PuSHR：一个多机器人非线性重排系统
PuSHR: A Multirobot System for Nonprehensile Rearrangement
摘要：
我们专注于重新安排一组对象与一队的汽车一样的机器人推使用现成的组件的问题。保持对被推物体的控制，同时避免在狭小空间中发生碰撞，需要高度协调的运动，这在受限的硬件上执行是具有挑战性的。集中式重新规划方法即使对于小规模的问题也变得棘手，而分散式方法经常陷入僵局。我们的关键见解是，通过仔细分配推任务给机器人，我们可以降低重排任务的复杂性，通过可扩展的分散控制实现鲁棒性能。基于这一见解，我们构建了PuSHR，这是一个离线优化分配机器人推送任务和轨迹的系统，并通过在线分散控制执行轨迹跟踪。通过模拟中的消融研究，我们证明了PuSHR在最多4个机器人的挑战性任务的成功率和时间效率方面占据了从纯粹分散到完全分散的基线。硬件实验证明了我们的系统转移到现实世界中，并强调其鲁棒性模型不准确。我们的代码可以在https：//github.com/prl-mushr/pushr上找到，我们的实验视频可以在https://youtu.be/DIWmZerF_O8上找到。
英文摘要：
We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intractable even for small-sized problems whereas decentralized approaches often get stuck in deadlocks. Our key insight is that by carefully assigning pushing tasks to robots, we could reduce the complexity of the rearrangement task, enabling robust performance via scalable decentralized control. Based on this insight, we built PuSHR, a system that optimally assigns pushing tasks and trajectories to robots offline, and performs trajectory tracking via decentralized control online. Through an ablation study in simulation, we demonstrate that PuSHR dominates baselines ranging from purely decentralized to fully decentralized in terms of success rate and time efficiency across challenging tasks with up to 4 robots. Hardware experiments demonstrate the transfer of our system to the real world and highlight its robustness to model inaccuracies. Our code can be found at https://github.com/prl-mushr/pushr, and videos from our experiments at https://youtu.be/DIWmZerF_O8.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
