多智能体机器人在复杂环境中实现稳定编队与避障的分布式规划方法
Multi-Agent Feedback Motion Planning using Probably Approximately Correct Nonlinear Model Predictive Control
- 基于概率近似正确的非线性模型预测控制，融合动态与感知不确定性建模
- 在高测量噪声下性能优于集中式方案，且可扩展至复杂动力系统
- 适合需要鲁棒协同的多机器人任务，如搜救、物流配送
多机器人团队在许多任务中能提供更高的效率、鲁棒性和弹性。然而，在真实场景中实现多机器人协作面临诸多挑战，尤其当动态机器人需在随机动力学和传感器不确定性下平衡编队控制与障碍物避让等冲突目标时。本文提出一种基于概率近似正确非线性模型预测控制（PAC-NMPC）的分布式、多智能体滚动时域反馈运动规划方法，能够同时处理模型与测量不确定性，实现鲁棒的多智能体编队控制，同时在杂乱障碍物环境中导航并避免机器人间碰撞。该方法不仅依赖于底层PAC-NMPC算法，还引入基于陀螺仪障碍物避让的终端代价函数。通过数值仿真验证，所提分布式方法性能与集中式方案相当，在显著测量噪声下表现更优，且可扩展至更复杂的动力系统。
原文摘要 · Abstract (English)
For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems.
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