Lifelong reinforcement learning framework for energy-efficient drone delivery

Abstract

Drones are attracting a lot of attention as a new means of logistics delivery to the extent that many companies are already applying drones to their systems. However, the insufficient flight time that is a chronic problem of drones limits the application of drones to the field, and this problem is difficult to solve within a hardware environment mature enough. This work presents a two-step drone delivery framework to improve the drone delivery operation more energy efficiently. The proposed method achieves the energy-efficient drone delivery system through an offline manner that allocates missions to drones using centralized calculation and reinforcement learning to avoid risks of collision and perform path planning in real-time. This paper performs the modeling based on the actual flight data of the drone and implements the simulation to consider the environmental variation. This work also proposes a reinforcement learning algorithm containing a continual learning technique responding to the changing environment in drone delivery scenarios. The proposed method achieves near-optimal energy consumption compared with the optimal solution of centralized calculation through energy-efficient drone delivery, including task assignment and path planning using reinforcement learning.