Path Planning based on Multi-objective Topological Map

Abstract

Recently, intelligent robot technology has developed rapidly. As an important topic, path planning has attracted more and more attention. However, the performance of multi-objective path planning is limited by the scale of the problem, and the results of path planning are generally not optimal. In this work, based on 12 problems defined in multimodal multiobjective path planning optimization contest for CEC 2021 [1], we present a multi-objective path planning optimization algorithm, which includes data preprocessing, multi-objective genetic evolution path planning algorithm, and non-dominated sorting algorithm with elite strategy. This algorithm flow can realize multi-objective path planning and generate the optimal solution set (i.e. Pareto solution set). We implement our algorithm flow to calculate the time complexity and space complexity indicators. Experimental results on problems indicate the proposed multi-objective path planning algorithm can solve the optimal solution set in time. Due to the constraints of the problem, the number of optimal solutions is different for different problems. We show the validity of our method with experiments for path planning. Finally, we visually present some experimental results intuitively. The code is released at https://github.com/zhangruihao/pathPlanning.