A Coalition Formation Algorithm for Multi-Robot Task Allocation in Large-Scale Natural Disasters
This addresses the challenge of efficient multi-robot task allocation for search and rescue operations in large-scale natural disasters, representing an incremental improvement over existing methods.
The paper tackles the problem of dynamically forming optimal coalitions of heterogeneous robots for cost-efficient task allocation in large-scale natural disaster search and rescue, proposing a heuristic based on Quantum Multi-Objective Particle Swarm Optimization (QMOPSO) that outperforms existing algorithms like NSGA-II and SPEA-II in convergence, diversity, and processing time.
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.