Trajectory Prediction in Cluttered Voxel Environments

Trajectory planning and optimization is a fundamental 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 movement 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.