让无人机群在3D空间快速换形，还能避撞，省时近一半。
CAT-ORA: Collision-Aware Time-Optimal Formation Reshaping for Efficient Robot Coordination in 3D Environments
- 用匈牙利算法分配目标点，直接约束冲突的机器人-目标配对来避撞。
- 仿真和19架无人机实测表明，换形时间平均缩短12%，最多省49%。
- 适合电池续航短的多旋翼无人机，尤其在需频繁变队形的场景。
本文提出一种用于三维环境中时间最优编队重构且避免碰撞的算法。针对移动机器人在实际应用中需频繁调整队形以高效导航或完成任务的问题，特别是电池供电的多旋翼无人机受限于运行时间，编队重构所需时间至关重要。所提出的碰撞感知时间最优编队重构算法（CAT-ORA）基于匈牙利算法解决机器人到目标点的分配问题，并通过直接约束互斥的机器人-目标对实现机间避撞，结合最小化重构过程持续时间的轨迹生成方法。理论验证证明了其最优性，仿真与包含19架无人机的真实室外实验进一步展示了其有效性。数值分析显示，在随机生成场景中，相较于常用方法，复杂编队重构任务的耗时平均减少12%，最多可降低49%。
原文摘要 · Abstract (English)
In this paper, we introduce an algorithm designed to address the problem of time-optimal formation reshaping in three-dimensional environments while preventing collisions between agents. The utility of the proposed approach is particularly evident in mobile robotics, where agents benefit from being organized and navigated in formation for a variety of real-world applications requiring frequent alterations in formation shape for efficient navigation or task completion. Given the constrained operational time inherent to battery-powered mobile robots, the time needed to complete the formation reshaping process is crucial for their efficient operation, especially in case of multi-rotor Unmanned Aerial Vehicles (UAVs). The proposed Collision-Aware Time-Optimal formation Reshaping Algorithm (CAT-ORA) builds upon the Hungarian algorithm for the solution of the robot-to-goal assignment implementing the inter-agent collision avoidance through direct constraints on mutually exclusive robot-goal pairs combined with a trajectory generation approach minimizing the duration of the reshaping process. Theoretical validations confirm the optimality of CAT-ORA, with its efficacy further showcased through simulations, and a real-world outdoor experiment involving 19 UAVs. Thorough numerical analysis shows the potential of CAT-ORA to decrease the time required to perform complex formation reshaping tasks by up to 49%, and 12% on average compared to commonly used methods in randomly generated scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。