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A Flexible Wearable Supernumerary Robotic Limb for Chronic Stroke Patients
Published on: October 27, 2023
Neural and fuzzy robotic hand control
1Dept. of Electr. Eng., Binghamton Univ., NY.
Summary
This study presents an efficient robotic grasp for wheelchair users, combining neural networks and fuzzy logic. This hybrid system learns from touch and slip experiences for improved object manipulation.
Area of Science:
- Robotics
- Artificial Intelligence
- Biomedical Engineering
Background:
- Wheelchair users often face challenges with object manipulation.
- Existing robotic grippers may lack adaptability and fine motor control.
- The integration of sensory feedback is crucial for dexterous robotic actions.
Purpose of the Study:
- To develop and present an efficient first grasp strategy for a wheelchair robotic arm-hand system.
- To create a hybrid control algorithm combining neural networks and fuzzy logic for grasp learning.
- To outline neurofuzzy modifications and demonstrate steps for physical implementation.
Main Methods:
- A hybrid control algorithm integrating neural networks and fuzzy logic was developed.
- The system learns from tip and slip control experiences.
- Object approach vector selection utilizes fuzzy data and an expert supervisor.
- A diagnostic neural controller for tip and slip detection was trained.
Main Results:
- The study outlines the methodology for an efficient robotic grasp.
- Neurofuzzy modifications are detailed for adaptive control.
- The approach focuses on learning from real-time sensory feedback.
- Preparation for physical implementation of the robotic grasp is demonstrated.
Conclusions:
- The proposed hybrid neurofuzzy approach offers a promising method for efficient robotic grasping.
- This system can enhance object manipulation capabilities for wheelchair users.
- Further development and physical implementation are expected to validate the system's effectiveness.
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