For radiological neutron surveying, neutron detectors require shielding to minimize contributions from sources outside the area of interest. To test the effectiveness of such a shield, Monte Carlo N-Particle Transport Codes (MCNP) were used to model a neutron detector so that the effectiveness of such a shield design could be explored. In this research, MCNP models of a 10 B/ZnS detector within a shield were developed and compared to experimental results. By carefully modeling the specifics of the neutron detector as well as the neutron source used in the experiments, the simulation was able to accurately predict the experimental results within 20%.
Recent advances in the field of grasp planning have used heuristics, dimensionality reduction, machine learning, and haptic feedback, with a high degree of success, to plan grasps for simple grippers and/or simple object geometry. We look at applying some of these techniques to the anthropomorphic Meka gripper. First, dimensionality reduction is attempted. We show that dimensionality reduction does not accurately predict the thumb position. A new algorithm is proposed in which measurements from 2D images are used to classify the thumb opposition angle to one of three positions. The remaining joints employ a reactive torque-control strategy to complete the grasp. The algorithm achieves force closure for 82% of 39 household objects. It is simple, computationally fast, and achieves a success rate that is similar to other contemporary grasp planning algorithms.
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