PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Trajectory Prediction in Cluttered Voxel Environments
Nikolay Jetchev and Marc Toussaint
ICRA 2010 pp. 2523-2528, 2010.


Abstract— Trajectory planning and optimization is a fun- damental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. There are many methods that can generate good movements when given enough time, but planning for high-dimensional robot configuration spaces in realistic environments with many objects in real time remains challenging. This work presents a novel way for faster move- ment planning in such environments by predicting good path initializations. We build on our previous work on trajectory prediction by adapting it to environments modeled with voxel grids and defining a frame invariant prototype trajectory space. The constructed representations can generalize to a wide range of situations, allowing to predict good movement trajectories and speed up convergence of robot motion planning. An empirical comparison of the effect on planning movements with a combination of different trajectory initializations and local planners is presented and tested on a Schunk arm manipulation platform with laser sensors in simulation and hardware.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8015
Deposited By:Nikolay Jetchev
Deposited On:17 March 2011