CRII: RI: Practical Algorithms for Robust Feedback Motion Planning Through Contact

CRII：RI：通过接触进行鲁棒反馈运动规划的实用算法

• 批准号: 1657186
• 负责人: Scott Kuindersma
• 金额: $ 17.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657186&HistoricalAwards=false
• 关键词: CRII; RI; Practical; Algorithms; Robust

Despite significant progress in robot motion planning and control, modern robots still struggle to operate robustly in the presence of unplanned disturbances, state uncertainty, and model errors. Indeed, the recent DARPA Robotics Challenge dramatically demonstrated that some of the world's most advanced robots still minimize contact with their environments and fail often in realistic operating scenarios. This creates a significant barrier to unleashing robots into critical disaster response, exploration, and industrial applications. Algorithms that explicitly reason about robustness require a coupling of motion planning and feedback design, in which the system's closed-loop response to disturbance sources is optimized. Due to the often heavy computational demands of solving such problems, their application to modern field robots has so far been limited.The research objective of this proposal is to address the theoretical, computational, and applied challenges of robust motion planning for robots with nonlinear dynamics and state and input constraints, including constraints arising from frictional contact. The algorithms under consideration in this research will build on direct trajectory optimization methods to simultaneously 1) support complex state constraints and rigid-body contacts and 2) exploit mathematical structure in disturbance and feedback controller sets to construct computationally-efficient algorithms. The ability of these algorithms to improve stability in tasks of broad interest, such as bipedal walking, will be investigated. Algorithms will be evaluated against state-of-the-art methods in locomotion and manipulation experiments on physical robots.

尽管在机器人运动规划和控制方面取得了重大进展，但现代机器人在存在计划外干扰、状态不确定性和模型误差的情况下仍然难以实现鲁棒性操作。事实上，最近的DARPA机器人挑战赛戏剧性地表明，一些世界上最先进的机器人仍然尽量减少与环境的接触，并且在实际操作场景中经常失败。这对机器人进入关键的灾难响应、勘探和工业应用造成了重大障碍。明确推断鲁棒性的算法需要运动规划和反馈设计的耦合，其中系统对干扰源的闭环响应是优化的。由于解决这些问题通常需要大量的计算量，它们在现代野外机器人中的应用迄今为止受到限制。本提案的研究目标是解决具有非线性动力学、状态和输入约束（包括摩擦接触产生的约束）的机器人鲁棒运动规划的理论、计算和应用挑战。本研究中考虑的算法将建立在直接轨迹优化方法的基础上，同时1)支持复杂的状态约束和刚体接触；2)利用干扰和反馈控制器集中的数学结构来构建计算效率高的算法。这些算法在诸如双足行走等广泛关注的任务中提高稳定性的能力将被研究。算法将在物理机器人的运动和操作实验中对最先进的方法进行评估。

项目成果

Contact-implicit trajectory optimization using variational integrators

• DOI: 10.1177/0278364919849235
• 发表时间: 2019-10-01
• 期刊: INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
• 影响因子: 9.2
• 作者: Manchester, Zachary;Doshi, Neel;Kuindersma, Scott
• 通讯作者: Kuindersma, Scott

Contact-Implicit Optimization of Locomotion Trajectories for a Quadrupedal Microrobot

• DOI: 10.15607/rss.2018.xiv.041
• 发表时间: 2018-06
• 期刊: ArXiv
• 作者: Neel Doshi;Kaushik Jayaram;Ben Goldberg;Zachary Manchester;R. Wood;S. Kuindersma