未来车辆架构中能源管理系统的实时路径规划

J. Brembeck, C. Winter
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引用次数: 9

摘要

针对高机动机器人电动车研究平台ROboMObil,提出了能量最优路径规划和速度剖面生成方法。ROMO[1]是德国航空航天中心机器人和机电一体化中心的一个发展,以应对几个研究课题,如能源效率,自主或远程控制驾驶的未来(电动)移动应用。该算法的主要任务是实时计算能量最优轨迹。它的设计目的是将实际交通情况(如迎面而来的交通)或变化情况(如下雪)的数据结合起来。然后将得到的轨迹前馈到较低层次的时间无关路径,跟随控制[2],计算我们的能量最优控制分配的运动需求。这反过来又将需求分配给过度驱动车辆的执行器。给出了一种可靠的数值方法来制定能量最优路径规划优化目标,该目标能够提供考虑车辆实际状态的一致性重规划特征。此外,还对不同类型的优化方法的实时性进行了评价。之后将计算速度剖面,并使剖面的生成能够处理动态重规划。最后,我们展示了几个实验结果,使用虚拟道路定义和商业实时平台上的测试。
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Real-time capable path planning for energy management systems in future vehicle architectures
In this paper an energy optimal path planning and velocity profile generation for our highly maneuverable Robotic Electric Vehicle research platform ROboMObil is presented. The ROMO [1] is a development of the German Aerospace Center's Robotics and Mechatronics Center to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. The main task of the proposed algorithms is to calculate an energy optimal trajectory in a real-time capable way. It is designed to incorporate data from actual traffic situations (e.g. oncoming traffic) or changed conditions (e.g. snowy conditions). The resulting trajectory is then fed forward to a lower level time independent path following control [2] that calculates the motion demands for our energy optimal control allocation. This in turn distributes the demand to the actuators of the over-actuated vehicle. We show a numerical reliable way to formulate the energy optimal path planning optimization objective, which is able to provide a consistent replanning feature considering the actual vehicle states. Besides this, different types of optimization methods are evaluated for their real-time capabilities. The velocity profile will be calculated afterwards and the generation of the profile is also enabled to handle dynamic replanning. Finally, we show several experimental results, using a virtual road definition and tests on a commercial real-time platform.
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