Graduate School of Electrical Engineering and Computer Science (CPGEI), Federal University of Technology-Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, Brazil.
Sensors (Basel). 2020 Oct 21;20(20):5960. doi: 10.3390/s20205960.
This work discusses a novel human-robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator's experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator's arm) to generate a system capable of recognition of the operator's skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes' purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.
本工作讨论了一种用于石化行业储罐焊缝检测的攀爬机器人的新型人机接口。该方法旨在根据操作人员的经验来调整机器人的自主性，其中远程工业操纵杆与肌电臂带配合作为输入。该臂带佩戴在前臂上，可以检测操作人员的手势和手臂的旋转角度。工业操纵杆和臂带的信息用于通过模糊控制器控制机器人。控制器采用滑动自主性（使用工业控制器的角速度、肌电图读数、储罐中焊缝位置和操作人员手臂执行的旋转角度等输入数据）工作，生成一种能够识别操作人员技能并在操作过程中纠正操作人员错误的系统。模糊控制器的输出是机器人要使用的自主性水平。实施的级别包括手动（操作人员控制机器人的角速度和线速度）、共享（操作人员和自主系统共享速度）、监督（机器人控制角速度以保持在焊缝上，操作人员控制线速度）和自主（操作人员定义端点，机器人控制线速度和角速度）。然后，通过在模拟环境中的机器人实验分析这些自主性级别以及所提出的滑动自主性，展示了每种模式的目的。通过实际的不同操作人员在虚拟工业场景中对所提出的方法进行了评估。
