Multi-sensor Collaborative Remote Control of a Robotic Arm Reflecting User Grasping Intention

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Key Points

This research aims to develop a hybrid remote control system that integrates EMG-based intention recognition and vision-based target estimation to enhance robotic arm operation.
Utilized wearable EMG armbands for intuitive detection of grasping intention via RMS analysis and MVC-based thresholds.
Employed a monocular camera and ArUco markers for precise 6-DoF pose estimation of target objects.
Implemented robotic arm control using Robot Operating System (ROS) for automated movement to target locations.
The EMG-vision hybrid system reduced average task completion time by 44% compared to joystick control.
The system decreased the task failure rate from 18.3% to 1.7%, demonstrating a 10-fold reduction in errors.

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Evaluates a new hybrid EMG-vision control system improving task performance in robotic control.

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Jeong et al. (2026) studied this question.

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