NRI: Collaborative Research: Versatile Locomotion: From Walking to Dexterous Climbing with a Human-Scale Robot

NRI：协作研究：多功能运动：使用人体规模的机器人从步行到灵巧攀爬

• 批准号: 1525889
• 负责人: Mark Cutkosky
• 金额: $ 45万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525889&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Versatile; Locomotion

The project aims to give legged robots the skills to navigate a wide variety of terrain. This capability is needed to employ robots in applications such as search-and-rescue, construction, and exploration of remote environments on Earth and other planets. The multidisciplinary team, composed of researchers at Duke, Stanford, UC Santa Barbara, JPL, and Motiv Robotics, will develop a robot to climb a variety of surfaces ranging from flat ground to overhanging cliffs. Using an array of sensors, unique hands, and sophisticated algorithms, the robot will dynamically adopt walking, crawling, climbing, and swinging strategies to traverse wildly varied terrain. During the course of this research, the team hopes to achieve the milestone of the first demonstration of a human-scale rock climbing robot. The research is also expected to lead to insights into cognitive and biomechanical processes in human and animal locomotion.Although rock climbing serves as an ideal proving ground for the work, this project conducts basic research to address more a general-purpose goal; namely, to provide the physical and cognitive skills for robots to adaptively navigate varied terrain. It takes a dexterous climbing approach, which uses non-gaited, coordinated sequences of contact to move the body, much as dexterous manipulation uses contact with the fingers and palm to move an object. It will apply principles from optimization, machine learning, bioinspiration, and control theory to make intellectual contributions in several domains, such as robot hand design, planning algorithms, balance strategies, and locomotion performance measurement. Novel grippers, sensor-based planning strategies, reactive maneuvers, and locomotion metrics will be developed during the course of this research.

该项目旨在赋予腿式机器人导航各种地形的技能。 在搜索和救援、建筑以及地球和其他行星上的偏远环境探索等应用中使用机器人需要这种能力。这个多学科团队由杜克大学、斯坦福大学、加州大学圣塔芭芭拉分校、喷气推进实验室和 Motiv Robotics 的研究人员组成，将开发一种机器人，可以攀爬各种表面，从平坦的地面到悬垂的悬崖。使用一系列传感器、独特的手和复杂的算法，机器人将动态地采用行走、爬行、攀爬和摆动策略来穿越各种各样的地形。在这项研究过程中，该团队希望实现首次展示人体规模攀岩机器人的里程碑。该研究预计还将深入了解人类和动物运动的认知和生物力学过程。尽管攀岩是这项工作的理想试验场，但该项目进行基础研究是为了解决更多通用目标；也就是说，为机器人提供身体和认知技能，以适应不同的地形。它采用灵巧的攀爬方法，使用非步态、协调的接触序列来移动身体，就像灵巧的操纵使用手指和手掌的接触来移动物体一样。 它将应用优化、机器学习、生物启发和控制理论的原理，在机器人手设计、规划算法、平衡策略和运动性能测量等多个领域做出智力贡献。 在这项研究过程中，将开发新颖的夹具、基于传感器的规划策略、反应性机动和运动指标。