发表机构
New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于约束二次规划的分散式控制器，通过仅放宽沿路径速度约束，在交叉路径上实现多四旋翼的碰撞避免与严格路径跟踪，并给出理论保证及物理引擎验证。
AI 中文摘要
本文研究了多四旋翼在交叉路径上的分散式安全路径跟踪问题，其中安全性要求既包括避免碰撞，也包括严格遵守预分配路线。所提出的控制器将横向反馈线性化重新表述为一个受四个等式约束的约束二次规划：其中两个约束强制收敛到路径并严格遵守路径，另外两个约束规定期望速度和航向。安全性通过仅放宽沿路径速度约束来实现，而路径和航向约束保持为硬约束。在所陈述的假设下，对于可接受的初始条件，该控制器保证所有智能体收敛到其分配路径并随后沿路径行进、避免碰撞，并避免姿态奇异性。我们在Drake物理引擎中，在非平面交叉路径上评估了该控制器，并将其与两种标称加安全滤波器级联方法进行了比较。代码和额外结果可在以下网址获取：此https URL。
英文摘要
This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and strict adherence to pre-assigned routes. The proposed controller reformulates transverse feedback linearization as a constrained quadratic program with four equality constraints: two enforce convergence to and strict adherence to the path, and two prescribe the desired speed and heading. Safety is achieved by relaxing only the along-path speed constraint, while the path and heading constraints remain hard. Under the stated assumptions, and for admissible initial conditions, the controller guarantees that all agents converge to and thereafter follow their assigned paths, avoid collisions, and avoid attitude singularities. We evaluate the controller in the Drake physics engine on non-planar intersecting paths and compare it against two nominal-plus-safety-filter cascades. Code and additional results are available at https://gradslab.github.io/safe_multiquad_pf/.
Comments8 pages, 3 figures
