robotics-control

Robotics Toolbox for Python

Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.

A Tutorial on Manipulator Differential Kinematics

Nimble: Physics Engine for Biomechanics and Deep Learning

NAP Framework source code

Julia implementation of various rigid body dynamics and kinematics algorithms

ROS package used to create an endpoint to accept ROS messages sent from a Unity scene using the ROS TCP Connector scripts

OpenSHC: A Versatile Multilegged Robot Controller

A differentiable physics engine and multibody dynamics library for control and robot learning.

ROS, ROS2, real-time, control, pendulum

Trajectory optimization algorithms for robotic control.

Effortless building, testing, and deploying customized robot operating systems at scale.

北京化工大学创新创业项目：机器人. An AGV based on ROS and SLAM.

Simulation Of AUBO Collaborative Robot On ROS Step by Step

Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.

Robotics Guide

This project consists of research on a 6 degree of freedom robot. A 3D model of the robot has been made on Unity to demonstrate Forward and Inverse Kinematics. Important concepts in robotics like Singularities, Path planning and Motion Control are explained as well.

Robust control tutorial by Purdue SMART Lab: Sliding Mode Control (SMC) with MATLAB/Simulink example implementation

Blocks for accessing MuJoCo physics engine within Simulink

Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.