Bio-Inspired Robotics Lab, Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK.
Sci Rep. 2022 Jan 10;12(1):335. doi: 10.1038/s41598-021-04204-9.
The ability to remotely control a free-floating object through surface flows on a fluid medium can facilitate numerous applications. Current studies on this problem have been limited to uni-directional motion control due to the challenging nature of the control problem. Analytical modelling of the object dynamics is difficult due to the high-dimensionality and mixing of the surface flows while the control problem is hard due to the nonlinear slow dynamics of the fluid medium, underactuation, and chaotic regions. This study presents a methodology for manipulation of free-floating objects using large-scale physical experimentation and recent advances in deep reinforcement learning. We demonstrate our methodology through the open-loop control of a free-floating object in water using a robotic arm. Our learned control policy is relatively quick to obtain, highly data efficient, and easily scalable to a higher-dimensional parameter space and/or experimental scenarios. Our results show the potential of data-driven approaches for solving and analyzing highly complex nonlinear control problems.
通过流体介质上的表面流远程控制自由漂浮物体的能力可以促进许多应用。由于控制问题的挑战性，当前对此问题的研究仅限于单向运动控制。由于表面流的高维性和混合，物体动力学的分析建模很困难，而控制问题也很困难，因为流体介质的非线性慢动力学、欠驱动和混沌区域。本研究提出了一种使用大规模物理实验和深度学习强化学习的最新进展来操纵自由漂浮物体的方法。我们通过使用机械臂在水中对自由漂浮物体进行开环控制来演示我们的方法。我们学习的控制策略获取速度快、数据效率高，并且易于扩展到更高维的参数空间和/或实验场景。我们的结果表明，数据驱动方法在解决和分析高度复杂的非线性控制问题方面具有潜力。
