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Simulation of the musculoskeletal system has important applications in biomechanics, biomedical engineering, surgery simulation, and computer graphics. The accuracy of the muscle, bone, and tendon geometry as well as the accuracy of muscle and tendon dynamic deformation are of paramount importance in all these applications. We present a framework for extracting and simulating high resolution musculoskeletal geometry from the segmented visible human data set. We simulate 30 contact/collision coupled muscles in the upper limb and describe a computationally tractable implementation using an embedded mesh framework. Muscle geometry is embedded in a nonmanifold, connectivity preserving simulation mesh molded out of a lower resolution BCC lattice containing identical, well-shaped elements, leading to a relaxed time step restriction for stability and, thus, reduced computational cost. The muscles are endowed with a transversely isotropic, quasi-incompressible constitutive model that incorporates muscle fiber fields as well as passive and active components. The simulation takes advantage of a new robust finite element technique that handles both degenerate and inverted tetrahedra.

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Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC.

The purpose of this study was to visualize and document the architecture of the human soleus muscle throughout its entire volume. The architecture was visualized by creating a three-dimensional (3D) manipulatable computer model of an entire cadaveric soleus, in situ, using B-spline solid to display muscle fiber bundles that had been serially dissected, pinned, and digitized. A database of fiber bundle length and angle of pennation throughout the marginal, posterior, and anterior soleus was compiled. The computer model allowed documentation of the architectural parameters in 3D space, with the angle of pennation being measured relative to the tangent plane of the point of attachment of a fiber bundle. Before this study, the only architectural parameters that have been recorded have been 2D. Three-dimensional reconstruction is an exciting innovation because it makes feasible the creation of an architectural database and allows visualization of each fiber bundle in situ from any perspective. It was concluded that the architecture is non-uniform throughout the volume of soleus. Detailed architectural studies may lead to the development of muscle models that can more accurately predict interaction between muscle parts, force generation, and the effect of pathologic states on muscle function.