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.

Efficient path planning is vital for multi-UAV inspection missions, yet the comparative effectiveness of different optimization strategies has not received much attention. This paper introduces the first application of the Genetic Algorithm (GA) and Hill Climbing (HC) to multi-UAV inspection of indoor pipelines, providing a unique comparative analysis. GA exemplifies the global search strategy, while HC illustrates an enhanced stochastic local search. This comparison is impactful as it highlights the trade-offs between exploration and exploitation—two key challenges in multi-UAV path optimization. It also addresses practical concerns such as workload balancing and energy efficiency, which are crucial for the successful implementation of UAV missions. To tackle common challenges in multi-UAV operations, we have developed a novel repair mechanism. This mechanism utilizes problem-specific repair heuristics to ensure feasible and valid solutions by resolving redundant or missed inspection points. Additionally, we have introduced a penalty-based approach in HC to balance UAV workloads. Using the Crazyswarm simulation platform, we evaluated GA and HC across key performance metrics: energy consumption, travel distance, running time, and maximum tour length. The results demonstrate that GA achieves a 22% reduction in travel distance and a 23% reduction in energy consumption compared to HC, which often converges to suboptimal solutions. Additionally, GA outperforms HC, Greedy, and Random strategies, delivering at least a 13% improvement in workload balancing and other metrics. These findings establish a novel and impactful benchmark for comparing global and local optimization strategies in multi-UAV tasks, offering researchers and practitioners critical insights for selecting efficient and sustainable approaches to UAV operations in complex inspection environments.

Inspection plays a crucial role in ensuring the longevity, security, and dependability of critical public infrastructure for both governments and businesses. However, traditional inspection processes are often labor-intensive and pose various risks. Consequently, there is a growing need for automation in such tasks. This research paper presents a comprehensive dataset that can be utilized to develop algorithms and systems for automating the inspection process, a critical area in the field of computer vision. The dataset encompasses a diverse range of inspection scenarios and serves as a valuable resource for advancing automation technology specifically for the inspection of steel pipes to detect corrosion defects. Real-life pipe maps have been used to derive scenarios that represent varying levels of corrosion. By leveraging this dataset, researchers and practitioners can contribute to the development of more efficient and accurate automated inspection systems, thus greatly improving the overall efficiency and long-term safety of infrastructure inspection.