Many Objectives Autonomous Robot Path Planning with Improved MOEA/D

Path planning is the core of autonomous robot navigation, which helps the robot to find a collision-free path to the destination based on the environment information. Most current path planning methods only consider the path length, but the optimal path may deviate from the shortest when considering other environmental factors such as uneven terrain or regions with varying traversal costs. Similarly, in scenarios prioritizing energy efficiency, a sole focus on path length may lead to suboptimal solutions. In this paper, an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with adaptive weight vector, external archive, and constrained update strategy namely the MOEA/D-EAWA is proposed. This algorithm not only considers the path length but also four additional objectives such as smoothness, traveling time, terrain (elevation), and speed limit (expected delay). In addition, MOEA/D-EAWA is better suited for such many-objective path planning problem which has an irregular, discrete, and sparse Pareto front. The simulation results from 90 map instances demonstrate that the proposed method outperforms the existing approaches.