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A Culling Procedure for Collision Avoidance Model Predictive Control with Application to Ship Autopilot Models

机译:用于避免模型预测控制的剔除程序,用应用程序运送自动驾驶仪模型

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An optimization-based approach for collision avoidance of fully-dimensional autonomous vehicles in confined environments is considered. This is realized by means of a dual reformulation of the signed distance function which yields an exact representation of the usually nonlinear, non-convex, and non-differentiable signed distance function. However, the dual collision avoidance MPC induces additional decision variables which greatly increase problem complexity. Therefore, a culling procedure is combined with the dual approach in order to reduce the problem size by identifying and eliminating decision variables associated with faces of the polyhedral obstacles and controlled vehicle, respectively. The proposed method is illustrated using a simulative study where a path-following task is performed for a ship autopilot model and near-field obstacle information is extracted from a set of AIS data from the Kiel bay area.
机译:考虑了基于优化的避免避免局限性自治车辆的优化方法。 这通过符号距离功能的双重重构来实现,其产生通常非线性,非凸和非可分子符号距离功能的精确表示。 然而,双碰撞避免MPC引起了额外的决策变量,这大大提高了问题复杂性。 因此,将剔除过程与双方法组合,以便通过分别通过识别和消除与多面体障碍物和受控车辆的面部相关联的判定变量来减少问题尺寸。 所提出的方法是使用模拟研究来示出的,其中对船舶自动驾驶仪模型执行路径后任务,并且从基尔湾区域的一组AIS数据中提取近场障碍物信息。

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