RSC让无人机群在复杂环境里保持队形移动，还能自动避障重组。
RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration
- 用预测规划+实时安全控制的混合架构，避免卡死和抖动。
- 25架无人机在障碍物中飞行，90%以上时间队形误差小于10%。
- 支持动态换位重组，适合大规模无人机编队任务。
去中心化刚性队形集群要求一群自主智能体在移动时维持预定几何构型，仅依赖局部感知与通信。然而，现有方法在复杂环境中难以严格满足代理间距离约束，常出现局部极小死锁、高频控制振荡或避障灵活性不足，导致成功率低下。为解决这些问题，我们提出刚性集群控制（RSC），一种用于大规模刚性队形集群的去中心化控制框架。RSC通过有限时域轨迹预测与反应式人工势场（APF）安全控制器，在混合架构中实现鲁棒长期规划与短期安全兼顾。此外，为加速穿越障碍后的队形重建而不中断任务，RSC引入基于稳定角色互换的在线主从重配置机制。在包含25架无人机的复杂障碍环境中的大量评估表明，RSC能可靠统一维持刚性队形、避障与目标追踪。在严格成功标准下——无碰撞且最大相对边长误差低于10%，RSC达到83%成功率，显著优于现有启发式与学习基基线（均低于5%）。
原文摘要 · Abstract (English)
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%.
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。