Rigid body simulation is an integral part of Virtual Environments (VE) for autonomous planning, training, and design tasks. The underlying physics based simulation of VE must be accurate and computationally fast enough for the intended application, which unfortunately are conflicting requirements. Two ways to perform fast and high fidelity physics based simulation are: (1) model simplification, and (2) parallel computation. Model simplification can be used to allow simulation at an interactive rate while introducing an acceptable level of error. Currently, manual model simplification is the most popular way of performing simulation speed up but it is time consuming. Hence, in order to reduce the development time of VEs, automated model simplification is needed. I will present an automated model simplification approach based on geometric reasoning, spatial decomposition, and temporal coherence. Geometric reasoning is used to develop an accessibility based algorithm for removing portions of geometric models that do not play any role in rigid body to rigid body interaction simulation. Removing such inaccessible portions of the interacting rigid body models has no influence on the simulation accuracy but reduces computation time significantly. Spatial decomposition is used to develop a clustering algorithm that reduces the number of fluid pressure computations resulting in significant speedup of rigid body and fluid interaction simulation. Temporal coherence algorithm reuses the computed force values from rigid body to fluid interaction based on the coherence of fluid surrounding the rigid body. The simulations can further be sped up by performing computing on general purpose graphics processor (GP-GPU). Harnessing GP-GPU computing technology requires development of parallel algorithms for the simulation. I will talk about the issues pertaining to the development of parallel algorithms for rigid body simulations. The developed algorithms have enabled real time high fidelity 6-DOF time domain simulation of Unmanned Sea Surface Vehicles. The developed simulator can be used for autonomous motion planning, teleoperation, and learning from demonstration.
Biography: Atul Thakur is a Ph.D. candidate in the Department of Mechanical Engineering at the University of Maryland. His main research interests include model simplification for physics based simulations for unmanned vehicles and robot motion planning. Prior to joining University of Maryland in 2007, he was working as Design Engineer at Aircraft Engine Performance Engineering center of excellence at General Electric - India Technology Center, Bangalore. He received a Master of Technology (M. Tech.) degree in Manufacturing Engineering from the Indian Institute of Technology, Bombay in 2006. He received a Bachelor of Engineering (B.E.) degree in Production Engineering from the University of Mumbai in 2003.