Autonomous Parking by Successive Convexification and Compound State Triggers

TL;DR

This paper introduces an optimal path planning algorithm for parking maneuvers using Successive Convexification and state-triggered constraints, enabling feasible, obstacle-avoiding paths with logical conditions in a continuous optimization framework.

Contribution

It combines Successive Convexification with state-triggered constraints to formulate parking as a single optimization problem, handling narrow environments and cusp points.

Findings

Guarantees path feasibility and constraint satisfaction.

Handles obstacle avoidance with logical constraints.

Effective in narrow parking scenarios.

Abstract

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.