Bio-Inspired Multi-UAV Path Planning Heuristics: A Review

Faten Aljalaud, Heba Kurdi () and Kamal Youcef-Toumi
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Faten Aljalaud: Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Heba Kurdi: Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Kamal Youcef-Toumi: Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA

Mathematics, 2023, vol. 11, issue 10, 1-35

Abstract: Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.

Keywords: metaheuristics; bio-inspired algorithms; unmanned aerial vehicle; multi-UAV; path planning; ACO; PSO; genetics algorithms; gray wolf optimization; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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