RI: Small: Hierarchical Planning, Estimation, and Control for Hybrid Mechanical Systems

混合机械系统出现在许多应用中，包括跳跃、行走和攀爬机器人，其中与地面的接触发生了变化；滑动转向车辆，库仑摩擦导致粘滑行为；以及与物体互动的假肢装置。所有这些应用都受到非线性、非平滑过渡和不确定性的影响，这些系统需要新的运动规划和控制工具。这项工作利用了机械系统的特定结构来约束不确定性的传播，并开发了最大限度地提高执行鲁棒性的反馈控制器。这项工作建立在运动规划和估计的最先进技术的基础上，包括基于样本和基于优化的规划，从而产生了用于不确定混合机械系统的工具，类似于用于线性系统的控制和估计工具。示例系统用于驱动算法开发以及验证性能。其中包括：1)Monkeybot，一种利用电磁铁和单个电机沿垂直墙壁移动的机器人；2)利用机械接触和跳跃攀爬狭窄通道的跑酷机器人；3)由于粘/滑摩擦效应而经历不连续动力学的滑移驾驶车辆。这项工作的各个阶段都包括本科生和少数民族学生的参与。除了在会议和期刊上传播外，研究结果还在一个公开可见的wiki上传播。

Hybrid mechanical systems arise in many applications, including hopping, walking, and climbing robots where contact with the ground changes; skid-steering vehicles where Coulomb friction introduces stick/slip behavior; and prosthetic devices that interact with objects. All of these applications are influenced by nonlinearity, nonsmooth transitions, and uncertainty, and these systems demand new tools in motion planning and control.This work takes advantage of the specific structure of mechanical systems to bound the propagation of uncertainty and to develop feedback controllers that maximize robustness of execution. The work builds on state-of-the-art techniques in motion planning and estimation, including sample-based and optimization-based planning, leading to tools for uncertain hybrid mechanical systems that are analogous to control and estimation tools used for linear systems.Example systems are used to drive algorithm development as well as to verify performance. These include 1) the Monkeybot, a robot that uses electromagnets and a single motor to locomote along a vertical wall;2) a parkour robot that uses mechanical contact and jumping to climb narrow passages; and 3) a skid-steered vehicle that experiences discontinuous dynamics due to stick/slip friction effects. All phases of this work include participation of undergraduates and minority students. In addition to dissemination in conferences and journals, results are disseminated on a publicly viewable wiki.