Kinodynamic RRT* Based UAV Optimal State Motion Planning with Collision Risk Awareness

Abstract

In this paper, an autonomous navigation strategy is proposed for unmanned aerial vehicles (UAVs) based on consideration of dynamic sampling and field of view (FOV). Compare to search-based motion planning, sampling-based kinodynamic planning schemes can often find feasible trajectories in complex environments. Specifically, a global trajectory is first generated with physical information, and an expansion algorithm is constructed regarding to kinodynamic rapidly-exploring random tree* (KRRT*). Then, a KRRT* expansion strategy is designed to find local collision-free trajectories. In trajectory optimization, bending radius, collision risk function, and yaw angle penalty term are defined by taking into account onboard sensor FOV and potential
risk. Then, smooth and dynamic feasible terms are penalized based on initial trajectory generation. Trajectories are refined by time reallocation, and weights are solved by optimization. Effectiveness of the proposed strategy is demonstrated by both simulation and experiment.