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A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

机译：基于多目标粒子群优化的移动机器人的分层全局路径规划方法

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In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach. (C) 2017 Elsevier B.V. All rights reserved.
机译：在本文中，提出了一种杂乱环境中的移动机器人的新型分层全局路径规划方法。这种方法具有三级结构，可获得可行，安全和最佳的路径。在第一级中，三角形分解方法用于快速建立机器人的几何自由配置空间。在第二级，Dijkstra的算法应用于找到一个用于下一个级别的输入参考的自由碰撞路径。最后，采用了一种基于Pareto优势原理的加速更新方法的提出的粒子群优化，以加速的更新方法进行了基于Pareto优势原理，以产生全局最优路径，重点是最小化路径长度并最大化路径平滑度。这项工作的贡献包括：（i）开发一种新颖的最佳分层全球路径规划方法，用于移动机器人在杂乱环境中移动; （ii）基于Pareto主导原则的加速更新方法的提出粒子群优化的发展，解决机器人路径规划问题; （iii）在路径长度和路径平滑方面提供最佳的全球机器人路径，以考虑到计算效率的物理机器人系统限制。进行各种类型环境的仿真结果，以说明等级方法的优势。 （c）2017 Elsevier B.v.保留所有权利。

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