The development of a generic and effective force model for semi-automatic or manual virtual assembly with haptic support is not a trivial task, especially when the assembly constraints involve complex features of arbitrary shape. The primary challenge lies in a proper formulation of the guidance forces and torques that effectively assist the user in the exploration of the virtual environment (VE), from repulsing collisions to attracting proper contact. The secondary difficulty is that of efficient implementation that maintains the standard 1 kHz haptic refresh rate. We propose a purely geometric model for an artificial energy field that favors spatial relations leading to proper assembly, differentiated to obtain forces and torques for general motions. The energy function is expressed in terms of a cross-correlation of shape-dependent affinity fields, precomputed offline separately for each object. We test the effectiveness of the method using familiar peg-in-hole examples. We show that the proposed technique unifies the two phases of free motion and precise insertion into a single interaction mode and provides a generic model to replace the ad hoc mating constraints or virtual fixtures, with no restrictive assumption on the types of the involved assembly features.
The PBM functionalities in virtual assembly applications (including collision response, impact friction mechanics, and noon-smooth Lagrangian dynamics @cite_3 ) are typically offered as part of a physics simulation engine (PSE). Although a PSE+CD approach seems the most natural choice (at least in theory) for a virtual mimicry of real-world constrained motion, it is not effective in practice for final insertion in low-clearance assembly @cite_30 @cite_48 . This arises for at least two reasons: 1) numerical errors due to the approximate representations used in fast CD methods popular for haptic rendering; and 2) input noise due to hand vibrations and device errors. The former can be solved by using exact representations, which comes at the expense of computational burden. For example, later versions of SHARP @cite_51 @cite_45 employed the collision detection manager (CDM) module of D-Cubed, which makes direct use of Boundary Representation (B-rep) of CAD models. However, the latter difficulty persists, even with exact CD.
@cite_30 @cite_48 @cite_3 @cite_45 @cite_51
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This research combines physics-based and constraint-based approaches for virtual assembly simulations where geometric constraints are created or deleted within the virtual environment at runtime. In addition, this research provides a solution to low clearance assembly by utilizing B-Rep data representation of complex CAD models for accurate collision physics results. These techniques are demonstrated in the SHARP software (System for Haptic Assembly and Realistic Prototyping). Combining physics-based and constraint-based techniques and operating on accurate B-rep data, SHARP can now assemble parts with 0.001 clearance and can accurately detect collision responses with 0.0001mm accuracy. Case studies are presented which can be used to identify the suitable combination of methods capable of best simulating intricate interactions and environment behavior during manual assembly.Copyright © 2007 by ASME This research presents a novel approach to virtual assembly that combines dynamic modeling with geometric constraint-based modeling to support low clearance manual assembly of CAD models. This is made possible by utilizing the boundary representation solid model data available in most contemporary CAD representations, which enables (a) accurate collision physics calculations on exact model definitions, and (b) access to geometric features. Application of geometric constraints during run-time, aid the designer during assembly of the virtual models. The feasibility of the approach is demonstrated using a pin and hole assembly example. Results that demonstrate the method give the user the ability to assemble parts without requiring extensive CAD preprocessing and without over constraining the user to arrive at predetermined final part orientations. Assembly is successful with diametral clearance as low as 0.0001 mm, as measured between a 26 mm diameter hole and pin. We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993 in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535 success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993 success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery. We present a framework for fast and accurate collision detection for haptic interaction with polygonal models. Given a model, we pre-compute a hybrid hierarchical representation, consisting of uniform grids and trees of tight-fitting oriented bounding box trees (OBB-Trees). At run time, we use hybrid hierarchical representations and exploit frame-to-frame coherence for fast proximity queries. We describe a new overlap test, which is specialized for intersection of a line segment with an oriented bounding box for haptic simulation and takes 6-36 operations excluding transformation costs. The algorithms have been implemented as part of H-COLLIDE and interfaced with a PHANToM arm and its haptic toolkit, GHOST, and applied to a number of models. As compared to the commercial implementation, we are able to achieve up to 20 times speedup in our experiments and sustain update rates over 1000Hz on a 400MHz Pentium II.