Human Motion Planning and Synthesis

Publications

Motion Planning of Human-Like Robots Using Constrained Coordination, Liangjun Zhang, Jia Pan, Dinesh Manocha

IEEE-RAS International Conference on Humanoid Robots (Humanoids09) , 2009, PDF

Abstract: We present a whole-body motion planning algorithm for human-like robots. The planning problem is decomposed into a sequence of low-dimensional sub-problems. Our formulation is based on the fact that a human-like model is a tightly coupled system and we use a constrained coordination scheme to solve the sub-problems in an incremental manner. We also present a local path refinement algorithm to compute collision-free paths in tight spaces and satisfy the statically stable constraint on CoM. We demonstrate the performance of our algorithm on an articulated human-like model and generate efficient motion strategies in complex CAD models.

A Hybrid Approach for Synthesizing Human Motion in Constrained Environments, Jia Pan, Liangjun Zhang, Ming Lin, Dinesh Manocha

the 23rd International Conference on Computer Animation and Social Agents (CASA 2010), 2010, PDF

Abstract: 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 assumptions 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 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 38-DOF articulated model.

Results and Videos

Download the video (avi format)

Acknowledgements

We would like to thank Jean-Paul Laumond and Kineo CAM for providing car models. We would like to thank Sean Curtis and Will Moss for their help in video and rendering. This research was supported in part by ARO Contract W911NF-04-1-0088, NSF awards 0636208, 0917040 and 0904990, DARPA/RDECOM Contract WR91CRB-08-C-0137, and Intel. Liangjun Zhang was supported in part by CRA/NSF/CCC Computing Innovation Fellows Project.

Publications

Results and Videos

Acknowledgements

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