Optimal Search and Coverage in Swarm Robotics Using the Reptile Search Algorithm

Bio-inspired locomotion algorithms facilitate exploratory robots in traversing difficult terrains by emulating natural movement techniques, hence improving flexibility and mobility in dynamic situations. This paper presents the Reptilian Utility-Cost Optimizer (RUCO), an innovative algorithm that enhances multi-robot exploration by reconciling utility-oriented goals with cost-effectiveness using reptilian-inspired search strategies. RUCO assesses neighboring terrain cells through a utility-cost paradigm to promote valuable exploration routes while reducing resource use. The algorithm’s efficacy is evaluated by three metrics: execution time, proportion of area explored, and frequency of mission failure. Comparisons with advanced methodologies—CME and CME-Grey Wolf Optimizer (CME-GWO)—across several intricate maps illustrate RUCO’s supremacy. In simulations, RUCO demonstrates expedited task completion, optimizes terrain coverage, and diminishes failure rates by utilizing bioinspired optimization concepts. The results validate RUCO’s efficacy in unstructured situations, surpassing current methodologies in efficiency, reliability, and flexibility, thereby positioning it as a viable alternative for autonomous exploration missions in hazardous or unfamiliar terrains