逐次凸化と複合状態トリガーによる自動駐車
Autonomous Parking by Successive Convexification and Compound State Triggers
SCvxと状態トリガー制約を用い、狭い駐車環境でもカスプ点を含む最適な非ホロノミック経路を単一の最適化問題として計画する手法を提案した。
著者: Ali Boyali, Simon Thompson
分類: cs.RO, math.OC
原文アブストラクト
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.
