大規模災害におけるマルチロボットタスク割当のための連合形成アルゴリズム
A Coalition Formation Algorithm for Multi-Robot Task Allocation in Large-Scale Natural Disasters
大規模災害での救助活動に向け、異種ロボット群の最適な連合を量子多目的粒子群最適化で形成する手法を提案し、NSGA-IIやSPEA-IIより収束性・多様性・処理時間で優れることを示した。
著者: Carla Mouradian, Jagruti Sahoo, Roch H. Glitho, Monique J. Morrow, Paul A. Polakos
分類: cs.MA, cs.RO
原文アブストラクト
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.
