Abstract
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.
Abstract (translated)
本文提出了一种分散式轨迹规划框架,用于解决具有静态和动态障碍物的环境中多个微型无人飞行器(MAVs)的碰撞避免问题。该框架利用了静态和动态占用网格图(SOGM),将预测周围空间邻居的占用状态作为环境表示。基于此表示,我们将动量惯性算法(Kinodynamic A*)和约束跟踪优化算法(Corridor-Constrained Trajectory Optimization)扩展到能够有效处理具有任意形状的静态和动态障碍物。通过共享计划轨迹并将其投影到SOGM,将碰撞避免集成到通信机器人之间。仿真结果表明,与其他方法相比,我们的方法在具有不同数量和形状的障碍物的动态环境中实现了竞争性的性能。最后,所提出的技术在实际实验中得到了验证。
URL
https://arxiv.org/abs/2404.15602
