Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

Abstract

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.

Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

“…The maximum overlapping window length is limited to 300 ms [46]. The approach developed by M. Kunapipat et al for classifying hand gestures achieved the highest prediction accuracy with a half-overlapping size temporal length [47].Similarly, C.…”

EMG Characterization and Processing in Production Engineering

“…The first group seeks to replace the traditional equipment control channels through new interfaces based on EMG, providing a more intuitive and effective control system. The analyzed experimental studies are carried out in simulated environments and combine EMG signals with other control systems and equipment, such as the HoloLens VR Kit [106] or an industrial joystick [107]. See Tables 3 and 4.…”

“…Hand gestures control the speed of the robot, while the orientation is controlled with the virtual reality glasses, establishing a straightforward and natural control system. Palar et al [107] introduce a novel HMI control system to adapt the autonomy of a climbing robot used for inspecting weld beads in storage tanks according to the operator´s experience. This robot operates at temperatures close to 100 degrees Celsius during inspections.…”

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“…With some experiments, it has been proved that this controller can be controlled effectively in the process of sewing tracking and in the process of joining sewing. Hongyuan Shen [2][3] developed a segmented adaptive PID controller based on the analysis of the motion mechanism and time difference of the real-time change of the robot, so that the robot can be stable and adjust the position of the welding torch during the process. weld seam.…”