Improving performance in swarm robots using multi-objective optimization

Erick Ordaz-Rivas and Luis Torres-Treviño

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 223, issue C, 433-457

Abstract: This paper presents a localization task for a robot swarm guided by the RAOI behavioral rules (repulsion, attraction, orientation, and influence). Slight changes in these parameters can significantly affect the task’s performance. To address this challenge, we have developed a swarm simulator incorporating the robots’ dynamic model. The localization task is evaluated using objective functions, represented as metrics, which are minimized using multi-objective optimization techniques. Our results showcase the Pareto fronts, illustrating how the objective functions react to variations in the RAOI parameters, aiming to expedite target localization while maintaining the swarm’s integrity.

Keywords: Swarm robotics; Swarm intelligence; Bio-inspired system; Multi-objective optimization; Localization task (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:223:y:2024:i:c:p:433-457

DOI: 10.1016/j.matcom.2024.04.027

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