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A coalition formation algorithm for Multi-Robot Task Allocation in large-scale natural disasters

机译：大规模自然灾害中多机器人任务分配的联盟形成算法

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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.
机译：在大规模自然灾害中，当人们试图到达高风险地点或采取搜救行动时，他们很可能会失败。但是，由于机器人具有多种多样的传感和致动功能，因此它们在幸存的危险和执行搜索与救援任务方面胜过同类机器人。为了成本效率而动态地形成这些异构机器人的最佳联盟非常具有挑战性，并且该领域的研究越来越受到关注。在本文中，我们提出了一种新颖的启发式方法。由于在大规模灾难环境中机器人的数量非常庞大，因此我们依赖于量子多目标粒子群优化（QMOPSO）。该问题被建模为多目标优化问题。进行了具有不同测试用例和度量的仿真，并与其他算法（例如NSGA-II和SPEA-II）进行了比较。实验结果表明，该算法不仅在收敛性上而且在多样性和处理时间上都优于现有算法。

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