AAV 3-D Path Planning Based on MOEA/D With Adaptive Areal Weight Adjustment

Abstract

Autonomous aerial vehicles (AAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning problems for AAVs can be treated as constrained multiobjective optimization problems. However, due to the complexity of real-world problems, the Pareto front frequently exhibits irregularity. For path planning problems characterized by sharp peaks and low tails on the Pareto front, this article proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with an adaptive areal weight adjustment (AAWA) strategy to make a tradeoff between the total flight path length and the terrain threat. AAWA is designed to improve the diversity and uniformity of the solutions. More specifically, AAWA first removes a crowded individual and its weight vector from the current population and then adds a sparse individual from the external elite population to the current population. To enable the newly added individual to evolve toward the sparser area of the population in the objective space, its weight vector is constructed by the objective function value of its neighbors. The experimental results in three types of synthetic scenarios and one realistic scenario demonstrate that MOEA/D-AAWA achieves uniformly distributed and diverse path solutions on sharp peaks and low tails, and provides a desired and collision-free compromise path.