The project is an open-source and low-cost kit to get started with underactuated robotics.

Overview

Torque Limited Simple Pendulum

Introduction

The project is an open-source and low-cost kit to get started with underactuated robotics. The kit targets lowering the entry barrier for studying underactuation in real systems which is often overlooked in conventional robotics courses. It implements a torque-limited simple pendulum built using a quasi-direct drive motor which allows for a low friction, torque limited setup. This project describes the offline and online control methods which can be studied using the kit, lists its components, discusses best practices for implementation, presents results from experiments with the simulator and the real system. This repository describes the hardware (CAD, Bill Of Materials (BOM) etc.) required to build the physical system and provides the software (URDF models, simulation and controller) to control it.

See a video the simple pendulum in action:

IMAGE ALT TEXT HERE

Documentation

The hardware setup and the motor configuration are described in their respective readme files. The dynamics of the pendulum are explained here.

In order to work with this repository you can get started here and read the usage instructions here for a description of how to use this repository on a real system. The instructions for testing the code can be found here.

Overview of Methods

Trajectory Optimization tries to find a trajectory of control inputs and states that is feasible for the system while minimizing a cost function. The cost function can for example include terms which drive the system to a desired goal state and penalize the usage of high torques. The following trajectory optimization algorithms are implemented:

Trajectory Following controllers act on a precomputed trajectory and ensure that the system follows the trajectory properly. As the PID and the tvLQR controller react to the actual state of the pendulum they can also be understood as closed loop controllers. The trajectory following controllers implemented in this project are:

Closed Loop or feedback controllers take the state of the system as input and ouput a control signal. Because they are able to react to the current state, they can cope with perturbations during the execution. The following feedback controllers are implemented:

  • Gravity Compensation: A controller compensating the gravitational force acting on the pendulum. The pendulum can be moved as if it was in zero-g.
  • Energy Shaping: A controller regulating the energy of the pendulum. Drives the pendulum towards a desired energy level.
  • Linear Quadratic Regulator (LQR): Linearizes the dynamics around a fixed point and drives the pendulum towards the fixpoint with a quadratic cost function. Only useable in a state space region around the fixpoint.
  • Model predictive control with iLQR: A controller which performs an iLQR optimization at every timestep and executes the first control signal of the computed optimal trajectory.

Reinforcement Learning (RL) can be used to learn a policy on the state space of the robot. The policy, which has to be trained beforehand, receives a state and outputs a control signal like a feedback controller. The simple pendulum is can be formulated as a RL problem with two continuous inputs and one continuous output. Similar to the cost function in trajectory optimization, the policy is trained with a reward function. The controllers acting on the policies are closed loop controllers. The following RL algorithms are implemented:

The implementations of direct collocation and TVLQR make use of drake, iLQR only makes use of the symbolic library of drake, FDDP makes use of Crocoddyl, SAC uses the stable-baselines3 implementation and DDPG is implemented in tensorflow. The other methods use only standard libraries.

The controllers can be benchmarked in simulation with a set of predefined criteria.

Authors

Feel free to contact us if you have questions about the test bench. Enjoy!

Contributing

  1. Fork it (https://github.com/yourname/yourproject/fork)
  2. Create your feature branch (git checkout -b feature/fooBar)
  3. Commit your changes (git commit -am 'Add some fooBar')
  4. Push to the branch (git push origin feature/fooBar)
  5. Create a new Pull Request

See Contributing for more details.

Safety Notes

When working with a real system be careful and mind the following safety measures:

  • Brushless motors can be very powerful, moving with tremendous force and speed. Always limit the range of motion, power, force and speed using configurable parameters, current limited supplies, and mechanical design.

  • Stay away from the plane in which pendulum is swinging. It is recommended to have a safety net surrounding the pendulum in case the pendulum flies away.

  • Make sure you have access to emergency stop while doing experiments. Be extra careful while operating in pure torque control loop.

Acknowledgements

This work has been performed in the VeryHuman project funded by the German Aerospace Center (DLR) with federal funds (Grant Number: FKZ 01IW20004) from the Federal Ministry of Education and Research (BMBF) and is additionally supported with project funds from the federal state of Bremen for setting up the Underactuated Robotics Lab (Grant Number: 201-001-10-3/2021-3-2).

License

This work has been released under the BSD 3-Clause License. Details and terms of use are specified in the LICENSE file within this repository. Note that we do not publish third-party software, hence software packages from other developers are released under their very own terms and conditions, e.g. Stable baselines (MIT License) and Tensorflow (Apache License v2.0). If you install third-party software packages along with this repo ensure that you follow each individual license agreement.


Comments
  • not loading pydrake.symbolic

    not loading pydrake.symbolic

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/software/python/simple_pendulum/trajectory_optimization/ilqr/ilqr.py#L4

    I have installed Drake, and I can import the main module with

    python -c 'import pydrake; print(pydrake.__file__)'
    

    However, when I run the benchmark_controller.py I get

    ModuleNotFoundError: No module named 'pydrake.symbolic'
    

    from the line referenced above. Was this usage deprecated or something?

