Multi-Purpose-MPC | Purpose MPC for Reference Path Tracking | Robotics library
kandi X-RAY | Multi-Purpose-MPC Summary
kandi X-RAY | Multi-Purpose-MPC Summary
In this repository you find an implementation of a multi-purpose Model Predictive Controller. The controller was implemented as a contribution to the Automatic Control Project Course (EL2425) at KTH Royal Institute of Technology, Stockholm. The developed algorithm was tested on a 1:10 RC car provided by KTH Smart Mobility Lab. The test scenarios comprised the following three tasks:. The controller is implemented in a way that enables its applicability to all three tasks by merely tuning the weight matrices of the underlying optimization problem. The illustration below shows the obstacle avoidance task in simulation. The rest of this readme is structured as follows. In Section 2 we will present an overview of the entire system and discuss all fundamental components of the implementation in detail. In Section 3 we will provide guidelines for using the implementation in simulation and practice. Section 4 will be dedicated to analyzing limitations of the current version of the controller and outline potential extensions of the implementation.
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Top functions reviewed by kandi - BETA
- Updates constraints for a given waypoint
- Compute free segments of waypoint
- Convert from pixel coordinates to pixel coordinates
- Convert from coordinates to pixel coordinates
- Plot the map
- Searches for sensors within a given map
- Compute speed profile
- Get the control signal of the model
- Convert to TemporalState
- Update the predicted states based on the given spatial state
- Compute the waypoint of the reference path
- Constructs a waypoint from waypoints
- Constructs the local curvature of the waypoint
- Compute the width of the waypoint
- Compute the minimum width of a waypoint
- Visualize the car
- Calculate the velocity of a given signal
- Add obstacle objects to map
- Compute temporal derivative of the temporal derivative
- Compute the derivative of the temporal derivative at a given state
- Add a list of boundaries
- Plot the beam beam
- Show the current prediction
Multi-Purpose-MPC Key Features
Multi-Purpose-MPC Examples and Code Snippets
Community Discussions
Trending Discussions on Robotics
QUESTION
I have imported a urdf model from Solidworks using SW2URDF plugin. The model loads correctly on Gazebo but looks weird on RVIZ, even while trying to teleoperate the robot, the revolute joint of the manipulator moves instead of the wheels. Is there anyone who has faced this issue before or has a solution to it? Here is how it looks on Gazebo
Here is the URDF file of the Model:
...ANSWER
Answered 2022-Mar-22 at 13:41So, I realized two problems:
First, you have to change the fixed frame in the global options of RViz to world or provide a transformation between map and world.
Second, your URDF seems broken. There is something wrong with your revolute-typed joints. Changing their type to fixed fixed the problem. I think, it's best if you ask a separate question with a minimal example regarding this second problem.
QUESTION
This question is related to my final project. In gazebo simulation environment, I am trying to detect obstacles' colors and calculate the distance between robot and obstacles. I am currently identifying their colors with the help of OpenCV methods (object with boundary box) but I don't know how can i calculate their distances between robot. I have my robot's position. I will not use stereo. I know the size of the obstacles. Waiting for your suggestions and ideas. Thank you!
My robot's topics :
- cameras/camera/camera_info (Type: sensor_msgs/CameraInfo)
- cameras/camera/image_raw (Type: sensor_msgs/Image)
- sensors/lidars/points (Type: sensor_msgs/PointCloud2)
ANSWER
Answered 2022-Feb-24 at 23:23You can project the point cloud into image space, e.g., with OpenCV (as in here). That way, you can filter all points that are within the bounding box in the image space. Of course, projection errors because of differences between both sensors need to be addressed, e.g., by removing the lower and upper quartile of points regarding the distance to the LiDAR sensor. You can use the remaining points to estimate the distance, eventually.
We have such a system running and it works just fine.
QUESTION
I’m a college student and I’m trying to build an underwater robot with my team.
We plan to use stm32 and RPi. We will put our controller on stm32 and high-level algorithm (like path planning, object detection…) on Rpi. The reason we design it this way is that the controller needs to be calculated fast and high-level algorithms need more overhead.
But later I found out there is tons of package on ROS that support IMU and other attitude sensors. Therefore, I assume many people might build their controller on a board that can run ROS such as RPi.
As far as I know, RPi is slower than stm32 and has less port to connect to sensor and motor which makes me think that Rpi is not a desired place to run a controller.
So I’m wondering if I design it all wrong?
...ANSWER
Answered 2022-Feb-12 at 15:18Robot application could vary so much, the suitable structure shall be very much according to use case, so it is difficult to have a standard answer, I just share my thoughts for your reference.
In general, I think Linux SBC(e.g. RPi) + MCU Controller(e.g. stm32/esp32) is a good solution for many use cases. I personally use RPi + ESP32 for a few robot designs, the reason is,
- Linux is not a good realtime OS, MCU is good at handling time critical tasks, like motor control, IMU filtering;
- Some protection mechnism need to be reliable even when central "brain" hang or whole system running into low voltage;
- MCU is cheaper, smaller and flexible to distribute to any parts inside robot, it also helps our modularized design thinking;
- Many new MCU is actually powerful enough to handle sophisticated tasks and could offload a lot from the central CPU;
QUESTION
In a formation robots are linked with eachother,number of robots in a neighbourhood may vary. If one robot have 5 neighbours how can I find the angle of that one robot with its other neighbour?
