Becker Marvin, Caspers Philipp, Lilge Torsten, Haddadin Sami, Müller Matthias A
Institute of Automatic Control, Leibniz University Hannover, Hannover, Germany.
Munich Institute of Robotics and Machine Intelligence, Technische Universität München (TUM), Munich, Germany.
Front Robot AI. 2025 Jan 3;11:1447351. doi: 10.3389/frobt.2024.1447351. eCollection 2024.
In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al. (2021) to incorporate this global information, simulating multiple robot trajectories with varying parameter sets to enhance avoidance strategies. Consequently, the proposed unified robotic motion planning framework seamlessly combines global trajectory planning with local reactive control and ensures comprehensive obstacle avoidance for the entire body of a robotic manipulator. The efficacy of the proposed approach is demonstrated through rigorous testing in over 4,000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework's performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot's ability to promptly adapt its motion plan and effectively avoid unpredictable movements by humans within its workspace. Overall, our contributions offer a robust and versatile solution for global reactive motion planning in dynamic environments.
在本文中，我们提出了一个全局反应式运动规划框架，该框架专为在复杂动态环境中导航的机器人操纵器而设计。利用无局部极小值的圆形场，我们的方法生成反应式控制命令，同时还利用来自任意配置空间运动规划器的全局环境信息来识别围绕障碍物的有前途的轨迹。此外，我们扩展了Becker等人（2021年）引入的虚拟代理框架，以纳入此全局信息，模拟具有不同参数集的多个机器人轨迹，以增强避障策略。因此，所提出的统一机器人运动规划框架无缝地将全局轨迹规划与局部反应式控制相结合，并确保对机器人操纵器的整个机身进行全面避障。通过在4000多个模拟场景中进行严格测试，证明了所提方法的有效性，该方法始终优于现有的运动规划器。此外，我们使用具有视觉反馈的协作式Franka Emika机器人在实际实验中验证了我们框架的性能。我们的实验说明了机器人能够迅速调整其运动计划，并有效避免其工作空间内人类的不可预测运动。总体而言，我们的贡献为动态环境中的全局反应式运动规划提供了一个强大且通用的解决方案。
