为差异极大的四旋翼飞行器学习单一近悬停位置控制器
Learning a Single Near-hover Position Controller for Vastly Different Quadcopters
AI总结：
本研究提出一种基于双神经网络的四旋翼自适应近悬停位置控制器，可在线适配不同硬件参数与未知扰动，仿真训练后直接部署到质量差4.5倍的真实四旋翼上，性能优于现有自适应控制器及定制PID控制器。
摘要：
本文提出了一种适用于四旋翼飞行器的自适应近悬停位置控制器，该控制器可部署在质量、尺寸和电机常数差异极大的四旋翼飞行器上，且在运行时能快速适应未知扰动。核心算法思想是学习单一策略，该策略在测试时可在同一框架内在线适应施加于无人机的扰动，同时也能适应机器人的动力学特性与硬件参数。我们通过训练一个神经网络来估计机器人与环境参数的隐层表征，以此来调节同样由神经网络表征的控制器的行为。我们完全在仿真环境中训练这两个网络，训练目标是让四旋翼飞行器飞向目标位置并避免坠地。我们将仿真中训练得到的同一控制器未经任何修改直接部署到真实世界的两架四旋翼飞行器上，这两架飞行器在质量、尺寸、电机和螺旋桨方面均存在差异，质量相差达4.5倍。此外，我们还验证了该控制器能快速适应最高达四旋翼飞行器质量三分之一的突发大幅扰动。我们在仿真和物理环境中均开展了全面评估，结果表明该控制器的性能优于当前最先进的基于学习的自适应控制器，以及为每个平台单独调试的传统PID控制器。视频结果可查看：https://youtu.be/U-c-LbTfvoA。
英文摘要：
This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters in the real world with differences in mass, size, motors, and propellers with mass differing by 4.5 times. In addition, we show rapid adaptation to sudden and large disturbances up to one-third of the mass of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://youtu.be/U-c-LbTfvoA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