    If there's an easier way to run some simulation tests, please advise.

    opened by ricopicone 4
  • Is ROS being used in the project?

    Is ROS being used in the project?

    I saw the mention of URDF files but couldnt find a trace of ROS anywhere. If ROS is not used where for example in the project are these URDF files used? I wanted to implment this project but only in simulation. Is this possible without having a physical setup?

    opened by Robotgir 3
  • "J" and "m*cx" are inaccurate in system_identification.py

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/software/python/simple_pendulum/model/system_identification.py#L87-L88

    Hi, J and m*cx may not be accurate in dynamics. It would be better to change to I (Interia) and m respectively.

    opened by Jarvis861 2
  • missing packages encountered when running pytest

    missing packages encountered when running pytest

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/docs/code_testing.md?plain=1#L12

    With a fresh installation, I get

    ModuleNotFoundError: No module named 'eigenpy'
    

    and

    ModuleNotFoundError: No module named 'pydrake'
    

    I didn't install Drake, so the latter may be expected. It looks like EigenPy can't be installed with pip either.

    After installing EigenPy, I'm also getting

    ModuleNotFoundError: No module named 'crocoddyl'
    

    I tried to pip which their site says should work, but no packages were found.

    opened by ricopicone 2
  • setup github actions workflow for continuous integration

    setup github actions workflow for continuous integration

    We should setup a CI/CD pipeline via github so that we can ensure that the software package is always installable on a clean system and the unit testing works.

    You need to include a yml file in order to do this. An example to set this up on github was pointed to me via Alexander Fabisch here: https://github.com/rock-learning/pytransform3d/blob/master/.github/workflows/python-package.yml

    Some further help on this can be found here:

    1. Building and testing with python: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
    2. Supported runners and hardware resources: https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources
    3. Getting started with actions: https://docs.github.com/en/actions
    4. Installing dependency software on Ubuntu runners: https://docs.github.com/en/actions/using-github-hosted-runners/customizing-github-hosted-runners#installing-software-on-ubuntu-runners
    opened by shivesh1210 1
  • Title cases in paper.bib

    Title cases in paper.bib

    For your entries in paper.bib, please for this guideline: https://pandoc.org/MANUAL.html#citations.

    In particular, the titles should be capitalized in title case.

    opened by jingnanshi 1
  • paper comments

    paper comments

    Thank you for open sourcing this library. Here are my comments regarding the submitted version of the paper:

    p2: It will be aesthetically more pleasing for the fonts of the variables to be consistent with the equation below. Also mention that the symbols are defined in the equation will be helpful.

    p3: Number the equation and reference it in Figure 1.

    p2: CubeMars_AK_V1.1: is this the actual model number of the controller board? If so, provide the reference to the manual if possible.

    p7: Figure 5: While the criteria have been defined on the previous page, the definitions of how percentages are calculated are not entirely clear. Maybe provide some explanations.

    Let me know if you have more questions. Thank you.

    opened by jingnanshi 1
  • length to CoM 0.045 is wrong

    length to CoM 0.045 is wrong

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/hardware/testbench_description.md?plain=1#L20

    Hi, the length to CoM here is 0.45m instead of 0.045m.

    opened by Jarvis861 1
  • wrong

    wrong "B" matrix in lqr_controller.py

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/software/python/simple_pendulum/controllers/lqr/lqr_controller.py#L39

    Hi, "B" matrix is probably wrong. It should be self.B = np.array([[0, 1/self.m/self.len**2.0]]).T. But this small issue does not affect the final result too much as shown below.

    • True 3 1 lqr_true

    • False 3 2 lqr_false

    Finally, thanks for your sharing! This project does greatly enhance my understanding of control methods.

    opened by Jarvis861 1
  • cannot be run as root

    cannot be run as root

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/docs/installation_guide.md?plain=1#L243

    The script returns an error that it can't be run as root. Remove sudo from this line.

    opened by ricopicone 1
  • encoder

    encoder

    https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum/blob/222c610b6ce8e684f7141c1eeddbeb8f85f45b65/hardware/testbench_description.md?plain=1#L25

    Include at least a mention that there's an encoder in this for feedback

    opened by ricopicone 1
  • Fixed Control law error in LQRController

    Fixed Control law error in LQRController

    Bugs:

    • The control law used was $u = -K y$, while instead it should've been $u = - K \Delta y$.

    Changes Made:

    • __init__ takes moment_of_inertia as a parameter for added functionality
    • The function set_goal previously had no functionality, now it sets the goal of the controller and recomputes $A$, $K$, and $S$ matrices
    • The control law is changed to $u = -K (y - y_\text{goal})$

    Testing:

    • I tested these changes in our TMotors setup, and they are functional
    opened by Haricharan1212 3
Releases(v1.0.0)
Python information display framework aimed at e-ink devices

My display, using a Raspberry Pi Zero W and Waveshare 6" e-paper hat infodisplay Modular information display framework aimed at e-ink devices. Built u

