We present a new algorithm to generate plausible walking motion for high-DOF human-like articulated figures in constrained environments with multiple obstacles. Our approach 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 motion capture data to compute a smooth and plausible trajectory. We demonstrate the results of generating motion corresponding to placing or lifting object, walking and bending for a 34-DOF articulated model.
CITATION STYLE
Pan, J., Zhang, L., & Manocha, D. (2013). Synthesizing human-like walking in constrained environments. In Cognitive Systems Monographs (Vol. 18, pp. 181–186). Springer Verlag. https://doi.org/10.1007/978-3-642-36368-9_14
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