A novel fuzzy and reverse auction-based algorithm for task allocation with optimal path cost in multi-robot systems

One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi-robot task allocation challenge with path costs, the lowest computing time, and task distribution. Furthermore, it is usual for a robot's processing capabilities to be restricted to operate in various target environments. As a consequence, adequate processing power consumption would demonstrate the system's efficiency. Task allocation and path planning issues must be addressed regularly to ensure multi-robot system operation. Task allocation and path planning issues must be addressed regularly to ensure multi-robot system operation. The above-mentioned serious challenge gets more complicated when system factors such as robots and tasks multiply. As previously stated, this article solves the issue using a fuzzy-based optimum path and reverse auction-based methods. The detailed simulation results indicate that the suggested methods can solve task allocation with the lowest path cost. A comparative study is conducted between the suggested algorithm and two existing commonly used techniques, the auction-based and the Hungarian algorithms. Finally, the suggested method was run in real-time on a TurtleBot2 robot. The findings show the suggested algorithm's efficiency and simplicity of implementation.