Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm

Honggen Xu,Aizhu Zhang,Shuai Jiang

doi:10.1109/access.2021.3072796

Abstract

Path planning is a global optimization problem aims to program the optimal flight path for Unmanned Aerial Vehicle (UAV) that has short length and suffers from low threat. In this paper, we present a Mixed-Strategy based Gravitational Search Algorithm (MSGSA) for the path planning. In MSGSA, an adaptive adjustment strategy for the gravitational constant attenuation factor alpha ( $\alpha$ ) is presented firstly, in which the value of $\alpha $ is adjusted based on the evolutionary state of the particles. This helps to adaptively balance the exploration and exploitation of the algorithm. In addition, to further alleviate the premature convergence problem, a Cauchy mutation strategy is developed for MSGSA. In this strategy, only when the global best particle cannot be further improved for several times the mutation is executed. In the MSGSA based path planning procedure, we construct an objective function using the flight length cost, threat area cost, and turning angle constraint to decrease the flight risk and obtain the smoother path. For performance evaluation, the MSGSA is applied to two typical simulated flight missions with complex flight environments, including user-defined forbidden flying areas, Radar, missile, artillery and anti-aircraft gun. The obtained flight paths are compared with that of the standard Gravitational Search Algorithm (GSA) and two improved variants of GSA, i.e. gbest -guided GSA (GGSA), and hybrid Particle Swarm Optimization and GSA (PSOGSA). The experimental results demonstrate the superiority of the MSGSA based method in terms of the solution quality, robustness, as well as the constraint-handling ability.

Highlights

Unmanned aerial vehicle (UAV) is an aircraft that can be remotely controlled or can fly autonomously based on a preprogrammed flight path without pilots onboard [1]
We propose an adaptive alpha-adjusting strategy to adjust the value of α based on the evolutionary state of the global best particle
In the proposed Mixed-Strategy based Gravitational Search Algorithm (MSGSA), an adaptive alpha-adjusting strategy is presented first based on the evolutionary state of the global best particle to keep the balance between exploration and exploitation

Summary

Introduction

Unmanned aerial vehicle (UAV) is an aircraft that can be remotely controlled or can fly autonomously based on a preprogrammed flight path without pilots onboard [1]. The path planning can be treated as a global optimization problem with various constraints from the certain mission, environment and UAV physical property [5]. A number of environmental constraints (such as flight length and threat constraints) and UAV’s self-constraints (such as flight altitude and turning angle constraints) should be considered. To solve this global optimization problem, different approaches based on the graph-theory [6], the mathematical programming [7], and the bi-level programming Liu et al [8] have been proposed

Methods

Results

Discussion

Conclusion