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Updated: Jun 6, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces.

Ana C Lopes¹, Urbano Nunes, Luis Vaz

¹Institute for Systems and Robotics, University of Coimbra, Polo II, 3030-290 Portugal. anacris@isr.uc.pt

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

This study introduces a shared-control system for powered wheelchairs, enabling users with severe motor disabilities to navigate using limited commands from interfaces like Brain Computer Interfaces (BCI). The approach trains wheelchair steering for enhanced user independence.

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Area of Science:

Robotics
Assistive Technology
Human-Computer Interaction

Background:

Users with severe motor disabilities face challenges using standard interfaces for powered wheelchairs.
Existing interfaces like scanning and Brain Computer Interfaces (BCI) offer limited, sparse command capabilities.

Purpose of the Study:

To develop and present a shared-control approach for Assistive Mobile Robots (AMR), specifically semi-autonomous powered wheelchairs.
To enable users with severe motor disabilities to navigate effectively using sparse Human-Machine Interfaces (HMI).
To integrate this approach within an Assisted Navigation Training Framework (ANTF) for user skill development.

Main Methods:

A shared-control system was designed, integrating user commands with the robot's local motion planning.
The system utilizes user characterization to adapt the shared-control strategy.
Simulations were conducted to evaluate the proposed shared-control method.

Main Results:

The proposed shared-control method effectively combines sparse user inputs with autonomous navigation capabilities.
Simulation results demonstrated the feasibility of the approach for assisted wheelchair navigation.
The system facilitates training for users with limited motor control.

Conclusions:

The developed shared-control approach offers a viable solution for powered wheelchair navigation for individuals with severe motor impairments.
This method enhances user autonomy by leveraging sparse HMIs within a structured training framework.
Further development and real-world testing are warranted to validate the system's efficacy.