← 研踪 PaperTrailarXiv:2611.11164 · v1
Tutorial: Vision-Based Formation Control with Differential-Drive Robots
首次提交 2026-11-08 14:11
版本更新 2026-11-08 14:11
雷达观测 2026-11-10 12:16
摘要
This tutorial presents a step-by-step introduction to multi-robot formation control using differential- drive robots, an overhead camera, and Python. ArUco markers are used to estimate robot positions and orientations, while desired pairwise displacement vectors define a circular formation. By comparing measured and desired relative positions, the controller computes movement vectors that are converted into differential-drive wheel commands, with basic proximity-sensor-based obstacle avoidance. The tutorial guides readers through hardware and software setup, robot pose estimation, formation geometry, feedback control, system integration, and formation-error evaluation. Motivated by the need to bridge the gap between theoretical understanding and practical implementation in robotics 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 demonstrations to support hands-on learning and further exploration of multi-robot systems.
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v1 · 2026-11-08 14:11 · 最新观测版本
Tutorial: Vision-Based Formation Control with Differential-Drive Robots
This tutorial presents a step-by-step introduction to multi-robot formation control using differential- drive robots, an overhead camera, and Python. ArUco markers are used to estimate robot positions and orientations, while desired pairwise displacement vectors define a circular formation. By comparing measured and desired relative positions, the controller computes movement vectors that are converted into differential-drive wheel commands, with basic proximity-sensor-based obstacle avoidance. The tutorial guides readers through hardware and software setup, robot pose estimation, formation geometry, feedback control, system integration, and formation-error evaluation. Motivated by the need to bridge the gap between theoretical understanding and practical implementation in robotics 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 demonstrations to support hands-on learning and further exploration of multi-robot systems.
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