Fast path planning for unmanned aerial vehicle using embedded GPU System

Abstract

Unlike commercial airlines that fly predetermined trajectories, military unmanned aerial vehicles (UAVs) operate in dynamic environments and must often adjust their itinerary based on the developing conditions during the mission. The path planner module is a key element of any autonomous UAV. It computes the optimal path from a start point to an end point. In this paper, we present a parallel genetic algorithm for UAV path planning using an embedded NVIDIA Jetson TX1 single-board computer. The path is built as a series of line segment connected by circular arcs to remove discontinuities and to account for the dynamics of fixed wing UAVs. It is optimized to minimize the average altitude avoiding detection by enemy radars and to minimize fuel consumption improving range. The software developed is tested on four different 3D terrains. By exploiting the parallel architecture of the Jetson TX1 GPU, the proposed path planner provides a speedup of 33x compared to a sequential execution on an ARM processor. It calculates quasi-optimal solutions in complex 3D environments in less than 4 seconds and requires only 10 Watts, making it an excellent solution for onboard path planning.