A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

Abstract

Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…For any given non-deterministic polynomial complex problem (i.e., the navigation path of a UAV), mathematical optimization-based AI approaches achieve near-optimal solutions [106]. The most dominant algorithms in this field are Particle Swarm Optimization (PSO) [107], Ant Colony Optimization (ACO) [108], Genetic Algorithm (GA) [109], Differential Evolution (DE) [110], and Gray Wolf Optimization (GWO) [111], as assessed in Table 2 and defined below:…”

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

“…The algorithm is in the form of a hierarchical ordering that is something like the structure of social hierarchy in wolf packs. This functional nature of the algorithm extends its adaptability and efficiency in tackling the specific optimization problems that are particular to WSNs [8][9][10][11].…”

Dynamic Task Allocation for Heterogeneous Multi-UAVs in Uncertain Environments Based on 4DI-GWO Algorithm

“…At the level of intelligent algorithmic solutions, there is a growing body of literature that applies the grey wolf optimization (GWO) algorithm to address the optimization problems associated with UAVs. The works in [27][28][29][30][31] demonstrated the efficacy of the GWO algorithm in addressing optimization issues across diverse domains. Among these, literature [27] conducted research on the specific trajectory planning problem of bridge inspection, while literature [28][29][30][31] proposed different improvement strategies for the convergence speed and search capability of the GWO algorithm.…”

“…The works in [27][28][29][30][31] demonstrated the efficacy of the GWO algorithm in addressing optimization issues across diverse domains. Among these, literature [27] conducted research on the specific trajectory planning problem of bridge inspection, while literature [28][29][30][31] proposed different improvement strategies for the convergence speed and search capability of the GWO algorithm. Among them, the work in [28] improved the grey wolf search strategy to enhance the convergence speed and combined it with the implementation of variability in the differential evolution algorithm to promote the search capability.…”

“…Among them, the work in [28] improved the grey wolf search strategy to enhance the convergence speed and combined it with the implementation of variability in the differential evolution algorithm to promote the search capability. And the work in [29] put forward a novel relative distance adaptation strategy with the objective of enhancing the convergence speed. This was combined with the simulated annealing algorithm for alternative position updates, with the intention of improving the search process with different capabilities.…”