Hunting moving targets with random motions and behavior is a challenge for robotic systems, and it occupies a significant position in the research on coordination and cooperation in multi-robot systems. Cooperative hunting between robots is used in a wide range of fields such as industry, military, rescue and other fields. The aim of this paper is to present a new strategy for target hunting by means of a cooperative multi-robot system in two-dimensional space, especially moving targets with random and unexpected behavior. The strategy is inspired from the behavior of wolves in hunting and Wolf Swarm Algorithm, as it summarizes the roles of robots in the system in three roles: the leader wolf, the antagonist wolf, and the follower wolf. This diversity of roles contributed to improve the convergence performance of the algorithm and reduce significantly the pursuit time. The validity of this strategy is supported by computer simulations.

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The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.