Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms

A multi-objective vehicle path planning method has been proposed to optimize path length, path safety and path smoothness using the elitist non-dominated sorting genetic algorithm (NSGA-II). Four different path representation schemes that begin its coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance, maximization of path safety, and maximization of path smoothness are considered as objectives of this study. A performance evaluation measure using the hypervolume metric of the obtained trade-off front is used. This study makes a careful analysis of a number of issues related to the optimization of path planning task – handling of constraints associated with the problem, handling single versus multiple objectives, performing a parametric study to locate reasonable values of key parameters, identifying an efficient path representation scheme, and evaluating the proposed algorithm on large sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91%) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems.