Autonomous Fault-Tolerant Operation of Redundant Robotic Arms

冗余机械臂的自主容错操作

• 批准号: 2205292
• 负责人: Biyun Xie
• 金额: $ 49.94万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205292&HistoricalAwards=false
• 关键词: Autonomous; Fault; Tolerant; Operation; Redundant

Environments with extreme temperatures, humidity, radiation, and other hazardous conditions often escalate the potential risk of hardware failures of robot actuators. A common root cause contributing to the frequent robot joint failures is the faults with the joint motors and the associated servo drives. Unfortunately, in unstructured, remote and dangerous environments, such as when robots are deployed for space exploration, nuclear waste remediation, and disaster rescue, not only are failures more likely to occur, but it is also impossible to perform routine maintenance for these robots after a failure occurs. Failures occurring in other safety-critical applications, such as robotic surgery, rehabilitation, and human-robot interaction, could also lead to very serious consequences or accidents. To improve robustness, this project will develop autonomous fault-tolerant and fail-active strategies for redundant robot arms based on predicted and identified joint faults, which will dramatically improve the reliability and robustness of redundant robot arms.Most of the conventional fault-tolerant control methods focus only on failure recovery, and unfortunately it is usually too late to mitigate damages after failures occur. The kinematic design of optimally fault-tolerant robots and fault-tolerant motion planning methods in anticipation of all potential failures can guarantee task completion and optimal post-failure performance. In particular, a novel efficient method is planned to compute the six-dimensional fault-tolerant workspace that includes both the volume and shape information. Then, the optimally fault-tolerant robots will be deigned based on the volume and shape of the fault-tolerant workspace. Additionally, existing fault-tolerant motion-planning algorithms assume that all the joints have equal probability to fail, and they can only provide local optimal solutions. In this research, the post-failure performance measures are developed based on predicted joint failure probabilities, and a real-time motion planning algorithm will be introduced to compute a globally optimal trajectory. Finally, the prognostic and diagnostic algorithms differ from all the conventional approaches in that it does not require any additional sensors or hardware, but still maintains high accuracy and fast prognostic and diagnostic speed.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). This project is also jointly funded by the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

具有极端温度、湿度、辐射和其他危险条件的环境通常会加剧机器人致动器硬件故障的潜在风险。导致频繁机器人关节故障的常见根本原因是关节电机和相关伺服驱动器的故障。不幸的是，在非结构化，偏远和危险的环境中，例如当机器人被部署用于太空探索，核废料修复和灾难救援时，不仅更容易发生故障，而且在故障发生后也不可能对这些机器人进行日常维护。在其他安全关键应用中发生的故障，如机器人手术，康复和人机交互，也可能导致非常严重的后果或事故。为了提高冗余度机器人手臂的鲁棒性，本研究将基于关节故障的预测和识别，开发冗余度机器人手臂的自主容错和失效主动控制策略，这将极大地提高冗余度机器人手臂的可靠性和鲁棒性。最优容错机器人的运动学设计和容错运动规划方法，在预期的所有潜在的故障，可以保证任务的完成和最佳的故障后的性能。特别是，一种新的有效的方法计划计算六维容错工作空间，其中包括体积和形状信息。然后，根据容错工作空间的体积和形状设计最优容错机器人。此外，现有的容错运动规划算法假设所有的关节有相同的概率失败，他们只能提供局部最优解。在这项研究中，故障后的性能指标的基础上预测的关节故障概率，并引入一个实时的运动规划算法来计算一个全局最优的轨迹。最后，预测和诊断算法不同于所有传统方法，因为它不需要任何额外的传感器或硬件，但仍然保持高精度和快速的预测和诊断速度。该项目由跨部门的机器人基础研究计划支持，由工程局（ENG）和计算机和信息科学与工程局（CISE）共同管理和资助。该项目也由激励竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Kinodynamic Motion Planning for Robotic Arms Based on Learned Motion Primitives from Demonstrations

基于从演示中学习到的运动原语的机械臂运动动力学运动规划

• DOI: 10.1109/aim46323.2023.10196178
• 发表时间: 2023
• 期刊: 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM
• 作者: Ashley, Joshua A.;Kennedy, Daniel J.;Xie, Biyun
• 通讯作者: Xie, Biyun