Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in...

Area of Science:

• Robotics and Control Systems
• Rehabilitation Engineering
• Medical Education Technology

Background:

• Current telerehabilitation (TR) models often lack flexibility for multiple participants.
• Adapting multi-lateral teleoperation for varying numbers of users typically requires controller redesign.
• Remote neurorehabilitation can improve patient access and reduce healthcare costs.

Purpose of the Study:

• To propose a novel scheme for multi-lateral remote rehabilitation involving a therapist, patient, and trainees.
• To develop a theoretical method using multi-agent systems (MAS) for flexible and robust teleoperation.
• To enable simultaneous training and therapy in telerehabilitation.

Main Methods:

• Implementation of a multi-agent systems (MAS) based decentralized control architecture.
• Leveraging self-intelligence within MAS to avoid controller redesign when participant numbers change.
• Accounting for operator dynamics uncertainties and time-varying communication delays.

Main Results:

• The proposed MAS framework allows dynamic adjustment of participants without controller redesign.
• The system incorporates tuning matrices (L and D) for adaptability to various multi-lateral teleoperation scenarios.
• Simulated scenarios demonstrated the framework's stability and performance in achieving simultaneous training and therapy.

Conclusions:

• The developed MAS-based scheme offers a stable and adaptable solution for multi-lateral remote rehabilitation.
• This approach enhances the efficiency and accessibility of neurorehabilitation and medical training.
• The framework provides a versatile platform for future advancements in remote healthcare and education.