Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments

TL;DR

This paper compares three UAV path planning algorithms—A*, RRT*, and PSO—in complex 3D urban environments, highlighting their strengths and limitations through extensive experiments.

Contribution

It provides a comprehensive experimental comparison of A*, RRT*, and PSO algorithms for UAV navigation in urban 3D spaces, revealing their relative performance.

Findings

A* outperforms others in efficiency and path quality

PSO excels in tight turns and dense environments

RRT* offers a balanced approach across scenarios

Abstract

The most crucial challenges for UAVs are planning paths and avoiding obstacles in their way. In recent years, a wide variety of path-planning algorithms have been developed. These algorithms have successfully solved path-planning problems; however, they suffer from multiple challenges and limitations. To test the effectiveness and efficiency of three widely used algorithms, namely A*, RRT*, and Particle Swarm Optimization (PSO), this paper conducts extensive experiments in 3D urban city environments cluttered with obstacles. Three experiments were designed with two scenarios each to test the aforementioned algorithms. These experiments consider different city map sizes, different altitudes, and varying obstacle densities and sizes in the environment. According to the experimental results, the A* algorithm outperforms the others in both computation efficiency and path quality. PSO is especially suitable for tight turns and dense environments, and RRT* offers a balance and works well across all experiments due to its randomized approach to finding solutions.