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Development of path tracking software for an autonomous steered-wheeled robotic vehicle and its trailer.

机译:为自动驾驶转向机械手车辆及其拖车开发路径跟踪软件。

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Wright Laboratory, at Tyndall AFB, Florida, has contracted the University of Florida to develop autonomous navigation systems for a variety of robotic vehicles, capable of performing tasks associated with the location and removal of bombs and mines. One of the tasks involves surveying plots of land for buried munitions. The navigation subtasks include vehicle positioning, path planning, path following, and obstacle avoidance.; The author's master's research involved implementing, on a navigation test vehicle (NTV), a path planner that generates the shortest collision-free path between two vehicle configurations. Subsequent to this work, a second planner was implemented that generates an efficient route for surveying a polygon shaped field. These routes ensure 100% of the surface area is covered, provided that the path is accurately followed.; Accuracy in path following is critical to the task. There are hundreds of acres that currently require surveying. The sites are typically divided into regions, where each mission can take up to 4.5 hours. These sites are usually surveyed using parallel rows. By improving the accuracy of path following, the distance between the rows can be increased to nearly the detection width of the ground penetrating sensors.; There are two ways to improve the accuracy of path following: improve vehicle positioning and reduce control errors. The NTV is currently capable of calculating its position in real-time, accurate to within 6 cm on an average. The author's doctoral research involves developing a strategy that takes advantage of an accurate positioning system and ensures accurate path following for any steered-wheeled vehicle over a range of speeds (0-4.5 m/s).; This strategy involves a process for standardizing and smoothing a given path, calculating a target position in real-time, combining the desirable features of a high-level PID and adaptive pure pursuit steering controller, autonomously calibrating the steering feedback data, autonomously tuning control parameters, and scheduling the control gains as a function of the vehicle speed. Auto-tuning is accomplished with a relay tuner and a golden section search (GSS) optimization. This strategy was demonstrated in simulation and implemented on the NTV.
机译:位于佛罗里达州廷德尔空军基地的赖特实验室与佛罗里达大学签订了合同,为各种机器人车辆开发自动导航系统,该系统能够执行与炸弹和地雷的定位和清除相关的任务。任务之一涉及勘测土地的埋藏弹药。导航子任务包括车辆定位,路径规划,路径跟踪和避障。作者的硕士研究涉及在导航测试车辆(NTV)上实施一种路径规划器,该路径规划器生成两种车辆配置之间的最短无碰撞路径。这项工作之后,实施了第二个计划器,该计划器生成了用于测量多边形场的有效路线。这些路径可确保覆盖100%的表面积,前提是必须正确遵循路径。路径跟踪的准确性对任务至关重要。目前有数百英亩需要测量。这些站点通常分为几个区域,每个任务最多可能需要4.5个小时。这些站点通常使用平行行进行调查。通过提高路径跟踪的精度,行之间的距离可以增加到接近地面穿透传感器的检测宽度。有两种方法可以提高路径跟踪的准确性:改善车辆定位并减少控制错误。 NTV当前能够实时计算其位置,平均精确度在6厘米以内。作者的博士研究涉及开发一种策略,该策略利用精确的定位系统并确保在速度(0-4.5 m / s)范围内的任何转向轮车辆都遵循正确的路径。该策略涉及标准化和平滑给定路径,实时计算目标位置,结合高级PID和自适应纯追随转向控制器的理想功能,自动校准转向反馈数据,自动调整控制参数的过程。 ,并根据车速安排控制增益。通过中继调谐器和黄金分割搜索(GSS)优化来完成自动调谐。该策略已在仿真中演示并在NTV上实现。

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