解决复杂环境中多机器人实时安全导航难题
SPF-EMPC Planner: A real-time multi-robot trajectory planner for complex environments with uncertainties
- 用安全概率场建模动态障碍物不确定性，结合优化生成安全轨迹
- 扩展状态模型预测控制使机器人精准跟踪轨迹，成功率是现有方法4倍
- 适合需要实时多机器人协同的智能物流、搜救等场景
在实际应用中，障碍物的不可预测运动和机器人状态观测不准确带来了显著不确定性，尤其在集群环境下。现有方法难以同时应对不确定性、复杂环境结构和机器人集群带来的挑战。本文提出一种带安全概率场的扩展状态模型预测控制规划器（SPF-EMPC），以解决复杂、动态且不确定环境下的多机器人导航问题。首先，安全概率场创新性地建模外部动态障碍物的不确定性，并与无约束优化方法结合，实现多机器人在线生成安全轨迹。随后，扩展状态模型预测控制器能精确跟踪这些轨迹，同时考虑机器人自身模型约束和状态不确定性，确保规划轨迹的可行性。仿真结果显示，该方法的成功率是当前最优算法的四倍；物理实验验证了其具备实时运行能力，可在不确定环境中实现多机器人安全导航。
原文摘要 · Abstract (English)
In practical applications, the unpredictable movement of obstacles and the imprecise state observation of robots introduce significant uncertainties for the swarm of robots, especially in cluster environments. However, existing methods are difficult to realize safe navigation, considering uncertainties, complex environmental structures, and robot swarms. This paper introduces an extended state model predictive control planner with a safe probability field to address the multi-robot navigation problem in complex, dynamic, and uncertain environments. Initially, the safe probability field offers an innovative approach to model the uncertainty of external dynamic obstacles, combining it with an unconstrained optimization method to generate safe trajectories for multi-robot online. Subsequently, the extended state model predictive controller can accurately track these generated trajectories while considering the robots' inherent model constraints and state uncertainty, thus ensuring the practical feasibility of the planned trajectories. Simulation experiments show a success rate four times higher than that of state-of-the-art algorithms. Physical experiments demonstrate the method's ability to operate in real-time, enabling safe navigation for multi-robot in uncertain environments.
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