Robot Motion Planning for Pouring Liquids. Pan, Z., Park, C., & Manocha, D. In
Robot Motion Planning for Pouring Liquids [link]Paper  abstract   bibtex   2 downloads  
We present a new algorithm to compute a collision-free trajectory for a robot manipulator to pour liquid from one container to the other. Our formulation uses a physical model of the fluid to model its highly deformable motion. We present a simulation guided, optimization based method to automatically compute the transferring trajectory. Instead of abstract or simplified liquid models, we adopt the full-featured Navier-Stokes model that provides the fine-grained information of velocity distribution inside the liquid body. Moreover, this information is used as an additional guiding energy term. In order to avoid large number of re-simulations, required by exhaustive and stochastic trajectory search, we use simple heuristic approximations based on the result of just one pass of simulation. We have implemented the method using hybrid particle-mesh fluid simulator (FLIP) and demonstrated its performance on 4 benchmarks, with different cup shapes and viscosity coefficients.
@inproceedings {icaps16-53,
    track    = {​​Robotics Track},
    title    = {Robot Motion Planning for Pouring Liquids},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13009},
    author   = {Zherong Pan and  Chonhyon Park and  Dinesh Manocha},
    abstract = {We present a new algorithm to compute a collision-free trajectory for a robot manipulator to pour liquid from one container to the other.  Our formulation uses a physical model of the fluid to model its highly deformable motion. We  present a simulation guided, optimization based method to automatically compute the transferring trajectory. Instead of abstract or simplified liquid models, we adopt the full-featured Navier-Stokes model that provides the fine-grained information of velocity distribution inside the liquid body. Moreover, this information is used as an additional guiding energy term. In order to avoid large number of re-simulations, required by exhaustive and stochastic trajectory search, we use simple heuristic approximations based on the result of just one pass of simulation. We have implemented the method using hybrid particle-mesh fluid simulator (FLIP) and demonstrated its performance on 4 benchmarks, with different cup shapes and viscosity coefficients.},
    keywords = {robot motion; path; task and mission planning and execution}
}
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