Efficient Path Planning Algorithm for Unmanned Aerial Vehicle in Dynamic Environment

To carry out missions independently and safely, the unmanned aerial vehicles (UAVs) must be capable of doing path-planning that avoids collisions with static or dynamic objects. This study is aimed at providing an effective UAV path planning method to operate safely in a dynamic environment. Our method is built upon three core components: bi-directional rule templates, a hybridization of rapidly exploring random tree stars and ant colony systems algorithm (RRT-ACS), and path planning in configuration space-time. Bi-directional rule templates guide the UAV’s movement by offering two potential paths at any decision point. The RRT-ACS algorithm accelerates convergence to an optimal path. Configuration space-time is utilized for path planning, allowing to account for dynamic obstacles and plan paths that avoid future collisions. Multiple simulations were conducted to validate the proposed method in a dynamic environment. Informed RRT*, RRT*FND, and MOD-RRT*, which are state-of-the-art path planning algorithms, are compared to the performance of the proposed method. Numerous experimental outcomes indicate that the proposed method outperforms alternative comparison algorithms. The findings indicate that the proposed method is applicable to UAVs that must navigate in a dynamic environment.