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.

This paper presents a novel path planning heuristic for multi-UAV pipe inspection missions inspired by the booby bird’s foraging behavior. The heuristic enables each UAV to find an optimal path that minimizes the detection time of defects in pipe networks while avoiding collisions with obstacles and other UAVs. The proposed method is compared with four existing path planning algorithms adapted for multi-UAV scenarios: ant colony optimization (ACO), particle swarm optimization (PSO), opportunistic coordination, and random schemes. The results show that the booby heuristic outperforms the other algorithms in terms of mean detection time and computational efficiency under different settings of defect complexity and number of UAVs.

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