A Coalition Formation Algorithm for Multi-Robot Task Allocation in Large-Scale Natural Disasters

TL;DR

This paper introduces a quantum-inspired multi-objective optimization algorithm for forming efficient robot coalitions in large-scale disaster scenarios, improving search and rescue operations.

Contribution

It presents a novel heuristic based on QMOPSO for dynamic coalition formation among heterogeneous robots in large-scale disasters.

Findings

Outperforms NSGA-II and SPEA-II in convergence and diversity.

Reduces processing time for coalition formation.

Effective in large-scale, heterogeneous robot environments.

Abstract

In large-scale natural disasters, humans are likely to fail when they attempt to reach high-risk sites or act in search and rescue operations. Robots, however, outdo their counterparts in surviving the hazards and handling the search and rescue missions due to their multiple and diverse sensing and actuation capabilities. The dynamic formation of optimal coalition of these heterogeneous robots for cost efficiency is very challenging and research in the area is gaining more and more attention. In this paper, we propose a novel heuristic. Since the population of robots in large-scale disaster settings is very large, we rely on Quantum Multi-Objective Particle Swarm Optimization (QMOPSO). The problem is modeled as a multi-objective optimization problem. Simulations with different test cases and metrics, and comparison with other algorithms such as NSGA-II and SPEA-II are carried out. The experimental results show that the proposed algorithm outperforms the existing algorithms not only in terms of convergence but also in terms of diversity and processing time.