An opposition based Gooseneck Barnacle Optimizer for energy-efficient UAV path planning in urban low altitude environments.
Classical path planning algorithms such as A* applied to multi-UAV systems are unable to cope with multi-UAV complex scenarios in urban low altitude. This is due to the limitations in computational speed and convergence at a local optimal which are unable to guarantee the optimal flight energy and mission success rate. For such purposes, the Upgraded Gooseneck Barnacle Optimization (UGBO) algorithm is proposed based on the concept of swarm intelligence meta-heuristic intelligence with three mechanisms of improvements. They are opposing-based learning for better diversity of initial population, self-adaptive population adjust to balancing explore and exploit, and a forbidden strategy to get out of the local optimal. An improved energy consumption model considering aerobic drag, and wind impacts are employed, with clear multi-UAV coordination in the process through horizontal layer and proximity deconfliction in three-dimensional space, so the trajectory will be physically realizable and collision-free. Simulation in a 100 m × 100 m × 50 m simulation environment including fifteen to twenty static obstacles, and three to five dynamic obstacles show the UGBO achieve the path length nine hundred fifty meters, flight energy four hundred eighty units, calculation time twelve seconds, and average optimal fitness 0.85. These are, compared to other three classical algorithms A*, ACO, and PSO, improved by 19.5-33.1%, 19.3-33.3%, and 25.0-112.5%, respectively. Also, comprehensive experimental comparison with the state-of-the-art research such as PR-DQN, APF-RRT, T-DRL, and AHMP demonstrates that UGBO perform well with 3.1-17.4% improvement from those four approaches. Through ablating test, the study show that the improvement of opposition-based learning plays the most significant role in all three mechanisms. The results show an efficient multi-UAV path planning for urban aerial vehicles that outperform current solutions and can be readily integrated into learning-based approaches and deployed on physical hardware.