Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach - Chunmin LIN; Liekang ZENG; Xu CHEN (物联网学报)
Autonomous drone navigation has received growing attention in the recent community.Compared with traditional navigation approaches which rely on location-based services highly, deep learning based visual methods have showed superior performance in self-adaption and generalization, which are a promising solution for autonomous navigation.Running the resource-hungry deep learning execution in the resource-constrained unmanned aerial vehicle (UAV), however, significant challenges were presented in power efficiency.To tackle this challenge, following the idea of edge intelligence, a deep reinforcement learning approach was introduced to dynamically configure the computational scale of the deep learning model on UAV and hence realize the autonomous navigation with low latency and high energy efficiency.Evaluations based on both simulation and real prototype experiments show that the proposed approach has the less energy consumption, longer navigation trail and higher obstacle avoidance rate.