Path planning and coordination in a multi-robot system are important and complex tasks in any environment. In a multi-robot system, there can be multiple objectives to be achieved by multiple robots simultaneously. Nowadays, many mobile service robots are being used in warehouses to reduce running costs and overheads. In a large warehouse, there can be multiple robots to handle the number of operations. Planning a path means to find out the optimal route, and coordinating them means a collision-free route. To get both the parameters to reach their optimal level becomes a tedious task to achieve. The efficiency of overall warehouse operation can be improved by adequately addressing the coordination and path planning issues among the robots. In warehouses, each robot has to navigate to its destination by finding a collision-free optimal route in coordination with other robots. In this paper, a comparative study with the acclaimed path planning and coordination has been presented. The proposed smart approach has been presented for a multi-robot system to find a collision-free optimal path in a warehouse to handle storage pods. This paper proposes a smart distance metric-based approach for a multi-robot system to identify their goals smartly and traverse only a minimal path to reach their goal without getting being collided. It uses a smart distance metric-based approach to find the intended path. The proposed work performs better when compared with other works like A* and ILP. It is strictly monitored that there is no collision occurred during execution. Three different instances of a warehouse have been considered to carry out the experiments with parameters such as path length, average path and elapsed time. The experiments with 800 pods and 16 robots report the improvement in performance up to 2.5% and 13% in average path length and elapsed time.

在任何环境中，多机器人系统中的路径规划和协调都是重要且复杂的任务。在多机器人系统中，多个机器人可以同时实现多个目标。如今，仓库中使用了许多移动服务机器人，以降低运行成本和管理费用。在大型仓库中，可以有多个机器人来处理许多操作。规划路径意味着找出最佳路线，进行协调意味着没有碰撞的路线。要使两个参数都达到最佳水平，就成为一项繁琐的任务。通过充分解决机器人之间的协调和路径规划问题，可以提高整个仓库的运营效率。在仓库里 每个机器人都必须通过与其他机器人协作找到无碰撞的最佳路线来导航到其目的地。在本文中，我们进行了一项备受赞誉的路径规划和协调的比较研究。已经针对多机器人系统提出了建议的智能方法，以在仓库中找到无冲突的最佳路径来处理存储吊舱。本文为多机器人系统提出了一种基于智能距离度量的方法，可以智能地识别他们的目标，并且只经过一条最小的路径即可达到目标而不会发生冲突。它使用基于智能距离度量的方法来查找预期路径。与A *和ILP等其他作品相比，拟议的作品表现更好。严格监控在执行过程中没有发生冲突。已考虑使用三个不同的仓库实例来执行实验，这些实验的参数包括路径长度，平均路径和经过时间。用800个吊舱和16个机器人进行的实验表明，平均路径长度和经过时间的性能分别提高了2.5％和13％。