Abstract
This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.
Highlights
In recent years, the multi-agent persistent monitoring problem has received much attention because of the wide range of applications such as smart cities, intelligent transportation, and industrial automation (Nigam, 2014; Yu et al, 2015; Ha and Choi, 2019; Maini et al, 2020)
Most approaches for multi-agent persistent monitoring problems are exploited in a centralized fashion, such as reinforcement learning (Chen et al, 2020; Liu et al, 2020), approximate dynamic programming (Deng et al, 2017), data-driven (Alam et al, 2018) and others (Smith et al, 2011; Zhao et al, 2018; Asghar et al, 2019; Ostertag et al, 2019; Hari et al, 2020)
The target states are dynamically changing with time, and the uncertainty estimation of target i is denoted by Ri(t) according to the following model: R_i(t)
Summary
The multi-agent persistent monitoring problem has received much attention because of the wide range of applications such as smart cities, intelligent transportation, and industrial automation (Nigam, 2014; Yu et al, 2015; Ha and Choi, 2019; Maini et al, 2020) It involves a finite set of targets with dynamical behaviors that need to be monitored, and the main objective is to design the motion strategy for a team of agents equipped with sensors to move between these targets to collect information or minimize the uncertainty metric of targets over a long period of time. (Rezazadeh and Kia, 2021) assigned to each target a concave and increasing reward function, and a distributed sub-optimal greedy algorithm with bounded performance was designed based on the submodularity of the objective function This approach does not take into account the need of dwell time for agents to monitor targets, which is related to the agents’ real-time strategies, limiting their application.
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