Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks

Abstract

It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.