Optimal coverage control of stationary and moving agents under effective coverage constraints
Optimal coverage control of stationary and moving agents under effective coverage constraints
复制标题
有效覆盖约束下静止和移动主体的最优覆盖控制
DOI：
10.1016/j.automatica.2023.111236
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发表时间：
2023
期刊：
影响因子：
6.4
通讯作者：
Cassandras, Christos G.
中科院分区：
文献类型：
--
作者：
Sun, Xinmiao;Ren, Mingli;Ding, Da-Wei;Cassandras, Christos G.
本文研究了在使命空间中同时使用固定和移动的智能体以满足有效覆盖约束的最大化覆盖问题，即，使命空间的每个点必须在给定的时间段内至少一次覆盖到预定水平。固定代理的部署可以预先给定，或者可以通过经典的覆盖控制算法来获得。移动的智能体的运动规划是在最大速度和加速度约束下设计的。当只有一个移动的智能体时，它的路径规划和速度规划可以分开设计。首先给出了给定路径的最优速度策略和相应的最优覆盖性能，为指定好的路径提供了准则;然后通过连接一组“检测点”，生成满足有效覆盖约束的路径规划。受最优速度策略的启发，提出了三种检测点生成方法，并通过基于旅行商问题（TSP）的方法获得检测点的最优连接顺序。最后，我们提出了两种方法，将单移动的智能体运动规划方案扩展到多个移动的智能体。仿真例子包括比较三种方法产生的检查点的性能，并比较多个移动的代理所提出的方法的性能。
This paper addresses the problem of maximizing coverage in a mission space with both stationary and mobile agents such that effective coverage constraints are satisfied, i.e., each point of the mission space must be covered to a predefined level at least once over a given time period. The deployment of the stationary agents may be given in advance or may be obtained by a classical coverage control algorithm. The motion planning of the mobile agents is designed under maximal speed and acceleration constraints. When there is only one mobile agent, it is shown that its path planning and velocity planning can be designed separately. We first obtain an optimal velocity policy and the corresponding optimal coverage performance for a given path, which provides a criterion to prescribe a good path. Then, path planning is generated to meet the effective coverage constraint by connecting a set of “inspection points”. Inspired by the optimal velocity policy, we propose three methods to generate the inspection points and obtain the optimal order of connecting the inspection points by a Traveling Salesman Problem (TSP)-based method. Finally, we extend the one-mobile-agent motion planning scheme to multiple mobile agents by proposing two methods. Simulation examples are included to compare the performance of the three methods for generating inspection points and compare the performance of the proposed methods for multiple mobile agents.