A Novel Improved Beluga Whale Optimization Algorithm for Solving Localization Problem in Swarm Robotic Systems

TL;DR

This paper introduces an Improved Beluga Whale Optimization Algorithm (IBWO) to enhance localization accuracy in swarm robotic systems, outperforming traditional and other meta-heuristic methods across various simulation scenarios.

Contribution

The study presents a novel meta-heuristic algorithm specifically designed for robot localization, improving accuracy over existing methods in swarm robotic systems.

Findings

IBWO outperforms traditional multilateration.

IBWO surpasses four other meta-heuristic localization methods.

Localization accuracy improves with higher anchor proportions.

Abstract

In Swarm Robotic Systems (SRSs), only a few robots are equipped with Global Positioning System (GPS) devices, known as anchors. A challenge lies in inferring the positions of other unknown robots based on the positions of anchors. Existing solutions estimate their positions using distance measurements between unknown robots and anchors. Based on existing solutions, this study proposes a novel meta-heuristic algorithm - Improved Beluga Whale Optimization Algorithm (IBWO) to address the localization problem of SRSs, focusing on enhancing the accuracy of localization results. Simulation results demonstrate the effectiveness of this study. Specifically, we test the localization accuracy of robots under different proportions of anchors, different communication radius of robots, and different total number of robots. Compared to the traditional multilateration method and four other localization methods based on meta-heuristic algorithms, the localization accuracy of this method is consistently superior.