Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

Multi-robot space exploration involves building a finite map utilizing a cluster of robots in an obstacle cluttered environment. The uncertainties are minimized by assigning tasks among robots and computing the optimum action. Such optimal trajectories are traditionally obtained utilizing deterministic or metaheuristic techniques, with each having peculiar limitations. Recently, limited work with the sub-optimal result has been done utilizing frameworks that utilize a blend of both techniques. This paper proposes a novel framework which involves the integration of deterministic Coordinated Multi-Robot Exploration (CME) and metaheuristic frequency modified Whale Optimization Algorithm (WOA) techniques, to perform search exploration that imitates the predatory behavior of whales. The frequency is dynamically adjusted utilizing a statistical objective function to tune exploitation and exploration operators. The proposed framework involves a) determination of the cost and utility functional values around individual group members utilizing deterministic CME technique, b) search space exploration to optimize and improve the overall solution utilizing frequency modified whale metaheuristic approach. The effectiveness of the proposed Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA) is ascertained by training the multi-robotic framework in different complexity environmental conditions. The results efficacy is then demonstrated by comparing the results of the proposed methodology with those achieved from three other contemporary optimization techniques namely CME-WOA, CME-GWO, and CME-SineCosine.

Automated summary

This paper proposes a novel framework that integrates deterministic Coordinated Multi-Robot Exploration (CME) and metaheuristic frequency modified Whale Optimization Algorithm (WOA) techniques to perform search exploration imitating the predatory behavior of whales. The framework dynamically adjusts frequency using a statistical objective function to tune exploitation and exploration operators to improve the overall solution optimization.