Collaborative Research: Kinematic Reductions for Underactuated Mechanical Systems

合作研究：欠驱动机械系统的运动学简化

• 批准号: 0301567
• 负责人: Kevin Lynch
• 金额: $ 19万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0301567&HistoricalAwards=false
• 关键词: Collaborative; Research; Kinematic; Reductions; Underactuated

One of the most fundamental capabilities for an autonomous orsemi-autonomous robot system is the ability to quickly plan andreliably execute its own motions. We will study these fundamentalproblems for underactuated mechanical systems -- systems with feweractuators than degrees-of-freedom. Our interest in controlling thisclass of systems stems from two considerations. (1) It allowssoftware control redundancy (as opposed to mechanical or actuationredundancy) when one or more actuators of a fully actuated systemfails. (2) It is possible to design inexpensive mechanical systems byminimizing expensive mechanical elements such as actuators andtransmissions and replacing them with advanced control algorithms.We will explore a novel concept for mechanical control systems calledkinematic reductions. A kinematic reduction of a mechanical system isa first-order driftless system whose trajectories can be followed bythe second-order mechanical system. Kinematic reductions allowkinematic constraints (such as obstacles and joint limits) andactuator limits to be handled in a computationally efficient manner,allowing the possibility of real-time trajectory generation forunderactuated systems. Kinematic reductions encode the notion of``preferred'' motion directions for the system, allowing the plannedtrajectories to be followed quickly.To make full use of these properties of mechanical systems forreal-time trajectory generation and control, we will study a number ofopen issues. These include understanding when the kinematic reductionallows for closed-form calculation of motion plans for underactuatedsystems; adapting efficient kinematic motion planners from therobotics literature to kinematically controllable systems; localtrajectory optimization; feedback control to stabilize trajectories ofthe kinematic reductions; and implementation and validation of thecontrol strategies on an experimental vehicle. We also plan toexplore the role kinematic reductions will play in developingreduced-complexity hierarchical hybrid motion models.

自主或半自主机器人系统最基本的能力之一是能够快速规划并可靠地执行自己的运动。 我们将研究欠驱动机械系统（执行器数量少于自由度的系统）的这些基本问题。 我们对控制此类系统的兴趣源于两个考虑。 (1) 当完全驱动系统的一个或多个执行器发生故障时，它允许软件控制冗余（与机械或驱动冗余相对）。 (2) 通过最小化执行器和变速器等昂贵的机械元件并用先进的控制算法替代它们，可以设计廉价的机械系统。我们将探索一种称为运动学简化的机械控制系统新概念。 机械系统的运动学简化是一阶无漂移系统，其轨迹可由二阶机械系统遵循。 运动学简化允许以计算有效的方式处理运动学约束（例如障碍物和关节限制）和执行器限制，从而可以为欠驱动系统生成实时轨迹。 运动学简化编码了系统“首选”运动方向的概念，允许快速遵循计划的轨迹。为了充分利用机械系统的这些特性进行实时轨迹生成和控制，我们将研究许多开放问题。 其中包括了解运动学简化何时允许对欠驱动系统的运动计划进行封闭式计算；将有效的运动规划器从机器人学文献改编为运动学可控系统；局部轨迹优化；反馈控制以稳定运动减速的轨迹；以及控制策略在实验车辆上的实施和验证。 我们还计划探索运动学简化在开发复杂性降低的分层混合运动模型中所发挥的作用。