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Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition.
Muhammad Syaiful Amri Bin Suhaimi1,2, Kojiro Matsushita2,3, Takahide Kitamura2,3
1Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, Kampar 31900, Malaysia.
Biomimetics (Basel, Switzerland)
|May 23, 2023
Summary
This study enhances robot arm grasping using electrooculography (EOG) gaze estimation combined with camera image processing. A 3 cm threshold improved grasping speed by 27% over a 2 cm threshold, enabling faster and more stable object acquisition.
Area of Science:
- Robotics
- Biomedical Engineering
- Computer Vision
Background:
- Electrooculography (EOG) signals enable gaze estimation for controlling robot arms, particularly in assistive applications.
- EOG signals can lose information through skin, leading to inaccuracies in gaze estimation and object grasping.
- Improving spatial accuracy in EOG-based control is crucial for reliable robot arm manipulation.
Purpose of the Study:
- To develop a system for quick and stable object grasping using a 3D robot arm controlled by EOG signals.
- To enhance the accuracy of EOG gaze estimation by integrating camera-based object recognition.
- To achieve precise object grasping through a combined approach of gaze estimation and image processing.
Main Methods:
- A system integrating a 3D robot arm, cameras, and an EOG measurement analyzer was developed.
- Users controlled the robot arm via camera images, with EOG gaze estimation identifying target objects.
- Object recognition and selection were performed using image processing, identifying the object centroid closest to the estimated gaze position within a set threshold.
Main Results:
- The EOG gaze estimation distance error was found to be in the range of 1.8-3.0 cm.
- Experiments with grasp thresholds of 2 cm and 3 cm were conducted.
- A 3 cm threshold resulted in a 27% faster grasping speed compared to a 2 cm threshold, indicating more stable object selection.
Conclusions:
- Combining EOG gaze estimation with camera-based object recognition significantly improves robot arm grasping accuracy.
- The system demonstrates a practical method for enhancing human-robot interaction and object manipulation.
- Optimizing the distance threshold is key to balancing grasping speed and selection stability in EOG-controlled robot systems.
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