AMP-CAD: Automatic Assembly Motion Planning Using C AD Models of Parts

Assembly with robots involves two kinds of motions, those that are point-to-point and those that are force/torque guided, the former kind of motions being faster and more amenable to automatic planning and the latter kind being necessary for dealing with tight clearances. In this paper, we describe an assembly motion planning system that uses descriptions of assemblies and CAD models of parts to automatically figure out which motions should be point-to-point and which motions should be force/torque guided. Our planner uses graph search over a potential field representation of parts to calculate candidate assembly paths. Given the tolerances of the parts and other uncertainties, these paths are then analyzed for the likelihood of collisions. Those path segments that are prone to collisions are then marked for execution under force/torque control. The calculation of the various motions is facilitated by an object-oriented and feature-based assembly representation. A highlight of this representation is the manner in which tolerance information is taken into account: Representation of, say, a part contains a pointer to the boundary representation of the part in its most material condition form. As first defined by Requicha, the most material condition form of a geometric entity is obtained by expanding all the convexities and shrinking all the concavities by relevant tolerances. An integral part of the assembly motion planner is the execution unit. Residing in this unit is knowledge of the different types of automatic EDR (error detection and recovery) strategies. Therefore, during the execution of the force/torque guided motion, this unit invokes the EDR strategies appropriate to the geometric constraints relevant to the motion. This system, called AMP-CAD, has been experimentally verified using a Cincinnati Milacron T3-726 robot and a Puma 762 robot on a variety of assemblies

Refining the continuous tracking paradigm to investigate implicit motor learning.

In two experiments we investigated factors that undermine conclusions about implicit motor learning in the continuous tracking paradigm. In Experiment 1, we constructed a practice phase in which all three segments of the waveform pattern were random, in order to examine whether tracking performance decreased as a consequence of time spent on task. Tracking error was lower in the first segment than in the middle segment and lower in the middle segment than in the final segment, indicating that tracking performance decreased as a function of increasing time-on-task. In Experiment 2, the waveform pattern presented in the middle segment was identical in each trial of practice. In a retention test, tracking performance on the repeated segment was superior to tracking performance on the random segments of the waveform. Furthermore, substitution of the repeated pattern with a random pattern (in a transfer test) resulted in a significantly increased tracking error. These findings imply that characteristics of the repeated pattern were learned. Crucially, tests of pattern recognition implied that participants were not explicitly aware of the presence of a recurring segment of waveform. Recommendations for refining the continuous tracking paradigm for implicit learning research are proposed

The measurement of anisotropic thermal conductivity in snow with needle probes

Thesis (M.S.) University of Alaska Fairbanks, 2011A new method for measuring thermal conductivity is being adapted from the method of measuring isotropic thermal conductivity in snow with needle probes as used by Sturm, Johnson and others, in order to enable the determination of anisotropic thermal conductivities. This method has particular relevance to measuring thermal conductivity of natural snowpacks where conductivity can be strongly anisotropic due to structures that develop from vapor transport-induced metamorphism, self-compaction and other mechanisms, and where there are known discrepancies between density-conductivity relations empirically derived from guarded hot plate and needle probe methods. Both analytically-based solutions and finite element numerical solutions to the anisotropic case are used to calculate the expected effective thermal conductivity as a function of anisotropic thermal conductivity and needle orientation. Additionally, preliminary measurements of both anisotropic salt/sugar layered samples and of snow were taken. Both suggest that detecting anisotropy in such materials is possible, though made difficult by variability between measurements and the requirement of multiple measurements at various angles. These studies suggest that anisotropy in snow may be able to explain in part the discrepancies between guarded hot plate and needle probe measurements in certain cases.Cooperative Institute For Alaska Research1. Introduction -- 1.1. Why snow's conductivity matters -- 1.2. Thermal conductivity measurements of snow -- 1.3. Snow metamorphic principles -- 1.4. Anisotropic behavior in snow -- 1.5. Motivation for measuring snow anisotropy -- 1.6. Anisotropic model -- 1.7. Document outline -- 2. Analytical needle probe approach -- 2.1. Introduction -- 2.2. The isotropic case -- 2.3. Difficulties in the anisotropic case -- 2.4. Posing the problem in two coordinates -- 2.5. Coordinate transformation -- 2.6. From temperature distribution to effective thermal conductivity -- 2.7. Finding effective conductivity as a function of needle orientation -- 2.8. Conclusions -- 3. Numerical needle probe approach -- 3.1. Introduction -- 3.2. Geometry and domain properties -- 3.3. MATLAB in geometry-based parametric studies using COMSOL 3.5a -- 3.4. Automatic calculation of conductivity from simulated time/temperature data -- 3.5. Convergence study -- 3.6. Conclusions -- 4. Experimental measurements -- 4.1. Introduction -- 4.2. Needle probe measurement fundamentals -- 4.3. Snow conductivity measurements -- 4.4. Benchtop tests -- 4.5. Raw materials for the anisotropic composite -- 4.6. Apparatus for containing anisotropic composite -- 5. Results and interpretation -- 5.1. Parameters and nondimensionalization -- 5.2. Numerical vs. analytical predictions -- 5.3. Benchtop measurements -- 5.4. In-situ snow measurements -- 5.5. Ramifications -- 6. Future work -- 6.1. Introduction -- 6.2. Assumptions in the analytical approach -- 6.3. Extended convergence study -- 6.4. Improved benchtop method -- 6.5. Comprehensive benchtop measurements -- 6.6. Comprehensive in-situ measurements -- 6.7. Exploration of the cooling curve -- 6.8. A method for determining anisotropic thermal conductivity from measurements -- 7. Conclusions -- Bibliography

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The Pacific Alumni February 1924

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Flattening an object algebra to provide performance

Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent result

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