The objective of this research is to improve the ability of a human operator to teleoperate an omnidirectional wheeled robot using omnidirectional force feedback. Omnidirectional wheeled robots offer improved mobility over conventional wheeled robots and can potentially benefit people requiring motorized transportation and industries where robotic vehicles must operate in confined spaces. However, omnidirectional robotic vehicles require more degrees of freedom to control due to the additional degrees of freedom inherent in the vehicle’s design. We hypothesize that providing force feedback to the operator through an omnidirectional joystick will allow the robot to assist the driver in navigating and avoiding collisions with obstacles in a manner that is natural to the operator, coordinating both translational and rotational degrees of freedom. This research is the first attempt to use omnidirectional 3-DOF (degree of freedom) force feedback to provide navigational assistance for a human to drive/teleoperate an omnidirectional vehicle. This paper presents experiments using a novel omnidirectional force-feedback joystick and force-feedback strategy to guide operators to navigate a virtual omnidirectional wheeled robot in real-time through a geometrically constrained virtual environment. Experimental results demonstrate that the 3-DOF force feedback significantly improves collision avoidance. Additionally, including rotational feedback and optimizing the center of compliance results in significantly smoother trajectories.
- Dynamic Systems and Control Division
Omnidirectional Force Feedback for Teleoperation of Omnidirectional Wheeled Robots
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Tyagi, R, & Mascaro, S. "Omnidirectional Force Feedback for Teleoperation of Omnidirectional Wheeled Robots." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T04A014. ASME. https://doi.org/10.1115/DSCC2018-9122
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