Role of Nonuniformly Quantized Actuation in Biological Motion Generation

非均匀量化驱动在生物运动生成中的作用

• 批准号: 1300019
• 负责人: Jun Ueda
• 金额: $ 28.94万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1300019&HistoricalAwards=false
• 关键词: Role; Nonuniformly; Quantized; Actuation; Biological

The goal of this award is to establish a physiologically inspired framework for recruiting compliant modular actuators for biological movement generation. The research will result in an in-depth understanding of how modularity and variability in the neuromuscular system play key roles in coordinating multiple muscles. The research studies a non-uniform recruitment method for compliant actuator arrays. Floating point quantization (FPQ), a popular numbering scheme in digital communications, appeared as a viable option which mimics aspects of Henneman's size principle of motor unit recruitment. The research hypothesizes that (1) variability in muscle forces and signal-dependency can be characterized by quantized actuation profiles in non-uniform modular actuator system, (2) a quantized actuator recruitment approach reproduces such signal-dependent variability without introducing an artificial source of noise into robotic architecture, and (3) optimization principles in muscle coordination can be modeled mathematically by mapping the variability at the level of individual muscles to the variability at an end-point from a stochastic control perspective. Quantized control methods for recruiting muscle-type robotic actuators for biological trajectory generation will be developed, and resultant motor performance in artificial robotic limbs will be compared to performance in humans.If successful, the research will advance the field of biologically inspired robotics, rehabilitation robotics, computational neuroscience, and character animation. A deeper understanding of neuromuscular physiology will provide more sophisticated computational models, resulting in a novel architecture for robotics that captures the advantages inherent in biological motor systems. Research outcomes will be integrated into current courses including offering a special topic for undergraduate design research. Graduate and undergraduate students will be recruited from interdisciplinary and multicultural groups including under-represented groups. Outreach activities for K-12 students will be conducted through a Robotics Summer Camp program, Georgia Tech the Student and Teacher Enhancement Partnership Program, and FIRST Robotics.

该奖项的目标是建立一个生理学启发框架，用于招募符合生物运动生成的模块化执行器。该研究将导致深入了解神经肌肉系统的模块化和变异性如何在协调多个肌肉中发挥关键作用。研究了柔性作动器阵列的非均匀招募方法。浮点量化（FPQ）是数字通信中流行的一种编号方案，作为一种可行的选择，它模仿了Henneman的运动单元招募尺寸原则。该研究假设：(1)肌肉力和信号依赖性的可变性可以通过非均匀模块化执行器系统中的量化驱动轮廓来表征；(2)量化执行器招募方法再现了这种信号依赖性的可变性，而不会在机器人结构中引入人为噪声源。(3)从随机控制的角度，通过将单个肌肉水平的可变性映射到终点的可变性，可以对肌肉协调的优化原理进行数学建模。将开发用于招募肌肉型机器人执行器以生成生物轨迹的量化控制方法，并将人工机械肢体的运动性能与人类的运动性能进行比较。如果成功，这项研究将推动生物启发机器人、康复机器人、计算神经科学和角色动画领域的发展。对神经肌肉生理学的深入了解将提供更复杂的计算模型，从而为机器人技术提供一种新的架构，这种架构可以捕捉生物运动系统固有的优势。研究成果将整合到现有课程中，包括为本科生设计研究提供一个专题。研究生和本科生将从跨学科和多元文化群体中招募，包括代表性不足的群体。面向K-12学生的拓展活动将通过机器人夏令营项目、佐治亚理工学院学生和教师增强伙伴关系项目以及FIRST Robotics进行。