Abstract:

Aiming at the global path planning problem of unmanned aerial vehicles (UAVs) in dynamic environments, this paper proposes an improved Harris Hawk optimization algorithm. To address the problem of insufficient search performance in the later stage of the algorithm, an adaptive chaos and core population dynamic partitioning strategy is proposed to improve the searchability of the algorithm in the later stage. The Harris Hawk update formula is modified, and the golden sine strategy is introduced to improve the search efficiency of the algorithm. Then, an adaptive dynamic cloud optimal solution perturbation strategy is integrated to improve the ability of the algorithm to jump out of the local extremum. For the three-dimensional grid path planning problem, a valuation function is established. By calculating the cost of reaching the endpoint for each grid, the algorithm is aided in filtering nodes, allowing it to search for a shorter path. For the problem of the non-smooth path, the path angle is processed by using the cubic B-spline curvefor three times to make the path more suitable for UAV flight. The effectiveness of the improved algorithm is validated by simulation experiments on international standard test functions and static and dynamic grid maps of varying sizes and complexity. The experimental results demonstrate that the proposed algorithm significantly outperforms the control group algorithm. On average, the planned path is shortened by 14.94% and the number of corners is reduced by 53.31%.

Key words: Harris Hawk optimization(HHO), three-dimensional path planning, UAV, dynamic environment, self-adaption