动态环境下基于时空占据栅格地图的分散式多智能体轨迹规划
Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Maps
AI总结：
本文提出一种基于时空占据栅格地图（SOGM）的分散式微型飞行器轨迹规划框架，通过扩展kinodynamic A*与走廊约束优化算法，有效解决了含任意形状动静态障碍物的避障问题，并在仿真与真实实验中验证了其优越性能。
摘要：
本文提出了一种分散式轨迹规划框架，用于解决包含静态和动态障碍物环境中多架微型飞行器（MAVs）的避障问题。该框架采用时空占据栅格地图（SOGM）作为环境表示，SOGM能够预测近未来邻近空间的占据状态。基于此表示，我们扩展了kinodynamic A*和走廊约束轨迹优化算法，以高效应对任意形状的静态与动态障碍物。通过共享规划轨迹并将其投影到SOGM上，实现了通信机器人间的避障集成。仿真结果表明，在具有不同数量和形状障碍物的动态环境中，该方法与最先进方法相比取得了颇具竞争力的性能。最后，所提方法在真实实验中得到了验证。
英文摘要：
This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of multiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of neighboring space in the near future, as the environment representation. Based on this representation, we extend the kinodynamic A* and the corridor-constrained trajectory optimization algorithms to efficiently tackle static and dynamic obstacles with arbitrary shapes. Collision avoidance between communicating robots is integrated by sharing planned trajectories and projecting them onto the SOGM. The simulation results show that our method achieves competitive performance against state-of-the-art methods in dynamic environments with different numbers and shapes of obstacles. Finally, the proposed method is validated in real experiments.
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