Autonomous Parking by Successive Convexification and Compound State Triggers
This work addresses parking path planning for autonomous vehicles, presenting an incremental improvement by integrating existing methods for constraint handling.
The paper tackles the problem of generating optimal nonholonomic paths for parking maneuvers with a kinematic car model, using Successive Convexification (SCvx) and state-triggered constraints to formulate it as a single optimization problem, resulting in an algorithm that plans constrained paths with cusp points in narrow parking environments.
In this paper, we propose an algorithm for optimal generation of nonholonomic paths for planning parking maneuvers with a kinematic car model. We demonstrate the use of Successive Convexification algorithms (SCvx), which guarantee path feasibility and constraint satisfaction, for parking scenarios. In addition, we formulate obstacle avoidance with state-triggered constraints which enables the use of logical constraints in a continuous formulation of optimization problems. This paper contributes to the optimal nonholonomic path planning literature by demonstrating the use of SCvx and state-triggered constraints which allows the formulation of the parking problem as a single optimisation problem. The resulting algorithm can be used to plan constrained paths with cusp points in narrow parking environments.