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首页> 外文会议>International conference on informatics in control, automation and robotics;ICINCO >Soil Identification and Control-Parameter Estimation for Optimal Driving of Wheeled Robots on Rough Terrain
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Soil Identification and Control-Parameter Estimation for Optimal Driving of Wheeled Robots on Rough Terrain

机译：崎Terra地形上轮式机器人最优驱动的土壤辨识与控制参数估计

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Wheeled robots on rough terrain are needed to effectively change wheel control strategies since optimal slip and maximum traction levels differ depending on soil types such as sandy soil, grassy soil or firm soil. In a view point of wheel control, this paper focuses on a prediction method of optimal control parameters such as optimal slip ratio and traction coefficient acting on wheels to maximize traction or minimize energy consumption. In this paper, optimal control parameter (OCP) models based on surface reaction index (SRI) are experimentally derived using characteristic data from wheel-soil interaction through indoor experiments by a testbed for analysis of wheel-soil interaction on three types of soil; grass, gravel and sand. For estimating surface reaction index (SRI), actual traction coefficient, including information of motion resistance, is observed by a state estimator which is constructed from longitudinal wheeled robot dynamics. The actual traction coefficient and slip ratio on wheels are employed to estimate surface reaction index (SRI) by a numerical method on the basis of derived optimal models. The proposed algorithm is verified through outdoor driving experiments of a wheeled robot on various types of soil.
机译：由于最佳滑移和最大牵引力水平会根据土壤类型（例如沙土，草皮土壤或坚硬土壤）而有所不同，因此需要在崎terrain地形上的轮式机器人才能有效地改变车轮控制策略。从车轮控制的角度出发，本文重点研究了最佳控制参数的预测方法，例如最优滑移率和作用在车轮上的牵引系数，以最大程度地提高牵引力或最小化能耗。本文通过利用轮床-土壤相互作用的特征数据，通过室内试验，通过试验平台，通过分析三种类型土壤上轮-土壤相互作用的实验数据，得出了基于表面反应指数（SRI）的最优控制参数（OCP）模型。草，砾石和沙子。为了估计表面反应指数（SRI），由状态估计器观察实际的牵引系数，包括运动阻力信息，该状态估计器由纵向轮式机器人动力学构造而成。利用车轮上的实际牵引系数和滑移率，在导出的最优模型的基础上，通过数值方法估算表面反应指数（SRI）。通过轮式机器人在各种土壤上的户外驾驶实验验证了该算法的有效性。

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