Scalable Vehicle Team Continuum Deformation Coordination With Eigen Decomposition
Scalable Vehicle Team Continuum Deformation Coordination With Eigen Decomposition
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DOI：
10.1109/tac.2021.3079208
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发表时间：
2020-02
影响因子：
6.8
通讯作者：
H. Rastgoftar;E. Atkins;I. Kolmanovsky
中科院分区：
文献类型：
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作者：
H. Rastgoftar;E. Atkins;I. Kolmanovsky
连续变形主从协调控制策略将多智能体系统中的车辆建模为可变形体的粒子。基于领导者的轨迹定义期望的连续体变形，并且通过本地通信由跟随者在真实的时间中获取。现有的连续体变形理论要求从变形体被放置在由前导变形体定义的凸单形中。本文放宽了这一限制。我们证明，在适当的假设下，任何$n+1$（$n= 1，2，3 $）车辆形成一个$n$-D单纯形可以被选为领导者，而追随者，任意位于内部或外部的领导单纯形，可以获得所需的连续变形在一个分散的方式。本文的第二个贡献是分配一个一对一的映射领导人的光滑轨迹和均匀变形特征之间的连续变形特征分解。因此，一个安全和光滑的连续变形协调可以通过塑造齐次变换功能或通过选择适当的领导者轨迹。这有利于在大规模群组中有效地规划和保证碰撞避免。一个模拟的案例研究中，一个虚拟的凸单纯形包含一个四轴飞行器车队在任何时候$t$; A* 搜索应用于优化四轴飞行器车队连续变形中的障碍物负载的环境。
The continuum deformation leader–follower cooperative control strategy models vehicles in a multiagent system as particles of a deformable body. A desired continuum deformation is defined based on leaders’ trajectories and acquired by followers in real time through local communication. The existing continuum deformation theory requires followers to be placed inside the convex simplex defined by leaders. This constraint is relaxed in this article. We prove that, under suitable assumptions, any $n+1$ ($n=1,2,3$) vehicles forming an $n$-D simplex can be selected as leaders while followers, arbitrarily positioned inside or outside the leading simplex, can acquire a desired continuum deformation in a decentralized fashion. The article’s second contribution is to assign a one-to-one mapping between leaders’ smooth trajectories and homogeneous deformation features obtained by continuum deformation eigendecomposition. Therefore, a safe and smooth continuum deformation coordination can be planned either by shaping homogeneous transformation features or by choosing appropriate leader trajectories. This is beneficial to efficiently plan and guarantee collision avoidance in a large-scale group. A simulation case study is reported in which a virtual convex simplex contains a quadcopter vehicle team at any time $t$; A* search is applied to optimize quadcopter team continuum deformation in an obstacle-laden environment.