Comparative Analysis of Fruit Fly-Inspired Multi-Robot Cooperative Algorithm for Target Search and Rescue

Abstract

In an unforeseen or human-made disaster, any search and rescue agency’s primary concern are to locate the survivor immediately. For the victim’s survival, even minutes may play a critical part. To strengthen the performance of the SAR community, the research community could introduce some creative technologies. With faster victim searches, initial assessment and visualization of the environment, and real-time assessment and management of search and rescue operations, multi-robot programs can significantly increase the productivity of search and rescue operations. This article proposes an intelligent algorithm based on the fruit fly optimization algorithm’s food search pattern. To update the fruit flies’ position and speed, it uses particle swarm optimization. Repeated research simulations have confirmed the feasibility of the proposed process in a forest-like environment. The simulation results under different scenarios and performance comparison with existing techniques show the proposed algorithm’s accuracy and robustness to be quite efficient in terms of detection rate and search and rescue time. The detection rate improves 10-25% and search and rescue time by 5 to 15%.