LiveNet：面向受限环境下安全且活跃导航的鲁棒、低侵入性多机器人控制方法
LiveNet: Robust, Minimally Invasive Multi-Robot Control for Safe and Live Navigation in Constrained Environments
AI总结：
针对受限拥挤环境中多机器人导航易碰撞死锁、现有方法无法兼顾安全与活跃性或侵入性高的问题，提出集成统一CBF公式的分布式低侵入性神经网络控制器LiveNet，仿真验证其在速度、侵入性和鲁棒性上均显著优于现有方法。
摘要：
在人口密集的真实环境中，机器人常会遭遇受限且杂乱的场景，比如穿过狭窄的门口、 hallway（走廊）以及走廊交叉口，这类场景中有限空间引发的冲突会导致机器人之间发生碰撞或陷入死锁。当前最先进的分布式优化方法与基于神经网络的方法存在两大局限：一是主要针对通用开放空间设计，二是策略过于保守，只能保证安全性或活跃性中的一项，无法同时兼顾二者。部分解决方案依赖集中式冲突消解机制，但其生成的轨迹侵入性极强，不具备实际部署的可行性。\n本文提出LiveNet——一种完全分布式的鲁棒神经网络控制器，可实现类人的避让与通行，在拥挤、易发生冲突的空间中实现灵活、非保守、无死锁且安全的导航。LiveNet属于低侵入性方案，无需智能体间通信，也不要求协作行为。其核心创新点是提出了可同时保障安全性与活跃性的统一CBF（控制障碍函数）公式，并将其集成到神经网络中以提升鲁棒性。\n我们在仿真环境中对LiveNet进行了评估，结果显示：通用的多机器人优化导航方法与基于学习的导航方法甚至无法抵达目标；专为这类环境设计的方法虽能成功完成任务，但速度慢10-20倍，侵入性高4-5倍，且对场景配置的变化（例如起始状态与目标状态的改变等）鲁棒性差得多。我们已将LiveNet的代码开源，地址为https://github.com/srikarg89/LiveNet。
英文摘要：
Robots in densely populated real-world environments frequently encounter constrained and cluttered situations such as passing through narrow doorways, hallways, and corridor intersections, where conflicts over limited space result in collisions or deadlocks among the robots. Current decentralized state-of-the-art optimization- and neural network-based approaches (i) are predominantly designed for general open spaces, and (ii) are overly conservative, either guaranteeing safety, or liveness, but not both. While some solutions rely on centralized conflict resolution, their highly invasive trajectories make them impractical for real-world deployment. This paper introduces LiveNet, a fully decentralized and robust neural network controller that enables human-like yielding and passing, resulting in agile, non-conservative, deadlock-free, and safe, navigation in congested, conflict-prone spaces. LiveNet is minimally invasive, without requiring inter-agent communication or cooperative behavior. The key insight behind LiveNet is a unified CBF formulation for simultaneous safety and liveness, which we integrate within a neural network for robustness. We evaluated LiveNet in simulation and found that general multi-robot optimization- and learning-based navigation methods fail to even reach the goal, and while methods designed specially for such environments do succeed, they are 10-20 times slower, 4-5 times more invasive, and much less robust to variations in the scenario configuration such as changes in the start states and goal states, among others. We open-source the LiveNet code at https://github.com/srikarg89/LiveNet{https://github.com/srikarg89/LiveNet.
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。
