Trajectory optimization with inter-sample obstacle avoidance via successive convexification
Daniel Dueri, Yuanqi Mao, Zohaib T. Mian, Jerry Ding, Behcet A. Acikmese · 2017
This paper develops a convex optimization-based method for real-time path planning onboard an autonomous vehicle for environments with cylindrical or ellipsoidal obstacles. Obstacles render the state space non-convex, thus a constrained, non-convex optimal control problem must be solved to obtain a feasible trajectory. A technique known as Successive Convexification is used to solve the non-convex optimal control problem via a convergent sequence of convex optimization problems. The paper has two main contributions: first, constraints for ensuring that the computed trajectories do not cross obstacles between discrete states are developed for a class of dynamics; second, the theory of successive convexification is extended to allow for a general class of non-convex state constraints. Finally, simulations for a relevant example are presented to demonstrate the effectiveness of the proposed method.