Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

Abstract

In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Analysis of influencing factors of carbon emissions from China’s marine fishery energy consumption under different development scenarios

“…Particle swarm algorithm (PSO) is an algorithm that simulates the feeding behavior of birds (Marini and Walczak, 2015). Particle swarm algorithms have a wide range of applications in optimization problems in areas such as neural network training, combinatorial optimization, image processing and signal processing (Yuan et al, 2019;Huo et al, 2023;Na et al, 2023). The algorithm proposes the concept of particles to simulate the birds in a flock, and the particles learn and exchange information among themselves to achieve the global optimal search (Zhang et al, 2019).…”

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

“…Y.N. et al [33] proposed an improved path planning method by integrating the classical particle swarm optimization (PSO) algorithm with the deep deterministic policy gradient (DDPG) model. Their objective was to generate an optimal path with minimum energy consumption in complex terrain environments.…”

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning

“…Path planning refers to the rapid planning of a safe path under UAV kinematic constraints by taking into account factors such as the terrain, weather, and threats in the search space. Common path planning algorithms are generally classified into three categories [3,9]: (i) search-based methods, such as the A* algorithm [10] and D* Lite algorithm [11]; (ii) sampling-based methods, such as the RRT algorithm [12,13] and the probabilistic roadmap algorithm (PRM) [14]; and (iii) bionic intelligent methods, such as particle swarm optimization (PSO) [15], genetic algorithm (GA) [16], ant colony optimization (ACO) [17], and the plant growth path planning algorithm (PGPP) [18]. Although search-based methods can ensure the optimal solution, the quality of the solution is limited by the resolution of the search space [19].…”