An Evaluation of Edge Computing Platform for Reliable Automated Drones

This paper evaluates the edge computing platform for the drone backup system, which enhances the reliability of automated drones. The drone backup system is assumed to be alternate to execute the critical applications, which used to be executed on edge or cloud, such as image recognition, path planning, etc. Since the drone is facing severe conditions in terms of computational capability, battery capacity, etc., the performance and energy consumption are key issues to support the operation of automated drones. In this paper, we measure the execution time and energy consumption on Raspberry Pi with Intel Neural Compute Stick 2 accelerator for three practical applications: Single Shot MultiBox Detector, State Lattice Planner, and Pix2Pix. The experimental results show the performance and energy consumption on the practical scenarios for the drone backup system. Based on these knowledge, the design optimization of the drone backup systems will be performed for safer drones.