SBP-Guided MPC to Overcome Local Minima in Trajectory Planning

Optimal Planning and Control: Fusing Offline and Online Algorithms Workshop, ICRA, 2019

Non-linearity of robot dynamics can cause failure of trajectory optimization methods (ex. iLQG) due to local optima. On the other hand, Sampling Based Planning (SBP) methods such as Rapidly-exploring Random Trees (RRT) are inherently robust to presence of local optima but often generate in-efficient trajectories. We propose combining these two classes of methods to retain the strengths of each. We use RRT trajectory as initialization for iLQG and overcome local optima while producing an efficient trajectory.