CAREER: Legged Locomotion Across Scales: Closing the Loop Between Task Planning and Motion Control

职业：跨尺度的腿式运动：关闭任务规划和运动控制之间的循环

• 批准号: 1350721
• 负责人: Ioannis Poulakakis
• 金额: $ 43.78万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350721&HistoricalAwards=false
• 关键词: CAREER; Legged; Locomotion; Across; Scales

This award seeks to introduce tightly integrated locomotion control and motion planning strategies for agile, highly-mobile legged robots with radically different sizes and morphologies. To realize the potential of these machines in real-world applications, basic movements must be composed to synthesize more complex locomotion behaviors that achieve desirable high-level planning objectives. As dynamic legged robots are becoming increasingly more capable, the need for hierarchically consistent locomotion planning strategies that translate descending task-level commands to suitable low-level control actions that harness the platform's locomotion capabilities becomes pressing. To address this need, this effort pursues new directions (i) in dynamic legged locomotion, by offering a portable library of locomotion primitives; (ii) in robot control, by providing constructive feedback reduction strategies and stochastic data-driven methods that map complex legged robot platforms to behavior-encoding target models; and (iii) in hybrid systems, by introducing a framework for complexity reduction through multiple layers of information processing and control action. This research effort seeks to enable legged machines to perform real-world tasks reliably and efficiently. This way, it promotes many different applications, including industrial, agricultural, and emergency response applications that require highly mobile and versatile robots. This effort also includes substantial educational and community outreach components, aiming at attracting underrepresented groups to science, technology, engineering, and mathematics. In addition, by pairing high-school teachers with graduate students, this effort addresses the critical need for K-12 teachers to stay current and articulate their teaching with the demands of college courses.

该奖项旨在引入紧密集成的运动控制和运动规划策略，用于具有完全不同大小和形态的灵活、高度移动的腿部机器人。为了实现这些机器在实际应用中的潜力，必须合成基本运动，以合成更复杂的运动行为，以实现期望的高级规划目标。随着动态腿式机器人的能力越来越强，对层次化一致的运动规划策略的需求变得迫切，这些策略将下行的任务级命令转换为适当的低级控制动作，从而利用平台的运动能力。为了满足这一需求，这项工作寻求新的方向(I)在动态腿部运动方面，通过提供可移植的运动原语库；(Ii)在机器人控制方面，通过提供建设性的反馈简化策略和随机数据驱动方法，将复杂的腿部机器人平台映射到行为编码目标模型；以及(Iii)在混合系统中，通过多层信息处理和控制动作引入降低复杂性的框架。这项研究工作旨在使腿部机器能够可靠而高效地执行现实世界的任务。通过这种方式，它促进了许多不同的应用，包括工业、农业和紧急响应应用，这些应用需要高度机动和多功能的机器人。这项努力还包括大量的教育和社区宣传部分，旨在吸引代表人数不足的群体学习科学、技术、工程和数学。此外，通过将高中教师与研究生配对，这一努力解决了K-12教师与时俱进的迫切需要，并将他们的教学与大学课程的要求清楚地联系起来。