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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Terrain aware inversion of predictive models for high performance UGVs
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Terrain aware inversion of predictive models for high performance UGVs

机译：高性能UGV的地形模型预测模型反演

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摘要

The capacity to predict motion adequately over the time scale of a few seconds is fundamental to autonomous mobility. Model predictive optimal control is a general formalism within which most historical approaches can be cast as special cases. Applications continue to grow in ambition to seek higher precision of motion and/or higher vehicle speeds. Predictions must therefore improve in fidelity and speed simultaneously. We favor an approach to precision motion control that we call parametric optimal control. It formulates the optimal control problem as one of nonlinear programming - optimizing over a space of parameterized controls encoding all feasible motions. It enables efficient inversion of the solutions to the equations of motion for a ground vehicle. Such an inversion enables a computation of precisely the command signals necessary to drive the vehicle to goal position, heading, and curvature while following the contours of the terrain under arbitrary wheel terrain interactions. Dynamics inversion is so fundamental that many other mobility behaviors can be constructed from it. Fielded applications include pallet acquisition controls for factory AGVs, high speed adaptive path following for military UGVs, compensation for wheel slip on the Mars Exploration Rovers and full configuration space planning in dense obstacle fields.
机译：在几秒钟的时间范围内充分预测运动的能力对于自主移动性至关重要。模型预测最优控制是一种通用形式主义，在这种形式主义中，大多数历史方法都可以视为特殊情况。为了寻求更高的运动精度和/或更高的车速，应用不断增长。因此，预测必须同时提高保真度和速度。我们偏爱一种称为参数最优控制的精确运动控制方法。它将最佳控制问题表述为非线性编程之一-在对所有可行运动进行编码的参数化控制空间上进行优化。它可以使地面车辆的运动方程的解有效地反转。这样的反转使得能够精确地计算在任意轮地形相互作用下跟随地形的轮廓的同时将车辆驾驶到目标位置，前进方向和曲率所必需的命令信号。动力学反演是如此基础，以至于可以构造许多其他移动性行为。现场应用包括用于工厂AGV的托盘采集控制，用于军事UGV的高速自适应路径跟踪，补偿火星探测漫游车上的车轮打滑以及在密集障碍场中进行完整配置的空间规划。

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