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Probabilistic Estimation of Human Interaction Needs in Context of a Robotic Assistance in Geriatrics
(2019)
The key purpose of assistance robots is to help people coping with work-related or everyday tasks. To ensure an intuitive and effective support by an assistance robot, its expectation conform behavior is essential. In particular, when using assistance robots in geriatrics to assist elderly patients, special attention to the human-robot interaction should be paid. In order to help elderly patients maintain their independence and abilities as much as possible, the robot should only intervene when its support is needed. Therefore, the continuous estimation of the patient’s need for interaction is of particular importance. For enabling suitable models to estimate this need, we elaborate the use of Bayesian Networks. The analysis of our results seems promising, yielding a robust and practical approach.
This paper presents a novel algorithm for computing absolute space representations (ASRs) in Yeap, W.K. and Jefferies, M. (1988) for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown
OBJECTIVES:
To generate a fast and robust 3-D visualization of the operation site during minimal invasive surgery.
METHODS:
Light fields are used to model and visualize the 3-D operation site during minimal invasive surgery. An endoscope positioning robot provides the position and orientation of the endoscope. The a priori un-known transformation from the endoscope plug to the endoscope tip (hand-eye transformation) can either be determined by a three-step algorithm, which includes measuring the endoscope length by hand or by using an automatic hand-eye calibration algorithm. Both methods are described in this paper and their respective computation times and accuracies are compared.
RESULTS:
Light fields were generated during real operations and in the laboratory. The comparison of the two methods to determine the unknown hand-eye transformation was done in the laboratory. The results which are being presented in this paper are: rendered images from the generated light fields, the calculated extrinsic camera parameters and their accuracies with respect to the applied hand-eye calibration method, and computation times.
CONCLUSION:
Using an endoscope positioning robot and knowing the hand-eye transformation, the fast and robust generation of light fields for minimal invasive surgery is possible.