On the Accuracy of Near-Optimal GPU-Based Path Planning for UAVs

Path planning is one of the key functional blocks for any autonomous aerial vehicle (UAV). The goal of a path planner module is to constantly update the route of the vehicle based on information sensed in real-time. Given the high computational requirements of this task, heterogeneous many-cores are appealing candidates for its execution. Approximate path computation has proven a promising approach to reduce total execution time, at the cost of a slight loss in accuracy. In this work we study performance and accuracy of state-of-the-art, near-optimal parallel path planning in combination with program transformations aimed at ensuring efficient use of embedded GPU resources. We propose a profile-based algorithmic variant which boosts GPU execution by up to ≈ 7x, while maintaining the accuracy loss below 5%.