Aerial filming with synchronized drones using reinforcement learning

无人机 计算机科学 强化学习 冗余(工程) 人工智能 基线(sea) 实时计算 操作系统 遗传学 生物 海洋学 地质学
作者
Kenneth C. W Goh,Raymond B. C Ng,Y.Y. Jennie Wong,Nicholas Ho,Matthew Chin Heng Chua
出处
期刊:Multimedia Tools and Applications [Springer Science+Business Media]
卷期号:80 (12): 18125-18150 被引量:8
标识
DOI:10.1007/s11042-020-10388-5
摘要

Usage of multiple drones is necessary for aerial filming applications to ensure redundancy. However, this could inevitably contribute to higher risks of collisions, especially when the number of drones increases. Hence, this motivates us to explore various autonomous flight formation control methods that have the potential to enable multiple drones to effectively track a specific target at the same time. In this paper, we designed a model-free deep reinforcement learning algorithm, which is mainly based on the Deep Recurrent Q-Network concept, for the aforementioned purposes. The proposed algorithm was expanded into single and multi-agent types that enable multiple drones tracking while maintaining formation and preventing collision. The involved rewards in these approaches are two-dimensional in nature and are dependent on the communication system. Using Microsoft AirSim simulator, a virtual environment that includes four virtual drones was developed for experimental purposes. A comparison was made among various methods during the simulations, and the results concluded that the recurrent, single-agent model is the most effective method, being 33% more effective than its recurrent, multi-agent counterparts. The poor performance of the non-recurrent, single-agent baseline model also suggests that the recurrent elements in the network are essential to enable desirable multiple-drones flight.

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