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Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

机译：基于多目标粒子群算法的多机器人路径规划

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摘要

In this paper, a new method is proposed for the path planning of multi-robots in unknown environments. The method is inspired by multi-objective particle swarm optimization (MOPSO) and is named multi-robot MOPSO. It considers shortness, safety, and smoothness. Due to the obscurity of the environment, the robots should decide the moving direction based on the information gathered by sensors only such that the optimal path between the start and goal positions can be found at the end of the algorithm. Sharing knowledge among the navigating robots is necessary to achieve this aim. So, a new concept, named the probabilistic window, is introduced in this paper. It combines the current information obtained through the robot sensors and experiences of the previous robots to select the paths that seem more likely to achieve higher fitness in the mentioned objectives. The proposed method has an outstanding performance on different complex benchmarks, and the results have shown that it is more effective and efficient compared with the classic and the state-of-the-art methods.
机译：本文提出了一种未知环境下多机器人路径规划的新方法。该方法受多目标粒子群优化（MOPSO）的启发，被称为多机器人MOPSO。它考虑了短度，安全性和平滑性。由于环境的模糊性，机器人只能根据传感器收集的信息来决定运动方向，以便在算法结束时可以找到起始位置和目标位置之间的最佳路径。为了实现这一目标，在导航机器人之间共享知识是必要的。因此，本文引入了一个新的概念，即概率窗口。它结合了通过机器人传感器获得的当前信息以及先前机器人的经验来选择在上述目标中似乎更有可能实现更高适应性的路径。所提出的方法在不同的复杂基准上均具有出色的性能，结果表明，与传统方法和最新方法相比，该方法更加有效。

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