Work

Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer

Year: 2024

Type: article

Abstract: Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj15110531

Authors: Ali Haidar Ahmad, Oussama Zahwe, Abbass Nasser, Benoit Clement

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, École nationale supérieure de techniques avancées Bretagne, VectraWave (France), Holy Spirit University of Kaslik

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,591