Efficient stochastic multicriteria arm trajectory optimization

摘要

Performing manipulation with robotic arms requires a method for planning trajectories that takes multiple factors into account: collisions, joint limits, orientation constraints, torques, and duration of a trajectory. We present an approach to efficiently optimize arm trajectories with respect to multiple criteria. Our work extends Stochastic Trajectory Optimization for Motion Planning (STOMP). We optimize trajectory duration by including velocity into the optimization. We propose an efficient cost function with normalized components, which allows prioritizing components depending on user-specified requirements. Optimization is done in two stages: first with a partial cost function and in the second stage with full costs. We compare our method to state-of-the art methods. In addition, we perform experiments on real robots: centaur-like robot Momaro and an industrial manipulator.