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A unified motion planning method for parking an autonomous vehicle in the presence of irregularly placed obstacles

机译:在存在不规则障碍物的情况下停放自动驾驶车辆的统一运动计划方法

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This paper proposes a motion planner for autonomous parking. Compared to the prevailing and emerging studies that handle specific or regular parking scenarios only, our method describes various kinds of parking cases in a unified way regardless they are regular parking scenarios (e.g., parallel, perpendicular or echelon parking cases) or not. First, we formulate a time-optimal dynamic optimization problem with vehicle kinematics, collision-avoidance conditions and mechanical constraints strictly described. Thereafter, an interior-point simultaneous approach is introduced to solve that formulated dynamic optimization problem. Simulation results validate that our proposed motion planning method can tackle general parking scenarios. The tested parking scenarios in this paper can be regarded as benchmark cases to evaluate the efficiency of methods that may emerge in the future. Our established dynamic optimization problem is an open and unified framework, where other complicated user-specific constraints/optimization criteria can be handled without additional difficulty, provided that they are expressed through inequalities/polynomial explicitly. This proposed motion planner may be suitable for the next-generation intelligent parking-garage system. (C) 2015 The Authors. Published by Elsevier B.V.
机译:本文提出了一种用于自动泊车的运动计划器。与仅处理特定或常规停车场景的流行和新兴研究相比,我们的方法以统一的方式描述了各种停车案例,无论它们是否为常规停车场景(例如平行,垂直或梯级停车案例)。首先,我们用严格的车辆运动学,避免碰撞条件和机械约束条件来制定时间最优动态优化问题。此后,引入内点同时方法来解决该公式化的动态优化问题。仿真结果验证了我们提出的运动计划方法可以解决一般的停车场景。本文中经过测试的停车场景可以被视为评估未来可能出现的方法的效率的基准案例。我们建立的动态优化问题是一个开放和统一的框架,在该框架中,可以通过不等式/多项式明确表示其他复杂的特定于用户的约束/优化条件,而不会产生其他困难。提出的运动计划器可能适用于下一代智能停车库系统。 (C)2015作者。由Elsevier B.V.发布

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