Abstract
Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.
Highlights
Steering a group of autonomous robots to search the targets is a well-studied problem due to its numerous important applications
swarm robotic pedestrian behavior (SRPB) is better than other strategies in terms of the time to find When the thelast population of robots collision ratehave of particle swarm optimization (PSO), A-robotic particle swarm optimization (RPSO), firefly algorithm (FA),so and source, andsize it shows greatexceeds stability.20, Thethe other strategies a highRPSO, standard deviation, these algorithms can be sorted as SRPB>RPSO>PSO>A-RPSO>FA>glowworm swarm optimization (GSO)>Levy flight search (LFS)
According to the collision rate, these strategies can be sorted as LFS>SRPB>GSO>A-RPSO RPSO>FA>PSO
Summary
Steering a group of autonomous robots to search the targets is a well-studied problem due to its numerous important applications. Once forming a group, robots will complete the task of target search based on GSO Robots in this strategy can seek multiple sources, the number of targets is known. This method loses the distribution information of the initial robots’ location. Considering the above constraints, including a limited range of communication and sensing, a limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination, a novel cooperative strategy is proposed. It is inspired by pedestrian behavior in subway/railway stations.
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