Improved RRT^* Algorithm-Based Path Planning for Unmanned Aerial Vehicle in a 3D Metropolitan Environment

The unmanned aerial vehicles (UAV’s) are widely used in smart logistic application. The optimal route prediction, however, is a fundamental prerequisite for UAV in commercial applications. This paper introduces an Improved Rapid random tree (IRRT^*) algorithm with triangular inequality rewiring technique for finding collision free path for UAVs in a three-dimensional (3D) environment. The 3D building environments for navigation were developed using MATLAB/Simulink 2021, a virtual occupancy grid model. By considering UAV variable elements such as roll angle, air speed, flight path angle, and boundary threshold parameters, the suggested work aims to provide a comparative analysis of sampling algorithm-based optimal path. The proposed route planning control strategy is to identify the violation free path to locate the destination in 3D environment at variable altitude and air speed. Compared to the standard RRT and RRT^* algorithms, the proposed IRRT^* algorithm can shorten the planning time, reduce the cost distance and improve the algorithm’s applicability in the formation path planning problem. Simulation experiments with two environments and their different situations are carried out to determine the efficiency and performance of the proposed IRRT^* algorithm. Statistical investigation supported the effectiveness of the IRRT^* approach, which has low computational cost and a smooth travel trajectory that significantly resolves the unmanned aerial vehicle path planning issues in logistic applications.