A constraint-based methodology for product design with virtual reality

This paper presents a constraint-based methodology for product design with advanced virtual reality technologies. A hierarchically structured and constraint-based data model is developed to support product design from features to parts and further to assemblies in a VR environment. Product design in the VR environment is performed in an intuitive manner through precise constraint-based manipulations. Constraint-based manipulations are accompanied with automatic constraint recognition and precise constraint satisfaction to establish constraints between objects, and are further realized by allowable motions for precise 3D interactions in the VR environment. The allowable motions are represented as a mathematical matrix and derived from constraints between objects by constraint solving. A procedure-based degrees-of-freedom combination approach is presented for 3D constraint solving. A rule-based constraint recognition engine is developed for both constraint-based manipulations and implicitly incorporating constraints into the VR environment. An intuitive method is presented for recognizing pairs of mating features between assembly components. Examples are presented to demonstrate the efficacy of the proposed methodology

Deformable Object Modelling Through Cellular Neural Network

This paper presents a new methodology for thedeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by an externalforce is propagated among mass points by the non-linearCNN activity. An improved autonomous CNN model isdeveloped for propagating the energy generated by theexternal force on the object surface in the naturalmanner of heat conduction. A heat flux based method ispresented to derive the internal forces from the potentialenergy distribution established by the CNN. Theproposed methodology models non-linear materials withnon-linear CNN rather than geometric non-linearity inthe most existing deformation methods. It can not onlydeal with large-range deformations due to the localconnectivity of cells and the CNN dynamics, but it canalso accommodate both isotropic and anisotropicmaterials by simply modifying conductivity constants.Examples are presented tThis paper presents a new methodology for the deformable object modelling by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by the non-linear CNN activity. An improved autonomous CNN model is developed for propagating the energy generated by the external force on the object surface in the natural manner of heat conduction. A heat flux based method is presented to derive the internal forces from the potential energy distribution established by the CNN. The proposed methodology models non-linear materials with non-linear CNN rather than geometric non-linearity in the most existing deformation methods. It can not only deal with large-range deformations due to the local connectivity of cells and the CNN dynamics, but it can also accommodate both isotropic and anisotropic materials by simply modifying conductivity constants. Examples are presented to demonstrate the efficacy of the proposed methodology

Motion analysis of a robotic assisted surgery and microsurgery system - experimental verification

Motion analysis of a parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) and the control structures enabling it to achieve milli/micromanipulations under the constraint of moving through a fixed penetration point or so-called remote centre-of-motion (RCM) are presented in this article. Two control algorithms are proposed suitable for minimally invasive surgery (MIS) with submillimeter accuracy and for minimally invasive micro-surgery (MIMS) with submicrometer accuracy. The RCM constraint is performed without having any mechanical constraint. Control algorithms also apply orientation constraint preventing the tip to orient relative to the soft tissues due to the robot movements. Experiments were conducted to verify accuracy and effectiveness of the proposed control algorithms for MIS and MIMS operations. The experimental results demonstrate accuracy and performance of the proposed position control algorithms