Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. The majority of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. One reason for this is the inability of today’s robot systems to understand and reason about the world. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there is currently no system that comes close to achieving the reasoning capabilities of humans.
In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. For example, using teleoperation a human can operate at a remote site, which may not be accessible for the operator for a number of reasons, by issuing commands to a remote agent that will act as an extension of the operator’s body.
Segmentation and recognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online in order to improve the performance in terms of execution time and overall precision. Acquiring, representing and modeling human skills are key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several sub-tasks in order to provide manageable modeling.
This thesis is focused on two aspects of human-machine collaborative systems. Classfication of an operator’s motion into a predefined state of a manipulation task and assistance during a manipulation task based on virtual fixtures. The particular applications considered consists of manipulation tasks where a human operator controls a robotic manipulator in a cooperative or teleoperative mode.
A method for online task tracking using adaptive virtual fixtures is presented. Rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory sub-task) is estimated and used to automatically adjusts the compliance of a virtual fixture, thus providing an online decision of how to fixture the movement.
A layered hidden Markov model is used to model human skills. A gestem classifier that classifies the operator’s motions into basic action-primitives, or gestemes, is evaluated. The gestem classifiers are then used in a layered hidden Markov model to model a simulated teleoperated task. The classification performance is evaluated with respect to noise, number of gestemes, type of the hidden Markov model and the available number of training sequences. The layered hidden Markov model is applied to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the layered hidden Markov model is suitable for modeling teleoperative trajectory-tracking tasks and that the layered hidden Markov model is robust with respect to misclassifications in the underlying gestem classifiers.