Path planning for a UGV using Salp Swarm Algorithm

Mohammad AlShabi; Khaled Awad Ballous; Ali Bou Nassif; Maamar Bettayeb; Khaled Obaideen; S. Andrew Gadsden

doi:10.1117/12.3013930

7 June 2024 Path planning for a UGV using Salp Swarm Algorithm

Mohammad AlShabi, Khaled Awad Ballous, Ali Bou Nassif, Maamar Bettayeb, Khaled Obaideen, S. Andrew Gadsden

Proceedings Volume 13052, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024; 130520L (2024) https://doi.org/10.1117/12.3013930
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mohammad AlShabi, Khaled Awad Ballous, Ali Bou Nassif, Maamar Bettayeb, Khaled Obaideen, and S. Andrew Gadsden "Path planning for a UGV using Salp Swarm Algorithm", Proc. SPIE 13052, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024, 130520L (7 June 2024); https://doi.org/10.1117/12.3013930

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