RSC：通过混合预测控制与在线重配置实现大规模集群的分散式刚性编队集群
RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration
AI总结：
提出一种分散式控制框架RSC，结合有限时域轨迹预测与反应式人工势场安全控制器，并引入在线领航-跟随重配置机制，在25架无人机杂乱环境中实现83%的编队保持、避障与目标跟踪成功率。
摘要：
分散式刚性编队集群要求自主智能体集群在移动过程中仅依靠局部感知和通信来维持预定的几何构型。然而，现有的分散式控制方法在杂乱环境中难以保持严格的智能体间距离约束，常常遭遇局部极小死锁、高频控制振荡或避障时灵活性有限等问题，导致成功率低。为解决这些限制，我们提出了刚性集群控制（RSC），一种用于大规模刚性编队集群的分散式控制框架。为了通过鲁棒的长期规划逃离局部极小同时确保短期安全，RSC在混合架构中集成了有限时域轨迹预测与反应式人工势场（APF）安全控制器。此外，为了在穿越障碍后加速编队重组而不中断任务执行，RSC引入了一种基于稳定角色交换的在线领航-跟随重配置机制。在25架无人机的挑战性杂乱环境中的广泛评估表明，RSC可靠地统一了刚性编队保持、避障和目标跟踪。在严格的成功标准——无碰撞运行且最大相对边长度误差低于10%下，RSC实现了83%的成功率，显著优于成功率低于5%的现有启发式和基于学习的基线方法。
英文摘要：
Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying solely on local sensing and communication. However, existing decentralized control methods struggle to maintain strict inter-agent distance constraints in cluttered environments, often suffering from local minima deadlocks, high frequency control oscillations, or limited flexibility during obstacle navigation, resulting in low success rate. To address these limitations, we propose Rigid Swarm Control (RSC), a decentralized control framework for large-scale rigid formation flocking. To escape local minima via robust long-term planning while ensuring short-term safety, RSC integrates finite-horizon trajectory predictions with a reactive artificial potential field (APF) safety controller within a hybrid architecture. Furthermore, to accelerate formation reassembly after obstacle traversal without interrupting task execution, RSC introduces an online leader-follower reconfiguration mechanism based on stable role exchange. Extensive evaluations in challenging cluttered environments with 25 UAVs demonstrate that RSC reliably unifies rigid formation maintenance, obstacle avoidance, and target tracking. Under strict success criteria - collision-free operation with a maximum relative edge-length error below 10%, RSC achieves an 83% success rate, significantly outperforming existing heuristic and learning-based baselines that fall below 5%.
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。
