A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management

Unmanned aerial vehicles are instrumental in monitoring and analyzing information and searching for people in disaster relief scenarios. In this paper, path planning is constructed as a multiobjective optimization problem with constraints in a three-dimensional terrain disaster scenario. The objective functions involve the distance and risk of the path, which are calculated based on Bezier theory. The constraints include the turning angle and flight altitude. To solve this problem in an effective and efficient manner, a differential evolution algorithm that is based solely on the knee point is proposed, in which the knee solution would guide the search direction of the algorithm. According to the minimal Manhattan distance approach, the algorithm can quickly identify an optimal solution to generating a smooth path for decision-makers. Experimental results have confirmed the superiority of the proposed algorithm, and the rankings of the minimal Manhattan distance approach are consistent with multicriteria decision-making methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a multiobjective optimization problem for path planning in a three-dimensional terrain disaster scenario. A differential evolution algorithm based on knee point is proposed to efficiently generate smooth paths and identify optimal solutions. Experimental results confirm the superiority of the algorithm.