Intelligent Haptic Controls for Robotic Teleoperation

用于机器人远程操作的智能触觉控制

• 批准号: RGPIN-2014-03927
• 负责人: Macnab, Chris
• 金额: $ 1.82万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658349
• 关键词: Intelligent; Haptic; Controls; Robotic; Teleoperation

Computer automation of tasks often implements "control systems", like a car's cruise-control that keeps the speed constant by controlling the throttle. A simple feedback control decides on the control input by multiplying the output error by a negative constant; in the case of the cruise control the throttle control is proportional to the error in speed. More accurate feedback control laws are normally achieved by performing more complex mathematical operations on the error signal, especially integration and differentiation. On the other hand, my research focuses on improving control systems through biologically inspired methods, specifically by utilizing the Cerebellar Model Arithmetic Computer or CMAC, a computer algorithm modelled on the cerebellum in the human brain. Such "artificial neural networks" incorporate a learning and memorization aspect into the control, resulting in adaptation to unknown variables (like the weight of the passengers in the car in the cruise control example). In this proposal, I will apply the CMAC to the new and exciting area of robotic "haptics" - the control of robotic manipulator arms through hand controllers that let people feel and/or command the forces. Applications range from the Canadarm2/Dextre to submarine manipulators to surgical robots. Most hand-controlled robotic systems only let people control movement (position or speed) while relying strictly on their eyes to know what the robot is doing. Some contemporary robotic systems now let people control the robot while also feeling the forces encountered by the robot; the sense of touch provides a more intuitive interaction resulting in faster and more accurate manipulation. Current control system technology, using mathematical feedback laws, may not produce a satisfactory feel to the user under certain conditions, like when there are time-delays in the system or the environment changes suddenly. Consider a surgical robot, which ideally should be able to move outside the patient, perform surgical tasks with human tissue, and push against bone (and transition between these tasks) without difficulty even when the surgeon does not feel the forces at exactly the same time as the robot. With current technology, surgical robots with haptics are designed for one particular type of task and can do another only with great difficulty, or not at all. Particular problems that can be encountered include sharp abrupt movements, unwanted vibrations, excessive movement during punctures, and excessive forces during collisions. I will use the CMAC to improve the control systems, so that the robot will naturally adapt its motions/forces to different environments while, at the same time, reacting more like a person would. By relying more on the CMAC's memorization capabilities rather than only on measurements of error, the motions can be made much smoother with less, or no, vibrations. It also becomes possible to smoothly switch between two different CMAC controls, so that during puncture or collision a smooth transition can be made between appropriate controls - avoiding extreme movement or forces. It is of utmost importance that the CMAC controls be developed within a mathematical framework that guarantees stability in the presence of the small unavoidable time delays. Because people will experience the system as more intuitive, they will require less formal training beforehand and experience less stress when controlling the system. Project NeuroArm at Foothills Hospital in Calgary has opened their haptics lab to me, which will give me the opportunity for testing and evaluating my controls using the latest in haptic equipment in close consultation with surgeons who use such systems on a daily basis.

任务的计算机自动化通常实现“控制系统”，就像汽车的巡航控制，通过控制油门来保持速度恒定。 简单的反馈控制通过将输出误差乘以负常数来决定控制输入;在巡航控制的情况下，油门控制与速度误差成比例。 更精确的反馈控制律通常通过对误差信号执行更复杂的数学运算，特别是积分和微分来实现。 另一方面，我的研究重点是通过生物启发的方法改进控制系统，特别是通过利用小脑模型算术计算机或CMAC，这是一种以人脑小脑为模型的计算机算法。 这种“人工神经网络”将学习和记忆方面纳入控制中，从而适应未知变量（例如巡航控制示例中车内乘客的重量）。 在这个建议中，我将CMAC应用到机器人“触觉”的新的和令人兴奋的领域-通过手控制器，让人们感觉和/或命令力的机器人机械臂的控制。 应用范围从Canadarm 2/Dextre到潜艇机械手到手术机器人。大多数手控机器人系统只能让人们控制运动（位置或速度），同时严格依赖眼睛来了解机器人在做什么。一些现代机器人系统现在让人们控制机器人，同时也感受到机器人遇到的力;触觉提供了更直观的交互，从而实现更快，更准确的操作。 当前的控制系统技术，使用数学反馈定律，在某些条件下可能不会产生令人满意的感觉给用户，如当系统中存在时间延迟或环境突然变化时。 考虑一个手术机器人，理想情况下，它应该能够在病人体外移动，用人体组织执行手术任务，并毫无困难地推动骨骼（以及这些任务之间的转换），即使外科医生没有与机器人完全同时感受到力。 根据目前的技术，具有触觉的手术机器人被设计用于一种特定类型的任务，并且只能非常困难地完成另一种任务，或者根本不能完成。 可能遇到的特殊问题包括剧烈的突然运动、不必要的振动、刺穿期间的过度运动以及碰撞期间的过度力。 我将使用CMAC来改进控制系统，这样机器人就可以自然地适应不同的环境，同时更像人一样做出反应。 通过更多地依赖CMAC的记忆能力，而不仅仅是误差测量，运动可以变得更平滑，振动更少，甚至没有振动。 它还可以在两个不同的CMAC控制之间平滑切换，以便在穿刺或碰撞期间在适当的控制之间进行平滑过渡-避免极端运动或力。 最重要的是，CMAC控制的数学框架内，保证在存在的小不可避免的时间延迟的稳定性进行开发。 因为人们将体验到更直观的系统，他们将需要更少的正式培训，并在控制系统时经历更少的压力。卡尔加里山麓医院的NeuroArm项目向我开放了他们的触觉实验室，这将使我有机会使用最新的触觉设备与每天使用此类系统的外科医生密切协商，测试和评估我的控制。