论文标题
通过连续的爆之间和复合状态触发器自动停车
Autonomous Parking by Successive Convexification and Compound State Triggers
论文作者
论文摘要
在本文中，我们提出了一种算法，用于使用运动型汽车模型最佳生成非卫生途径，以计划停车行动。我们证明了使用连续的凸化算法（SCVX），可确保路径可行性和约束满意度，以用于停车方案。此外，我们使用受状态触发的约束来避免障碍物，从而在连续的优化问题表述中使用逻辑约束。本文通过证明使用SCVX和国家触发的约束来促进最佳的非自然路径计划文献，从而允许将停车问题作为单个优化问题提出。所得算法可用于计划在狭窄的停车环境中使用尖点点的约束路径。
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.
