Abstract
This paper presents a distributed cooperative search algorithm for multiple unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities in a nonconvex environment. The objective is to control multiple UAVs to find several unknown targets deployed in a given region, while minimizing the expected search time and avoiding obstacles. First, an asynchronous distributed cooperative search framework is proposed by integrating the information update into the coverage control scheme. And an adaptive density function is designed based on the real-time updated probability map and uncertainty map, which can balance target detection and environment exploration. Second, in order to handle nonconvex environment with arbitrary obstacles, a new transformation method is proposed to transform the cooperative search problem in the nonconvex region into an equivalent one in the convex region. Furthermore, a control strategy for cooperative search is proposed to plan feasible trajectories for UAVs under the kinematic constraints, and the convergence is proved by LaSalle’s invariance principle. Finally, by simulation results, it can be seen that our proposed algorithm is effective to handle the search problem in the nonconvex environment and efficient to find targets in shorter time compared with other algorithms.
Highlights
Over the past decade, unmanned air vehicles (UAVs) with functional diversity and low cost have been extensively employed in many civil and military applications, such as environment surveillance, battle reconnaissance, and search and rescue in the hazardous environment [1,2,3]
The goal of this paper is to develop an efficient cooperative search method for multiple unmanned aerial vehicles (UAVs) to find several unknown targets in a nonconvex environment with arbitrary obstacles
This paper presents a distributed cooperative search method with multiple UAVs in a complex nonconvex environment with arbitrary obstacles
Summary
Over the past decade, unmanned air vehicles (UAVs) with functional diversity and low cost have been extensively employed in many civil and military applications, such as environment surveillance, battle reconnaissance, and search and rescue in the hazardous environment [1,2,3]. Hu et al [15] formulate the path planning as a coverage control problem to find an optimal configuration of all agents that minimizes a given coverage performance cost function They use the uncertainty information as the density function to cover the region uniformly, which cannot directly facilitate agents to search the region with high target existence. We integrate the information update into the coverage control scheme by introducing the probability map of target existence together with the uncertainty map into the density function of coverage optimization. An adaptive density function is formulated depending on real-time updated probability map and uncertainty map, which can balance target search and environment exploration.
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