UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment

TL;DR

This paper introduces an enhanced MOEA/D algorithm with adaptive areal weight adjustment for efficient 3-D UAV path planning, balancing path length and terrain threat, validated across multiple scenarios.

Contribution

It presents a novel adaptive areal weight adjustment strategy for MOEA/D, improving solution diversity in UAV 3-D path planning.

Findings

Improved solution diversity with AAWA strategy.

Effective tradeoff between path length and terrain threat.

Validated performance across synthetic and realistic scenarios.

Abstract

Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning is a key challenge for task decision-making. This paper proposes an improved multi-objective 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 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 towards the sparser area of the population in the objective space, its weight vector is constructed by the objective function value of its neighbors. The effectiveness of MOEA/D-AAWA is validated in twenty synthetic scenarios with different number of obstacles and four realistic scenarios in comparison with other three classical methods.