Drone Path Planning Based on an Improved Whale Optimisation Algorithm

To address the issues of insufficient convergence performance and high sensitivity to local optima in the traditional Whale Optimization Algorithm (WOA) when handling 3D path planning tasks for unmanned aerial vehicles (UAVs), this paper proposes an improved UAV path planning algorithm based on the Whale Optimization Algorithm (R*WOA). Firstly, the global search capability and path optimisation mechanism of the Rapidly Expanding Random Tree Star (RRT*) algorithm are utilised to generate a high-quality initial population, thereby enhancing population diversity and the algorithm’s global exploration capability; Secondly, the linear convergence factor of the traditional WOA is adjusted to a non-linear dynamic adjustment strategy based on the cosine function, enhancing global search capability in the early stages of iteration and local search capability in the later stages; simultaneously, a non-linear inertial weight is employed to modulate the position update mechanism of individuals, further enhancing the algorithm’s local optimisation accuracy and convergence stability in the later stages of iteration. Finally, comparative experimental results on a basic test function set and in scenarios constructed using Digital Elevation Models (DEMs) demonstrate that R*WOA exhibits stable optimisation performance, capable of planning safer paths that are shorter in length and smoother in trajectory.