论文标题
关于空间非合作对象的主动视觉跟踪的深度重复增强学习
On Deep Recurrent Reinforcement Learning for Active Visual Tracking of Space Noncooperative Objects
论文作者
论文摘要
仅依靠视觉摄像机的空间非合作对象的主动跟踪对于自主会合和碎屑去除非常重要。考虑到其部分可观察到的马尔可夫决策过程（POMDP）属性，本文提出了一个基于深度复发的增强学习的新颖跟踪器，被称为Ramavt，驱动追逐的航天器以遵循任意空间的非操作对象，具有高频和近距离的速度控制控制命令。为了进一步提高主动跟踪性能，我们引入了多头注意（MHA）模块和挤压和兴奋（SE）层（SE）层到Ramavt中，这显着提高了神经网络的代表性能力，几乎没有额外的计算成本。与其他最先进的算法相比，在SNCOAT基准上实施的广泛实验和消融研究表明，我们方法的有效性和鲁棒性。源代码可在https://github.com/dongzhou-1996/ramavt上找到。
Active tracking of space noncooperative object that merely relies on vision camera is greatly significant for autonomous rendezvous and debris removal. Considering its Partial Observable Markov Decision Process (POMDP) property, this paper proposes a novel tracker based on deep recurrent reinforcement learning, named as RAMAVT which drives the chasing spacecraft to follow arbitrary space noncooperative object with high-frequency and near-optimal velocity control commands. To further improve the active tracking performance, we introduce Multi-Head Attention (MHA) module and Squeeze-and-Excitation (SE) layer into RAMAVT, which remarkably improve the representative ability of neural network with almost no extra computational cost. Extensive experiments and ablation study implemented on SNCOAT benchmark show the effectiveness and robustness of our method compared with other state-of-the-art algorithm. The source codes are available on https://github.com/Dongzhou-1996/RAMAVT.
