CAREER: Motion Capabilities for Autonomous Mobile Manipulation

职业：自主移动操作的运动能力

• 批准号: 0545934
• 负责人: Oliver Brock
• 金额: $ 48万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-15 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0545934&HistoricalAwards=false
• 关键词: CAREER; Motion; Capabilities; Autonomous; Mobile

CAREER: Motion Capabilities for Autonomous Mobile ManipulationAbstractConcerted efforts in industry and academia have lead to the design and construction of highly advanced humanoid robots and mobile manipulators. These mechanisms combine dexterous manipulation capabilities with mobility and are equipped with multi-modal sensor suites. Autonomous motion of these complex mechanisms is subject to a multitude of constraints, each requiring the consideration of feedback at a specific frequency. The investigator proposes to devise a novel motion generation generation approach that addresses motion constraints and their feedback constraints. At the low end of the frequency spectrum, the investigator proposes the application of techniques from active learning and utility theory to global motion planning. These techniques permit the exploitation of structure inherent in any real-world motion-planning problem to increase the efficiency of motion planning. The resulting planning methods will satisfy the feedback requirements of autonomous mobile manipulation. To obtain adequate feedback about the state of the environment, perceptual information is provided by novel sensing and modeling techniques, which will also be developed as part of this effort. To address motion constraints that require high-frequency feedback, the investigator proposes to combine multi-objective control methods with planning approaches for constraint satisfaction. These planning methods overcome the susceptibility of control-based motion generation to local minima. The resulting overall approach to motion generation addresses the motion requirements of autonomous mobile manipulation, offering a significant advance towards the autonomous operation of humanoid robots and mobile manipulators in unstructured environments.

职业：自主移动的操作的运动能力摘要工业界和学术界的共同努力导致了高度先进的仿人机器人和移动的机械手的设计和建造。 这些机制结合了联合收割机灵活的操作能力与机动性，并配备了多模态传感器套件。 这些复杂机构的自主运动受到许多约束，每个约束都需要考虑特定频率的反馈。 研究者提出设计一种新的运动生成方法，解决运动约束及其反馈约束。 在频谱的低端，研究者提出了主动学习和效用理论的技术应用于全局运动规划。 这些技术允许利用任何现实世界的运动规划问题中固有的结构，以提高运动规划的效率。 所得到的规划方法将满足自主移动的操纵的反馈要求。 为了获得关于环境状态的足够反馈，通过新颖的传感和建模技术提供感知信息，这些技术也将作为这项工作的一部分开发。 为了解决需要高频反馈的运动约束，研究者提出将联合收割机多目标控制方法与约束满足规划方法相结合。 这些规划方法克服了基于控制的运动生成对局部最小值的敏感性。 由此产生的整体运动生成方法解决了自主移动的操作的运动要求，提供了一个显着的进步，在非结构化环境中的人形机器人和移动的机械手的自主操作。