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.