LIVEPOINT让多机器人在复杂环境里安全、不堵死地自主导航
LIVEPOINT: Fully Decentralized, Safe, Deadlock-Free Multi-Robot Control in Cluttered Environments with High-Dimensional Inputs
- 用点云合成通用安全约束，实现无中心化实时控制
- 零碰撞零堵死，挑战场景成功率100%优于基线方法
- 动态调速避免拥堵，兼顾安全与运动流畅性
在动态杂乱环境中实现完全去中心化、安全且无死锁的多机器人导航是机器人学中的关键挑战。现有方法依赖精确状态测量以保证安全与活跃性（如通过控制屏障函数CBFs），但难以直接从机载传感器（如激光雷达和摄像头）获取。本文提出LIVEPOINT，一种去中心化控制框架，通过点云合成通用CBFs，实现复杂动态环境中多机器人安全、无死锁的实时导航。此外，LIVEPOINT基于新颖的对称交互度量动态调整智能体速度，实现最小侵入性的死锁规避。我们在门道、交叉口等高度受限的多机器人场景中进行仿真验证。结果表明，相比基于优化的方法（如MPC、ORCA）和神经网络方法（如MPNet），LIVEPOINT在挑战性环境中实现零碰撞、零死锁，成功率达100%。尽管优先保障安全与活跃性，其在门道场景下仍比基线方法平滑35%，并在受限环境中保持敏捷性。
原文摘要 · Abstract (English)
Fully decentralized, safe, and deadlock-free multi-robot navigation in dynamic, cluttered environments is a critical challenge in robotics. Current methods require exact state measurements in order to enforce safety and liveness e.g. via control barrier functions (CBFs), which is challenging to achieve directly from onboard sensors like lidars and cameras. This work introduces LIVEPOINT, a decentralized control framework that synthesizes universal CBFs over point clouds to enable safe, deadlock-free real-time multi-robot navigation in dynamic, cluttered environments. Further, LIVEPOINT ensures minimally invasive deadlock avoidance behavior by dynamically adjusting agents' speeds based on a novel symmetric interaction metric. We validate our approach in simulation experiments across highly constrained multi-robot scenarios like doorways and intersections. Results demonstrate that LIVEPOINT achieves zero collisions or deadlocks and a 100% success rate in challenging settings compared to optimization-based baselines such as MPC and ORCA and neural methods such as MPNet, which fail in such environments. Despite prioritizing safety and liveness, LIVEPOINT is 35% smoother than baselines in the doorway environment, and maintains agility in constrained environments while still being safe and deadlock-free.
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