UAV Path Planning using Rotated TOR in Structured Environment

Path planning is one of the crucial criteria for an unmanned aerial vehicle (UAV) in providing a safe path to fly from any location to the specified goal. Global approach to autonomous path planning often used the harmonic potentials technique to guide the planner. The harmonic potentials are essentially the solutions of Laplace's equation that are employed to model the environment. The computational resources required to obtain these harmonic potentials often involve a large number of mathematical calculations. In the previous work, fast iterative methods that apply the use of full-sweep iteration were suggested. This study examines a fast-iterative numerical approach, namely Rotated Two-Parameter Successive Over-Relaxation (RTOR), to UAV path planning. The algorithm is implemented in a self-developed UAVPlanner Simulator where hills are generated randomly in grid areas. The proposed method was tested using several simulation scenarios which demonstrated the efficiency of the algorithm in finding the path, where it was evaluated in terms of the number of iterations and computational time efficiency with the different number of outdoor static obstacles. The results show that RTOR outperformed the previous methods and gives faster computational time and iteration in generating a path for UAVs.