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Robotic path planning using hybrid genetic algorithm particle swarm optimisation

机译：混合遗传算法的粒子群算法在机器人路径规划中的应用

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摘要

The problem of robotic path planning has always attracted the interests of a significantly large number of researchers due to the various constraints and issues related to it. The optimisation in terms of time and path length and validity of the non-holonomic constraints, especially in large sized maps of high resolution, pose serious challenges for the researchers. In this paper we propose hybrid genetic algorithm particle swarm optimisation (HGAPSO) algorithm for solving the problem. Diversity preservation measures are introduced in this applied evolutionary technique. The novelty of the algorithm is threefold. Firstly, the algorithm generates paths of increasing complexity along with time. This ensures that the algorithm generates the best path for any type of map. Secondly, the algorithm is efficient in terms of computational time which is done by introducing the concept of momentum-based exploration in its fitness function. The indicators contributing to fitness function can only be measured by exploring the path represented. This exploration is vague at start and detailed at the later stages. Thirdly, the algorithm uses a multi-objective optimisation technique to optimise the total path length, the distance from obstacle and the maximum number of turns. These multi-objective parameters may be altered according to the robot design.
机译：由于各种约束和相关问题，机器人路径规划问题一直吸引着大量研究人员的兴趣。在时间和路径长度以及非完整约束的有效性方面的优化，特别是在高分辨率的大型地图中，对研究人员提出了严峻的挑战。本文提出了一种混合遗传算法粒子群算法（HGAPSO）来解决该问题。在这种应用的进化技术中引入了多样性保护措施。该算法的新颖性是三重的。首先，该算法生成的复杂度随时间增加的路径。这样可以确保算法为任何类型的地图生成最佳路径。其次，该算法在计算时间方面很有效，这是通过在其适应度函数中引入基于动量的探索概念来完成的。只能通过探索代表的路径来测量有助于健身功能的指标。这项探索在开始时比较模糊，在以后的阶段中会详细介绍。第三，该算法使用多目标优化技术来优化总路径长度，距障碍物的距离和最大转弯数。这些多目标参数可以根据机器人设计进行更改。

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