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快速规划和可靠地执行自己的运动的能力。我们将研究欠驱动机械系统的这些基本问题——驱动器少于自由度的系统。我们对控制这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失效时，它允许软件控制冗余（与机械或驱动冗余相反）。(2)通过最小化昂贵的机械元件，如执行器和传动装置，并用先进的控制算法代替它们，设计廉价的机械系统是可能的。我们将探讨一个新的概念，为机械控制系统称为运动学减少。机械系统的运动学约简是一阶无漂移系统，其轨迹可以被二阶机械系统跟踪。运动学减少允许以计算高效的方式处理运动学约束（如障碍物和关节限制）和执行器限制，从而为欠驱动系统提供实时轨迹生成的可能性。运动学约简编码了系统“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，我们将研究一些开放的问题。这些包括理解何时运动学简化允许对欠驱动系统的运动计划进行封闭形式的计算；将运动学文献中的高效运动规划器应用于运动学可控系统localtrajectory优化;稳定运动减速轨迹的反馈控制；并在实验车上进行了控制策略的实现与验证。我们还计划探索运动学约简将在开发降低复杂性的分层混合运动模型中发挥的作用。