Primitive-Swarm：面向大规模空中集群的超轻量级可扩展规划器
Primitive-Swarm: An Ultra-lightweight and Scalable Planner for Large-scale Aerial Swarms
AI总结：
针对大规模空中集群的规划难题，提出超轻量级可扩展规划器Primitive-Swarm，采用离线预计算原语库与线性复杂度选择，实现毫秒级规划，支持千机实时仿真。
摘要：
实现大规模空中集群具有挑战性，原因在于平衡计算效率与可扩展性之间存在固有矛盾。本文介绍了Primitive-Swarm，一种专为大规模自主空中集群设计的超轻量级且可扩展的规划器。所提出的方法采用分散式异步重规划策略。其中包含一个新颖的运动原语库，由时间最优且动态可行的轨迹组成。这些轨迹利用一种基于可达性分析的新型时间最优路径参数化算法（TOPP-RA）生成。然后，通过根据冲突将运动原语与离散周围空间关联，开发了一种快速碰撞检测机制。通过同时考虑空间和时间冲突，该机制同时处理机器人-障碍物和机器人-机器人碰撞。随后，在重规划过程中，每个机器人根据用户定义的需求从库中选择安全且成本最小的轨迹。时间最优运动原语库和占用信息均离线计算，将耗时的优化问题转化为线性复杂度的选择问题。这使得规划器能够全面探索充满众多障碍物和机器人的非凸、不连续三维安全空间，有效识别最佳隐藏路径。基准比较表明，我们的方法在密集环境中实现了最短飞行时间和飞行距离，计算时间小于1毫秒。涉及多达1000个机器人的超大规模集群仿真实时运行，验证了该方法的可扩展性。真实世界实验验证了该方法的可行性和鲁棒性。代码将发布以促进社区协作。
英文摘要：
Achieving large-scale aerial swarms is challenging due to the inherent contradictions in balancing computational efficiency and scalability. This paper introduces Primitive-Swarm, an ultra-lightweight and scalable planner designed specifically for large-scale autonomous aerial swarms. The proposed approach adopts a decentralized and asynchronous replanning strategy. Within it is a novel motion primitive library consisting of time-optimal and dynamically feasible trajectories. They are generated utlizing a novel time-optimial path parameterization algorithm based on reachability analysis (TOPP-RA). Then, a rapid collision checking mechanism is developed by associating the motion primitives with the discrete surrounding space according to conflicts. By considering both spatial and temporal conflicts, the mechanism handles robot-obstacle and robot-robot collisions simultaneously. Then, during a replanning process, each robot selects the safe and minimum cost trajectory from the library based on user-defined requirements. Both the time-optimal motion primitive library and the occupancy information are computed offline, turning a time-consuming optimization problem into a linear-complexity selection problem. This enables the planner to comprehensively explore the non-convex, discontinuous 3-D safe space filled with numerous obstacles and robots, effectively identifying the best hidden path. Benchmark comparisons demonstrate that our method achieves the shortest flight time and traveled distance with a computation time of less than 1 ms in dense environments. Super large-scale swarm simulations, involving up to 1000 robots, running in real-time, verify the scalability of our method. Real-world experiments validate the feasibility and robustness of our approach. The code will be released to foster community collaboration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
