you for your input
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I am thinking of developing a tendon driven robot manipulator for an industrial application that requires a high level of reliability. However, I am aware that tendons in a robot are prone to wear and tear, and failure.
How can I go about selecting a suitable tendon material (steel, kevlar, spectra, etc.) and use it appropriately?
Have any studies been undertaken to examine longevity and failure patterns in robotic tendon materials?
If I were to perform tests on materials myself, how can I perform those tests efficiently, and make best use of the testing time (learn as much as possible about tendon failure in a reasonable length of time).
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Is there a taxonomy of errors that are common in robotics? Things that come to mind but I don't have names for are:
Getting stuck in a stable infinite loop
Going into an unstable feedback loop (A balancing robot overcompensating more with each correction)
An inability to generalize between tasks (Pick up a bowl vs pick up a glass)
An inability to generalize between 'similar' sensory inputs.
Causing damage to itself or its environment.
These would be things that make a robot look 'stupid' to a non-roboticist. If you're curious I want to have this list so I can then prepare a clear answer ready for people who don't know why these various things are hard.
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Given a six-axis articulated robot arm holding a tool at its end-effector, if I have a desired tool position and tool orientation, there will be exactly 1 solution to the inverse kinematics equation for the robot to reach that position.
(or rather up to 16 different solutions, depending on range of the joints)
But if the robot is holding something like a pen, and I want the robot to mark a specific point with that pen on the target, then I do not care how the pen is oriented, as long as it is perpendicular to the marked surface.
So the inverse-kinematics equation will have infinitely many solutions.
How can I pick among these solutions the joint configuration that is closest to the current configuration: the one that will require the least amount of movement to reach?
(or the joint configuration that is optimal according to some other similar criterion, such as that all joint angles are furthest from their maximum and minimum?)
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I am interested in learning more about subsumption architecture. I have read a number of books that talk about the idea but none of them go into great detail. I have also read a fair number of Dr. Brooks papers on the topic however he hasn't published much on the topic in recent years.
Is this still an active area of research? Are there are any must read papers on the topic?
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I am looking for a way to restrict a robot's range of motion, using complex constraints such as not tearing of a cable attached to the robot.
Take an articulated 6-axis robot arm as shown below, with attached cable (red), fixed at points X (before axis A4) and Y (after axis A6).
The cable will limit the range of movement for the robot. It can stretch and bend only to some extend, but something like a full 360° turn of axis A4, with all other axes remaining as they are in the picture, will tie the cable around the arm and rip it off.
If joint A5 is at 0°, then A4 and A6 can still move the full 360°, but they cannot diverge too much from each other, as that would twist the cable. If A5 is tilted, the relationship becomes even more complicated.
How can you express such a constraint?
It is not a simple joint constraint, where you can independently limit the range of the joints, and it is also not a positional constraint, where you can define a region the robot must not enter. Checking a start and a goal posture is not sufficient, since along the path from start to goal posture there may still be a posture that puts too much strain on the cable.
Without limiting the robot to a small set of pre-tested paths, how can you limit the robot to movements that will not rip off the cable?
What are the standard techniques used for this sort of problem?
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Currently I have a tricycle style robot that uses an extended kalman filter in order to track 6 state variables. The inputs to the system are a steer encoder, a distance encoder, and a rotating laser that returns bearing only information to known landmarks. Currently both encoders are located on the main wheel (The one that steers, and is also powered).
The 6 variables tracked by the Kalman Filter are X, Y, Heading, Distance Scaling (calibration of the distance encoder), Steer Calibration (offset of the steer encoder), and finally a bearing calibration of a rotating laser.
With this kind of system we put together a vehicle give it a known good location with plenty of landmarks, drive it around a bit, and end up with a well calibrated vehicle that can drive extended distances reliably with few landmarks. Its simple and it works great. Over time if an encoder drifts it will automatically follow the drift and adjust.
We are now attempting to apply the same principles to a robot with multiple steer and drive wheels. In this case the vehicle will be able to move in any direction, spin in place, etc. . Each steer/drive wheel will have its own steer and distance encoder that each need to be calibrated.
Can I expect to get the same kind of reliability and performance out of the more complex system? Are there any common pitfalls to look out for when expanding a kalman filter to include more variables? Is there a risk of it settling on sub-optimal values?
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I am considering programming a line following robot using reinforcement learning algorithms. The question I am pondering over is how can I get the algorithm to learn navigating through any arbitrary path?
Having followed the Sutton & Barto Book for reinforcement learning, I did solve an exercise problem involving a racetrack where in the car agent learnt not to go off the track and regulate its speed. However, that exercise problem got the agent to learn how to navigate the track it trained on.
Is it in the scope of reinforcement learning to get a robot to navigate arbitrary paths? Does the agent absolutely have to have a map of the race circuit or path? What parameters could I possibly use for my state space?
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Are inverse kinematics and reinforcement learning techniques contending techniques to solve the same problem viz. movement of robotic manipulators or arm?
By a glance through the wikipedia article, it appears that inverse kinematics seems to attempt to achieve a solution as opposed to reinforcement learning which attempts to optimizes the problem. Have I misunderstood anything?
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I want to build a robot arm that'll be approximately 1.25 meter long and will be able to lift up to 2 kilograms. It'll have 6 dof and it is an expensive project. And most importantly, i am the only programmer in this brand new robotics facility of ours. :)
The robot that i want to build will be led by Inverse Kinematics, so with all these parameters and matrices, i think that i'll need a tough processor (Not so sure).
