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Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
The International Journal of Robotics Research ( IF 9.2 ) Pub Date : 2020-09-11 , DOI: 10.1177/0278364920953902
Siqi Zhou 1 , Mohamed K Helwa 1, 2 , Angela P Schoellig 1
Affiliation  

High-accuracy trajectory tracking is critical to many robotic applications, including search and rescue, advanced manufacturing, and industrial inspection, to name a few. Yet the unmodeled dynamics and parametric uncertainties of operating in such complex environments make it difficult to design controllers that are capable of accurately tracking arbitrary, feasible trajectories from the first attempt (i.e., impromptu trajectory tracking). This article proposes a platform-independent, learning-based “add-on” module to enhance the tracking performance of black-box control systems in impromptu tracking tasks. Our approach is to pre-cascade a deep neural network (DNN) to a stabilized baseline control system, in order to establish an identity mapping from the desired output to the actual output. Previous research involving quadrotors showed that, for 30 arbitrary hand-drawn trajectories, the DNN-enhancement control architecture reduces tracking errors by 43% on average, as compared with the baseline controller. In this article, we provide a platform-independent formulation and practical design guidelines for the DNN-enhancement approach. In particular, we: (1) characterize the underlying function of the DNN module; (2) identify necessary conditions for the approach to be effective; (3) provide theoretical insights into the stability of the overall DNN-enhancement control architecture; (4) derive a condition that supports data-efficient training of the DNN module; and (5) compare the novel theory-driven DNN design with the prior trial-and-error design using detailed quadrotor experiments. We show that, as compared with the prior trial-and-error design, the novel theory-driven design allows us to reduce the input dimension of the DNN by two thirds while achieving similar tracking performance.

中文翻译:

深度神经网络作为附加模块,用于增强机器人在即兴轨迹跟踪中的性能

高精度轨迹跟踪对许多机器人应用至关重要,包括搜索和救援、先进制造和工业检测等。然而,在这种复杂环境中运行的未建模动力学和参数不确定性使得设计能够从第一次尝试(即即兴轨迹跟踪)准确跟踪任意可行轨迹的控制器变得困难。本文提出了一个独立于平台、基于学习的“附加”模块,以增强黑盒控制系统在临时跟踪任务中的跟踪性能。我们的方法是将深度神经网络 (DNN) 预级联到稳定的基线控制系统,以便建立从所需输出到实际输出的身份映射。先前涉及四旋翼飞行器的研究表明,对于 30 个任意手绘轨迹,与基线控制器相比,DNN 增强控制架构平均减少了 43% 的跟踪误差。在本文中,我们为 DNN 增强方法提供了一个独立于平台的公式和实用的设计指南。特别地,我们:(1)表征 DNN 模块的底层功能;(2) 确定方法有效的必要条件;(3) 为整个 DNN 增强控制架构的稳定性提供理论见解;(4)推导出支持DNN模块数据高效训练的条件;(5) 使用详细的四旋翼飞行器实验将新颖的理论驱动的 DNN 设计与先前的试错设计进行比较。我们表明,与先前的试错设计相比,
更新日期:2020-09-11
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