Useful Links for Bio-Robot Design
Physics-based models are in principle no different from other software. Their parts consist of input, processing, and output, and are all supported by an infrastructure. However, these models do not include hardware-in-the-loop simulations or other real-time modeling. Typically speaking, the models of most interest at the workshops run so slowly compared to real time that the kind of linking often used in real-time applications cannot be made.
The input phase can be characterized as the process of describing the physical traits of the system in terms the software understands. This often laborious task can represent a significant fraction of the human effort in the process. This is particularly true for physics-based models because a computational structure must be constructed in addition to the detailed geometric description; a process that can require extensive user interactions.
Physics-based modeling tools are further distinguished from engineering models or heuristic design tools by the computational resources devoted to processing. Since fundamental conservation equations are solved for the physical state within a computational cell or element, complex global states can be accurately represented by applying a highly resolved computational template. Numerical methods for solving the very large numbers of equations required are the source of continual research and development, which extends from basic software algorithms to specialized hardware designs.
One byproduct of highly resolved physics-based calculations is an extreme amount of output data. The quantities of data which can be produced routinely by physics-based codes would overloaded the mass storage capabilities of just a few computer generations ago. To gain insight from the modeling process, this data must be made accessible by interactive, or at least rapid, interrogation by the user. This is a vital step if the modeling and simulation is used to support T&E. Analytical measures of performance have to be developed which can be related to performance metrics measurable during testing.
The explicit inclusion of well-understood basic physics in the processing algorithms should produce results that are understandable, trustworthy, readily extrapolated to new conditions, and provide the most utility to V/L assessments, particularly in LFT&E. The modeling detail required to realize this expectation is typically only practical in the high performance computing (HPC) or supercomputing environments. Recent advances in HPC horsepower have made computing more affordable and allowed researchers to work problems that were unmanageable in the past.
At its lowest level of abstraction, our work is an instance of physics-based graphics modeling. This approach involves constructing dynamic models of animated objects and computing their motions via physical simulation. Physics-based modeling implies that object motions are governed by the laws of physics, which leads to physically realistic animation. Moreover, this approach frees the animator from having to specify many low-level motion details, since motion is synthesized automatically by the physical simulation. This is evident especially when animating passive motion (i.e. motions of inanimate objects)--the animator need only supply the initial state of the object and a physical simulator automatically computes its motion by integrating the differential equations stemming from Newton's laws.
The success of physics-based modeling was demonstrated in modeling the movements of inanimate objects, such as deformable objects [Terzopoulos et al.1987, Terzopoulos and Fleischer1988, Witkin and Welch1990], chains [Barzel and Barr1988] and tree leaves [Wejchert and Haumann1991]. A substantial amount of research has also been concerned with the motion of animate objects, such as humans and animals [Armstrong and Green1985, Wilhelms1987, Badler, Barsky and Zeltzer1991, Hodgins et al.1995].
An animator requires control over physics-based models in order to produce useful animations. We can categorize physics-based control techniques into two approaches: the constraint-based approach and the motion synthesis approach.