Assuming that my robots control interface will be on an Android tablet, i thought that i also could develop my program for Android, and send necessary commands to the control chip via RS-232 interface.
So, my question is, are standart 1 GHz Android tablets suitable for these tasks? If not, has anybody got an advice for me?
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My team and I are setting up an outdoor robot that has encoders, a commercial-grade IMU, and GPS sensor. The robot has a basic tank drive, so the encoders sufficiently supply ticks from the left and right wheels. The IMU gives roll, pitch, yaw, and linear accelerations in x, y, and z. We could later add other IMUs, which would give redundancy, but could also additionally provide angular rates of roll, pitch, and yaw. The GPS publishes global x, y, and z coordinates.
Knowing the robot's x y position and heading will useful for the robot to localize and map out its environment to navigate. The robot's velocity could also be useful for making smooth movement decisions. It's a ground-based robot, so we don't care too much about the z axis. The robot also has a lidar sensor and a camera--so roll and pitch will be useful for transforming the lidar and camera data for better orientation.
I'm trying to figure out how to fuse all these numbers together in a way that optimally takes advantage of all sensors' accuracy. Right now, we're using a Kalman filter to generate an estimate of [x, x-vel, x-accel, y, y-vel, y-accel] with the simple transition matrix:
[[1, dt, .5*dt*dt, 0, 0, 0],
[0, 1, dt, 0, 0, 0],
[0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, dt, .5*dt*dt],
[0, 0, 0, 0, 1, dt],
[0, 0, 0, 0, 0, 1]]
The filter estimates state exclusively based on the accelerations provided by the IMU. The IMU isn't the best quality; within about 30 seconds it will show the robot (at rest) drifting a good 20 meters from its initial location. I want to know out how to use roll, pitch, and yaw from the IMU, and potentially roll, pitch, and yaw rates, encoder data from the wheels, and GPS data to improve the state estimate.
Using a bit of math, we can use the two encoders to generate x, y, and heading information on the robot, as well as linear and angular velocities. The encoders are very accurate, but they can be susceptible to slippage on an outdoor field.
It seems to me that there are two separate sets of data here, which are difficult to fuse:
Estimates of x, x-vel, x-accel, y, y-vel, y-accel
Estimates of roll, pitch, yaw, and rates of roll, pitch, and yaw
Even though there's crossover between these two sets, I'm having trouble reasoning about how to put them together. For example, if the robot is going at a constant speed, the direction of the robot, determined by its x-vel and y-vel, will be the same as its yaw. Although, if the robot is at rest, the yaw cannot be accurately determined by the x and y velocities. Also, data provided by the encoders, translated to angular velocity, could be an update to the yaw rate... but how could an update to the yaw rate end up providing better positional estimates?
Does it make sense to put all 12 numbers into the same filter, or are they normally kept separate? Is there already a well-developed way of dealing with this type of problem?
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I have been using the Cyberglove to control a humanoid robot hand, but found it disappointing as it doesn't measure the posture of the human hand very accurately.
I have been wondering about the possibility of using Inertial Measurement Units (IMUs) mounted on the fingers to track position and measure posture. But I'm not sure how feasible it is.
Would an IMU return enough data to make tracking reliable in all circumstances?
Would it be possible to fool the system into incorrectly tracking the fingers?
Might it be possible to get away with using simple 3-axis accelerometers, or would it need 9-axis (accelerometer, gyro, and magnetometer)?
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I am aware of the legislation's in Nevada, but what is happening with the technology currently. When is it expected to be commercialized ?
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I have never used an accelerometer before, but I am aware that they come with I2C, SPI and analog outputs. If I choose to use an I2c or SPI, device, will I accumulate errors due to communication time?
Is the fast sampling of an analog signal likely to get me a more accurate deduced position than using am I2C?
Will this be true for
A robot moving in a room
A robot moving in an outdoor terrain and is likely to slip and roll down a slope.
Also, I have no sense of Gs. I tried to move my hand around fast with my phone running andro-sensor in my fist and saw that the readings saturated at 20m/s2. What G can I expect my robot to experience if it is hit by another fat moving bot or bumped by a fast walking human?
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I have not bought any parts yet, but I am making my own quadcopter. I have done the research and know all about the parts that I need, but many guides are sponsored and cost thousand(s) of euros/dollars while not explaining things entirely clearly.
Firstly, I have found this flight control board. Would I need another microcontroller (such as the Arduino nano) for it to work? (IF ANYONE has experience with this board, let me know!).
Secondly, would the above board work with this radio controller. Are controllers universal?
(Please tell me if I'm not in the right section here, or if this doesn't count as a relevant topic).
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I apologize if this question may sound a little vague. I am working on a robotics project that will contain 27 servos of various sizes and I am having trouble figuring out how they should be powered.
I was hoping to use several (3-6) 5 W 18650 battery boxes to power them, but the smallest motors would use 2.5 W each, so 1 battery box can only power two. The larger servos, obviously, use even more current, so this plan of using a small number of 18650's becomes infeasible.
There is not enough room on the robot for a 12 V car battery, and adding one would require recalculating the sizes of the servomotors that would be needed. Furthermore, I am not sure how to convert the car battery 12 V output down to 5 V input for the servomotors.
P.S. What about the stall current of the motors? Should the power supply be able to supply the stall current of all the motors it supplies (at the same time) or just the working current? Should I use a fuse to handle when (if?) the servomotors stall? Should I use a fuse or a circuit breaker? Do they make 5 V fuses? If so, where can I get one?
Something like a larger version of the 18650 box would be most preferable.
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