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.

中文翻译：

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多轮机器人的博弈论战略协调与导航

在动态工作空间中协商的多个机器人可能会导致碰撞。为避免此类问题，多机器人导航和协调变得必要，但在计算上非常具有挑战性，特别是当有许多机器人时。本文解决了单个机器人需要协调的多机器人导航问题。尽管已经研究了一些模拟多机器人协调和导航的尝试，但这项工作提出了一种博弈论协调策略，也称为战略协调。我们利用遗传算法调整的基于模糊逻辑的运动规划器。所提出的战略协调策略已与基于潜在场的运动规划器（也称为启发式方法）进行了性能比较。结果通过计算机模拟与不同轮次的 8 到 17 个机器人进行比较。从所获得的结果可以看出，所提出的协调方案对于大量机器人的有效性很强。此外，发现与基于遗传模糊的运动规划器的拟议战略协调方案在解决方案的质量和达到目标位置的时间方面优于其他组合。还比较和介绍了不同方法的计算复杂度。就解决方案的质量和达到目标位置的时间而言，与基于遗传模糊的运动规划器的拟议战略协调方案被发现优于其他组合。还比较和介绍了不同方法的计算复杂度。就解决方案的质量和达到目标位置的时间而言，与基于遗传模糊的运动规划器的拟议战略协调方案被发现优于其他组合。还比较和介绍了不同方法的计算复杂度。