Tutorial: Vision-Based Formation Control with Differential-Drive Robots
Tutorial: Vision-Based Formation Control with Differential-Drive Robots
This tutorial introduces multi-robot formation control using differential-drive robots, an overhead camera, and Python. It covers hardware and software setup, robot pose estimation via ArUco markers, formation geometry, feedback control, system integration, and error evaluation. The guide emphasizes translating mathematical concepts into coordinated behaviors on physical robots and includes an open-source GitHub repository with Python implementations and demonstrations for hands-on learning.
The authors provide a practical implementation guide for vision-based formation control using differential-drive robots, ArUco markers for pose estimation, and Python code available in an open-source repository.
Source: arXiv Robotics — research abstracts · Read original article ↗
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education, we emphasize how mathematical concepts are translated into coordinated behaviors on physical robots. Although the implementation uses a specific robotic platform, the underlying perception and formation-control principles can be adapted to other differential-drive robots and simulation environments. The accompanying open-source GitHub repository provides the Python implementation, setup instructions, and
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Source：arXiv Robotics — research abstracts · arxiv.org