A cost-effective nash-based allocation method for task distribution of multiple robots in distributed robotic networks

Efficiency is paramount in distributed robotic networks (DRNs), where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. The study introduces a new cost-efficient Nash-based game framework for task allocation in a distributed robotic network. The proposed model relies on a decentralized decision-making strategy, where each robot selects a single task that optimizes its execution time at a constant speed, thereby maximizing energy harvesting and minimizing energy consumption. In this context, each robot optimizes its choices for individual benefit while also considering the collective welfare, achieving the Nash equilibrium as a nearly optimal allocation strategy in DRNs. The proposed model is tested on various MRTA scenarios involving five robots, seven robots, ten robots, fifteen robots, and twenty robots with the same number of tasks. The proposed Nash-based decentralized model outperforms the Hungarian method by significantly reducing computational costs and complexity to O(N), making it more efficient for large-scale problems.