A hybrid approach for simulating human motion in constrained environments

We present a new algorithm to generate plausible motions for high-DOF human-like articulated figures in constrained environments with multiple obstacles. Our approach is general and makes no assumption about the articulated model or the environment. The algorithm combines hierarchical model decomposition with sample-based planning to efficiently compute a collision-free path in tight spaces. Furthermore, we use path perturbation and replanning techniques to satisfy the kinematic and dynamic constraints on the motion. In order to generate realistic human-like motion, we present a new motion blending algorithm that refines the path computed by the planner with mocap data to compute a smooth and plausible trajectory. We demonstrate the results on generating motion corresponding to placing or lifting object, bending and walking for a 38-DOF articulated model.