Adaptive Path Planning and Obstacle Avoidance for Unmanned Aerial Vehicles Using the Indian Spotted Eagle Algorithm

In the era of unmanned aerial vehicles (UAVs), effective navigation strategies are crucial for ensuring successful operations. These strategies enable UAVs to maneuver through complex environments, avoid obstacles, and reach their destinations efficiently while minimizing energy consumption and travel time. This study introduces a novel bio-inspired optimization technique called the “Indian Spotted Eagle Algorithm (ISEA),” which enhances UAV path planning. The algorithm incorporates Cubic Spline Interpolation to ensure smooth and continuous paths, allowing UAVs to navigate efficiently through challenging environments. The study assesses the performance of ISEA in comparison with other bio-inspired optimization techniques through simulations in similar scenarios. The findings reveal that the proposed method significantly surpasses other methods by reducing path length by up to 37%. Additionally, the convergence results show that ISEA is faster than the compared bio-inspired algorithms, indicating its strong practical applicability.