Abstract
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.
Highlights
Mobile-robot space exploration utilizing cluster of robots have a wide spectrum utilization ranging from transportation [1], healthcare [2], industry [3], rescue [4]–[6], to all sort of Dull Dirty and Dangerous (DDD) missions [7]–[9]
AND DISCUSSION we present the results for the proposed hybrid multi-coordinated exploration based on frequency modified whale optimization algorithm (WOA)
The maps are divided into sections namely 0-40%, 40-75% and 75-100%, according to the area explored by the robots
Summary
Mobile-robot space exploration utilizing cluster of robots have a wide spectrum utilization ranging from transportation [1], healthcare [2], industry [3], rescue [4]–[6], to all sort of Dull Dirty and Dangerous (DDD) missions [7]–[9]. 3) HYBRID ALGORITHMS Hybrid algorithms for multi-robot involve utilization of both deterministic CME and stochastic Bio-inspired techniques to solve modal problems They facilitate efficient way-point generation in the map and achieve improvements in the results that could be achieved from the utilization of individual approaches. 4) CRITICAL OBSERVATION Based on our review of the work done in regards to space exploration utilizing multi-robot configuration and the cited papers, it can be noted that certain areas are not fully covered in the literature and require further investigation. Meta-heuristic techniques such as Grey-wolf utilized in many existing hybrid algorithms increase the convergence rate, the results might not be always optimal This increases the probability to get stuck in the local/global optima problem. There remains a need to explore hybrid algorithms combining CME with meta-heuristic technique to achieve optimal space convergence utilizing multi-robot configuration. The efficacy of the results achieved through the proposed algorithm is demonstrated by comparing the results with other hybrid techniques which involved the integration of CME with grey wolf algorithm, CME with conventional whale algorithm, and CME with SineCosine algorithm
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