Artificial Hummingbird Algorithm: A Novel Approach for Efficient Path Planning of Drones

Efficient path planning is a crucial aspect of drone operations, enabling them to navigate through complex environments, avoid obstacles, and reach designated destinations while conserving energy and time. This paper presents the “Artificial Hummingbird Algorithm,” a novel bio-inspired approach for optimizing path planning in drones. Inspired by the agile and adaptive flight patterns of hummingbirds, the proposed algorithm combines elements of swarm intelligence, optimization, and dynamic decision-making to create an intelligent and efficient path planning strategy. This study evaluates the performance of the Artificial Hummingbird algorithm against conventional path planning algorithms using a series of simulation scenarios and real-world drone missions. The results demonstrate that the proposed approach significantly outperforms traditional methods in terms of path length optimization, obstacle avoidance, and overall mission completion time. The obtained percentage of deviation between simulation results and real-time results are very less.