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

Zuhao Teng, Qian Dong

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (22 sections, 24 equations, 12 figures, 6 tables, 1 algorithm)

This paper contains 22 sections, 24 equations, 12 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. Range-free Localization
  4. Range-based Localization
  5. The basis of proposed localization method
  6. DV-Hop Method
  7. Beluga Whale Optimization
  8. Proposed localization method
  9. Hop-optimized DV-Hop
  10. Improved Beluga Whale Optimization Algorithm
  11. Cyclone Foraging Strategy
  12. Chain Foraging Strategy
  13. Golden Sine Strategy
  14. Improved Beluga Whale Optimization
  15. Fitness Function and Localization Process
  16. ...and 7 more sections

Figures (12)

  • Figure 1: (a) Hop-optimized DV-Hop. (b) DV-Hop.
  • Figure 2: CCFS strategy
  • Figure 3: CAFS strategy
  • Figure 4: The performance of IBWO and 5 other representative meta-heuristic algorithms on the F1-F20 benchmark functions.
  • Figure 5: Flow chart of proposed localization method (a)Hop-optimized DV-Hop (b)Improved Beluga Whale Optimization
  • ...and 7 more figures