Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.
A modular functional framework for the design and evaluation of multi-robot navigation
