Autonomous Fault-Tolerant Operation of Redundant Robotic Arms

极端温度、湿度、辐射和其他危险条件的环境通常会增加机器人执行器硬件故障的潜在风险。导致机器人关节频繁故障的一个共同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、核废料补救和灾难救援时，不仅更有可能发生故障，而且发生故障后也不可能对这些机器人进行常规维护。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-机器人交互，也可能导致非常严重的后果或事故。为了提高机器人的鲁棒性，本项目将基于预测和识别的关节故障来开发冗余机械臂的自主容错和故障主动控制策略，这将极大地提高冗余机械臂的可靠性和鲁棒性。最优容错机器人的运动学设计和预测所有潜在故障的容错运动规划方法可以保证任务的完成和故障后的最佳性能。特别是，针对同时包含体积和形状信息的六维容错工作空间，提出了一种新的高效计算方法。然后，根据容错工作空间的体积和形状来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关节的失败概率相等，并且只能提供局部最优解。在本研究中，基于预测的关节失效概率开发了失效后的性能度量，并将引入实时运动规划算法来计算全局最优轨迹。最后，预测和诊断算法与所有传统方法的不同之处在于，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，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该项目也是由既定的激励竞争性研究计划(EPSCoR)共同资助的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.