An Improved Plant Growth Algorithm for UAV Three-Dimensional Path Planning

In response to the challenge of three-dimensional path planning for unmanned aerial vehicles (UAVs), this paper introduces a Multi-Strategy Plant Growth Path Planning (MSPGPP) algorithm. Initially, a threat sphere and obstacle avoidance cone model are constructed to ensure safety in avoidance and enhance algorithm accuracy and convergence velocity, while B-spline curves are introduced to improve the quality of the path. Next, the concept of threat level is incorporated along with UAV maneuverability to establish a multi-objective optimization function, enabling the algorithm to plan optimal paths. Finally, an algorithm framework is developed, and comparative simulation results of the improved algorithm against the original algorithm and other optimization algorithms are presented in a three-dimensional environment. The results demonstrate that compared to the original algorithm, the improved algorithm better balances obstacle avoidance safety and UAV maneuverability, planning safer and more effective paths. Additionally, it achieves higher accuracy and faster convergence. Compared to other optimization algorithms, the improved algorithm has better optimization capabilities, higher computational efficiency, and superior path quality.