YouBot Control demos on V-REP platform.

🤖 A motion planning MATLAB & V-rep implementation for the KUKA LBR iiwa robotic arm, performing null-space reconfiguration for obstacle avoidance.

How-to on simulating a robot with V-REP and controlling it with ROS

Reinforcement Learning framework for Robotics

TUM Master’s thesis: Steering a robot with an event-based vision sensor in a lane-keeping task using methods such as Deep Reinforcement Learning or Spiking Neural Networks.

ROS and V-REP for Robot Mapping and Localization

Open-AI Gym extension for robotics based on V-REP

This repository contains the code to experience CoppeliaSim in VR

Simple for use Python binding for Coppelia Robotics V-REP simulator (remote API)

Medical Robotic - NDI Polaris Vega

build a reinforcement environment like openai gym based on V-rep for a dual-arm robot

This contain a drone simulated in V-REP (CoppeliaSim)

ABB 140 Robot Draws a Given Picture

No description provided.

Universal Robot 10 in V-REP for picking and placing bottles

V-REP Quadcopter Test Codes

Simulation of autonomous car with CoppeliaSim Robot Simulator and Python.

Simulation and control of Raven 2 surgical robot in V-REP

Robot Navigation using the Attractor Dynamics Approach: V-REP Simulation

This project uses Improved Grey Wolf Optimizer (IGWO) and Improved Particle Swarm Optimization (IPSO) for robot path planning with Laser Range Finder (LRF) data reduction in CoppeliaSim (V-REP). Robots autonomously navigate unknown environments and avoid collisions using IGWO/IPSO.

Obstacle Avoidance Bot training on V-rep using Q learning Algorithm. Neural Networks were used as function approximator for state space

Control the robot in CoppeliaSim (V-REP) with C#

V-REP plugin that publishes a full revolution of PointCloud2 point into ROS

C artificial intelligence that guides an autonomous car through a virtual racetrack.

V-REP C++ for vision tasks

ENPM662 (Introduction to Robot Modeling) - Project

Quadrotors Volleyball Simulation With V-REP

Robotic Arm picking and placing was a group project. Group members where Xinjang Yang; Anshul Sungra and Haonan Peng. Simulation Video can be found here: https://www.youtube.com/watch?v=65FFMoTsIAQ

University group project concerning the use of an optimal motion planning algorithm to move a mobile that is assigned a navigation task. The optimal motion planning algorithm chosen is the anytime motion planning based on the RRT*, which is a sampling-based algorithm with an asymptotic optimality property. The simulation environment is V-rep.

ETHZ Autonomous Mobile Robot VREP Exercises