Probabilistic Virtual Fixtures: Arbitration, Automation and Stable Control

Kurzfassung

Virtual Fixtures (VFs) are software-generated guides that assist a human operator in telemanipulation and human-robot interaction tasks by providing force feedback. Often multiple fixtures with different properties and input modalities are required, to optimally support the human in challenging tasks. The uncertainty measure available from a probabilistic fixture formulation, which utilizes Gaussian Mixture Models to capture the variability and uncertainty in human motion and task execution, allows to allocate control authority between human and fixture based on stiffness adaptation as well as to arbitrate between different fixtures. Such formulation furthermore allows to automate VFs while still keeping the human in control. This lecture explores the required control algorithms to implement such behavior and achieve a stable controller. Additionally, the implementation on haptic devices and hands-on robots is covered. The practicability of these approaches is shown on a set of different use cases in space, industrial, and medical applications.