A multirobot target searching method based on bat algorithm in unknown environments

Abstract

Multirobot target searching in unknown environments is a currently trending topic of discussion. In this paper, an improved bat algorithm (BA) for multirobot target searching in unknown environments, named adaptive robotic bat algorithm (ARBA), is proposed; it acts as the controlling mechanism for robots. The obstacle avoidance problem is considered in the proposed ARBA. The adaptive inertial weight strategy helps ARBA improve its diversity and provides an effective mechanism for escaping from local optima. In addition, the Doppler effect is introduced to improve ARBA; the effect can be adaptively compensated when the robot moves and helps robots avoid premature convergence. Moreover, the location of the target in an unknown environment is unknown, and a multi-swarm strategy is introduced into the ARBA to improve the diversity and expand the search space of robots so that robots can find the location of the target as well as the target itself faster than the existing algorithms. Experiments were conducted in three aspects to verify the effectiveness and efficiency of ARBA. We compared ARBA with the other algorithms in this field; the experimental results demonstrate that ARBA exhibits better performance in multirobot target searching and can be applied to multirobot intelligent systems.