...ANSWER
Answered 2021-Dec-15 at 10:03(Following a comment, I replaced the sequence of <face + read heading> with just using towards, wich I had overlooked as an option. For some reason the comment I am referring to has been deleted quickly so I don't know who gave the suggestion, but I read enough of it from the cell notification)
In NetLogo it is often possible to use turtles' heading to know degrees.
Since your agents are linked, a first thought could be to use link-heading, which directly reports the heading in degrees from end1 to end2.
However note that this might not be ideal: using link-heading will work spotlessly only if you are interested in knowing the heading from end1 to end2, which means:
- If your links are directed, it reports the heading from the source to the target;
- If your links are undirected, it reports the heading from the older turtle to the younger turtle.
If that's something that you are interested in, fine. But it might not be so! For example, if you have undirected links and are interested in knowing the angle from turtle 1 to turtle 0, using link-heading will give you the wrong value:
QUESTION
Overlapping targetless stereo camera calibration can be done using feautre matchers in OpenCV and then using the 8-point or 5-point algoriths to estimate the Fundamental/Essential matrix and then use those to further decompose the Rotation and Translation matrices.
How to approach a non-overlapping stereo setup without a target?
Can we use visual odometry (like ORB SLAM) to calculate trajectory of both the cameras (cameras would be rigidly fixed) and then use hand-eye calibration to get the extrinsics? If yes, how can the transformations of each trajectory mapped to the gripper->base transformation and target->camera transformation? Or is there another way to apply this algorithm?
If hand-eye calibration cannot be used, is there any recommendations to achieve targetless non-overlapping stereo camera calibration?
...ANSWER
Answered 2021-Dec-08 at 03:13Hand-eye calibration is enough for your case. Just get the trajectory from each camera by running ORBSLAM. Then, calculate the relative trajectory poses on each trajectory and get extrinsic by SVD. You might need to read some papers to see how to implement this.
This is sometimes called motion-based calibration.
QUESTION
As a premise I must say I am very inexperienced with ROS.
I am trying to publish several ros messages but for every publish that I make I get the "publishing and latching message for 3.0 seconds", which looks like it is blocking for 3 seconds.
I'll leave you with an example of how I am publishing one single message:
...ANSWER
Answered 2021-Nov-29 at 18:44Part of the issue is that rostopic CLI tools are really meant to be helpers for debugging/testing. It has certain limitations that you're seeing now. Unfortunately, you cannot remove that latching for 3 seconds message, even for 1-shot publications. Instead this is a job for an actual ROS node. It can be done in a couple of lines of Python like so:
QUESTION
A c++ novice here! The verbose in the terminal output says the problem is solved successfully, but I am not able to access the solution. What is the problem with the last line?
...ANSWER
Answered 2021-Nov-20 at 02:41You will need to change the line
QUESTION
I'm programming a robot's controller logic. On the controller there is 2 buttons. There is 3 different actions tied to 2 buttons, one occurs when only the first button is being pushed, the second when only the second is pushed, and the third when both are being pushed.
Normally when the user means to hit both buttons they would hit one after another. This has the consequence of executing a incorrect action.
Here is part of the code.
...ANSWER
Answered 2021-Oct-22 at 16:58You could use a short timer, which is restarted every time a button press is triggered. Every time the timer expires, you check all currently pressed buttons. Of course, you will need to select a good timer duration to make it possible to press two buttons "simultaneously" while keeping your application feel responsive.
You can implement a simple timer using a counter in your loop. However, at some point you will be happier with an event based architecture.
QUESTION
I'm trying to put together a programmed robot that can navigate the room by reading instructions off signs (such as bathroom-right). I'm using the AlphaBot2 kit and an RPI 3B+.
the image processing part works well, but for some reason, the MOTION CONTROL doesn't work. I wrote a simple PID controller that "feeds" the motor, but as soon as motors start turning, the robot turns off.
...ANSWER
Answered 2021-Oct-03 at 14:33It is probably not the software. Your power supply is not sufficient or stable enough to power your motors and the Raspberry Pi. It is a very common problem. Either:
- Use separate power supplies which is recommended
- Or Increase your main power supply and use some short of stabilization of power
What power supply and power configuration are you using?
QUESTION
I have read multiple resources that say the InverseKinematics class of Drake toolbox is able to solve IK in two fashions: Single-shot IK and IK trajectory optimization using cubic polynomial trajectories. (Link1 Section 4.1, Link2 Section II.B and II.C)
I have already implemented the single-shot IK for a single instant as shown below and is working, Now how do I go about doing it for a whole trajectory using dircol or something? Any documentation to refer to?
ANSWER
Answered 2021-Oct-16 at 18:09The IK cubic-polynomial is in an outdated version of Drake. You can check out https://github.com/RobotLocomotion/drake/releases/tag/last_sha_with_original_matlab. In the folder drake/matlab/systems/plants@RigidBodyManipulator/inverseKinTraj.m
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Install Multi-Purpose-MPC
You can use Multi-Purpose-MPC like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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