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11/02/08 |
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1. Effects of position uncertainty in grasping 3D objects Sponsor: National Institute of Healty (NIH)
This work addresses the problem of how humans compensate for the position uncertainty of objects when grasping them. One of the most challenged problems in robotics is grasping objects when their position is known imprecisely. Even though this problem is considered very difficult for robotic systems, human beings may develop strategies using prior information for handling the position uncertainty of the objects. To investigate this issue, we performed experiments in which participants reached to and lifted cylindrical objects using precision grasps. Uncertainty about the object’s location was introduced by randomly moving a binocularly viewed object using a PUMA robotic arm over sequence of 5 positions. The positions were sampled from a 2-D Gaussian distribution with a strongly oriented covariance matrix. Vision of the object was then removed using liquid crystal shutter glasses, and the robot moved the object one additional time out of view. The participant’s were then cued to rapidly reach for the object while out of view. All the object positions were sampled from the same covariance for a block of 100 trials, and data were collected for several covariance conditions by rotating orientation covariance matrix. Finger trajectories were recorded by placing 3 Optotrak sensors on top of the subjects’ thumb and index finger nails. Initially, the subject sets its thumb and index finger on a position called referred position of the trial. We tested the specific hypotheses: Does the subject build a specific strategy for grasping the object in each experiment? And is there any significant difference on the way that the subject grasps the object as the uncertainty ellipsoid varies? 2. Brain Computer Interface using Non-Invsasive Techniques (EEG, MEG) Sponsor: VA Medical Center, University of Minnesota
Brain Computer Interface (BCI) provides communication and control to people with severe motor disabilities. It can use invasive and non-invasive methods for recording brain signals and controlling prosthetic devices. Invasive BCI uses implanted electrodes to extract the brain signal information that conveys the user’s commands. However, the implanted electrodes in the brain carry inherent risk, since they show viability of some months and they should be replaced by new electrodes. In this work, we present a new non-invasive BCI technique using Electroencephalography (EEG) for recording the brain signals. A human subject copies a pentagon for 5 minutes using an X-Y joystick while EEG signals are being recorded from 64 (or 32) channels. Finally, a linear summation of weighted contributions of the EEG signals is used to predict the movement trajectory.
3. Grasping a 2 Dimensional
Object under shape and contact location uncertainty
Sponsor: National
Institute of Healty (NIH)
One of the most important problems in grasping and manipulation is the selection of contact points to grasp an object. Humans have thousands of sensors and actuators to coordinate and adapt them to grasp an object. In addition, they learn how to handle uncertainty in object shape through developmental experience. Understanding the way that human grasps an object in a stochastic environment can help us to build better artificial devices (e.g., limbs). In this work, we explore the benefits that we should gain by taking uncertainty into account.
1. Next Generation
Medtronic Spinal Cord Stimulator (Summer Internship) Neurostimulation is a pain treatment that delivers low voltage electrical stimulation to the spinal cord or peripheral nerve to inhibit or block the sensation of pain. The neurostimulation system consists of stimulating lead(s), which deliver(s) electrical stimulation to the spinal cord or peripheral nerve; an extension wire, which conducts electrical pulses from the power source to the lead; and a power source, which generates the electrical pulses.
2. Multi Robot Trajectory Generation for Single Source Explosion
Parameter Estimation
3. Development of an Intelligent Autonomous
Navigation System for Unmanned Aerial Vehicles
4. Preliminary Design and Development of a
Turbo-Jet Engine for an Unmanned Aerial Platform
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This site was last updated 11/02/08