Niek Blankers 3 Apr 08, 2022
智能无人机路径规划仿真系统是一个具有操作控制精细、平台整合性强、全方向模型建立与应用自动化特点的软件

Drone智能无人机路径规划仿真系统是一个具有操作控制精细、平台整合性强、全方向模型建立与应用自动化特点的软件。它以A、B两国在C区开展无人机战争为背景,该系统的核心功能是通过仿真平台规划无人机航线,并进行验证输出,数据可导入真实无人机,使其按照规定路线精准抵达战场任一位置,支持多人多设备编队联合行动。

wwy 349 Jan 03, 2023
Simple python3 implementation of microKanren with lots of type annotations for clarity

MicroKanren-py This is (yet another) python implementation of microKanren. It's a reasonably 1:1 translation of the code provided in the paper, but ev

Erik Derohanian 3 Dec 10, 2022
Automatic CPU speed & power optimizer for Linux

Automatic CPU speed & power optimizer for Linux based on active monitoring of laptop's battery state, CPU usage, CPU temperature and system load. Ultimately allowing you to improve battery life witho

Adnan Hodzic 3.4k Jan 07, 2023
Claussoft Personal Digital Assistant

Claussoft Personal Digital Assistant Install on Linux $ sudo apt update $ sudo apt install espeak ffmpeg libespeak1 portaudio19-dev $ pip install -r r

Christian Clauss 3 Dec 14, 2022
Implementation of Forwards Kinematics, Inverse Kinematics, Point to Point Movement and Synchronous movement for Kuka KR 120 R2700-2.

I made this project for my university course in robotics. I rarely found any information regarding the implementation of mathematics in code. So I decided to make this repo in order to help others :)

2 Dec 27, 2022
A python project based on a TV show Wheel of Fortune

Wheel-of-Fortune-using-Python Wheel of Fortune in python this game is the hands-on project in Python 3 Programming Specialization offered By Universit

Eszter Pai 1 Jan 03, 2022
Hardware: CTWingSKIT_BC28 Development Toolkit

IoT Portal Monitor Tools hardware: CTWingSKIT_BC28 Development Toolkit serial port driver: ST-LINK hardware development environment: Keli 5 MDK IoT pl

Fengming Zhang 1 Nov 07, 2021
Home assiatant Custom component: Camera Archiver

Camera archiver Archive your ftp camera meadia files on other ftp with files renaming and event creation. Event can be used for send information to el

1 Jan 06, 2022
Hourglass on the pi pico using circuitpython

hourglass-on-pico "Hourglass" on the raspberry pi pico using circuitpython circuitpython version 7.0.0 Components used: Raspberry Pi Pico ADXL345 acce

4 Jul 18, 2022
Minimal and clean dashboard to visualize some stats of Pi-Hole with an E-Ink display attached to your Raspberry Pi

Clean Dashboard for Pi-Hole Minimal and clean dashboard to visualize some stats of Pi-Hole with an E-Ink display attached to your Raspberry Pi.

Alessio Santoru 104 Dec 14, 2022
How to configure IOMMU device for nested Proxmox hypervisor (PVE) VM - PCIe Passthrough

Configuring PCIe Passthrough for Nested Virtualization on Proxmox Summary: If you are running bare-metal L0 (level 0) Proxmox (PVE) hypervisor with ne

Travis Johnson 6 Aug 30, 2022
An arduino/ESP project that can play back G-Force data previously recorded

An arduino/ESP project that can play back G-Force data previously recorded

7 Apr 12, 2022
Final-project-robokeeper created by GitHub Classroom

RoboKeeper! Jonny Bosnich, Joshua Cho, Lio Liang, Marco Morales, Cody Nichoson Demonstration Videos Grabbing the paddle: https://youtu.be/N0HPvFNHrTw

Cody Nichoson 1 Dec 12, 2021
A ESP32 project template with a web interface built in React

ESP AP Webserver demo.mp4 This is my experiment with "mobile app development" for the ESP32. The project consists of two parts, the ESP32 code and the

8 Dec 15, 2022
Home Assistant component to handle key atom

KeyAtome Home Assistant component to handle key atom, a Linky-compatible device made by Total/Direct-Energie. Installation Either use HACS (default),

18 Dec 21, 2022
LifeSaver automatically, periodically saves USB flash drive data into the PC

LifeSaver automatically, periodically saves USB flash drive data into the PC. Theoriticaly it will work with any any connected drive ex - Hard Disk ,SSD ... But, can't handle Backing up multipatition

siddharth dhaka 4 Sep 26, 2021
Sleep Functionality for Adafruit MacroPad RP2040

Adafruit-MacroPad-RP2040 Sleep Functionality for Adafruit MacroPad RP2040 Details This is a modification of AdaFruit project bundle found here specifi

9 Dec 18, 2022
This tool emulates an EMV-CAP device, to illustrate the article "Banque en ligne : à la decouverte d'EMV-CAP" published in MISC

About This tool emulates an EMV-CAP device, to illustrate the article "Banque en ligne : à la decouverte d'EMV-CAP" published in MISC, issue #56 and f

Philippe Teuwen 28 Nov 21, 2022
A Home Assistant integration for Solaredge inverters

A Home Assistant integration for Solaredge inverters. Supports multiple inverters chained through RS485.

Seth 50 Dec 23, 2022