Energy-Efficient UAV Path Planning Using PSO-ABC Algorithm in Obstacle-Rich Environments

A unique hybrid approach for energy-efficient path planning in two-dimensional landscapes with obstacles is presented in this article, combining Artificial Bee Colony (ABC) along with Particle Swarm Optimisation (PSO). Path planning is a critical challenge in robotics, autonomous vehicles, and various other fields. While numerous algorithms exist, many struggle to balance computational efficiency, solution quality, and energy considerations. In order to overcome these obstacles, We constructed a hybrid PSO-ABC algorithm that combines the benefits of PSO's global search capabilities with ABC's local search strengths. The algorithm optimizes paths considering multiple objectives: path length, obstacle avoidance, and energy consumption. We represent paths using a series of handle points, employing spline interpolation for smooth trajectories. The al-gorithm initializes solutions using both random and straight-line approaches, then iteratively improves them through particle up-dates and bee phases. We introduce an adaptive inertia weight for PSO and incorporate energy consumption into the cost function. Experimental results demonstrate the algorithm's effectiveness in finding feasible, energy-efficient paths in environments with randomly placed circular obstacles. The hybrid approach shows improved convergence compared to standalone PSO or ABC algorithms. Furthermore, the inclusion of energy considerations in path optimization presents a more realistic model for real-world applications. This research enriches the field of intelligent path planning by providing a stable, energy-conscious algorithm that works well in challenging settings.