3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.