In recent years, attention has been increasingly focused on human-machine cooperative teleoperation due to limited capabilities of autonomous robots. A method of sensor and model assisted teleoperation using variable velocity mapping is presented. The application of variable velocity mapping to the execution of Fitts tasks is described, and comparisons are made with traditional teleoperation. Application to a practical task of docking is also described.
Radioactive tank waste remediation and decontamination and decommissioning (D&D) of contaminated Department of Energy (DOE) facilities, and other nuclear cleanup tasks require extensive remote handling technologies. The unstructured nature of these tasks and limitations of the current sensor and computer decision-making technologies prohibit the use of completely autonomous systems for remote manipulation.This paper presents a new methodology in which modelbased computer assistance is incorporated into human controlled teleoperator systems.This approach implies a form of assistance function in which the human input is enhanced rather than superseded by the computer. A specific task of cutting a pipe with a saw is chosen as an example to demonstrate the implementation of the assistance functions in D&D size reduction tasks and the results are presented.
In this paper we discuss the possibility of improving eye-hand coordination in children diagnosed with this problem, using a robotic mapping from a haptic user interface to a virtual environment. Our goal is to develop an assessment and training procedure that will result in improving handwriting taking advantage of the force feedback provided by the haptic device. We also incorporate inertia and viscosity effects to decrease the tremor in the hand as well as to stimulate the muscles involved in the task of holding a pencil (known as facilitation technique in the Occupational Therapy field). A set of assessment tests, commonly used by occupational therapists, were chosen to implement various functions using force, inertia and viscosity effects. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK Software.
In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user's motion intention. In this paper, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the endeffector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.
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