中文摘要：

正确地在线识别土壤参数是软地面越野机器人运行性能优化和控制的基础,其实施需要解决多解问题和准确性问题。利用气垫式机器人的垂向力控制自由度,提出g算法对3个土壤推力参数进行解耦和识别,能够解决多解问题。g算法的实施需要确定3个采样点,需要限制由状态噪声和测量噪声引起的土壤参数估值误差,因此有必要建立合理的采样点选取规则。其方法如下：将估值误差的减小具体表征为3方面,经数学推理分别建立采样点选取规则,再得出折中方案。结合一个工程实例进行了不同状态噪声和测量噪声水平下的估值准确性试验。试验结果表明：①在各种噪声水平下,尽管存在或多或少的误差,g算法均能够识别出3个土壤推力参数;②在各种噪声水平下,根据选取规则得到的理想采样点组合相对于随机组合具有明显优势;③系统的非线性导致状态噪声和测量噪声均对g算法的估值准确性有较大影响。上述结果显示出针对气垫式越野机器人提出g算法及其采样点选取规则的必要性和可行性。

英文摘要：

Correct on-line identification of soil parameters is the basis of performance optimization and control for robots navigating on soft terrain, in which, the problems of multiple-solution and accuracy should be solved. By taking advantage of the additional degree of control freedom of air-cushion-typed off-road robots for vertical force to eliminate the multi-solution problem, a new identification algorithm, g-function algorithm, is proposed to estimate the three soil parameters related to the tractive effort in real time. The implementation of the g-function algorithm requires three sampling points so that it is essential to establish their selection rules to decrease the estimation error sourced from state noises and measurement noises. This decrease is embodied in three aspects, with respect to which, selection rules are respectively established by mathematical derivation firstly and then are balanced after a trade-off consideration. Based on an engineering example, experiments on estimation accuracy are conducted in different levels of state noises and measurement noises. The results show： ① The g-function algorithm succeeds to identify the three targeted soil parameters in all noise levels despite existing more or less errors; ②The ideal combination of sampling points that is obtained by using the selection rules presents an obvious advantage over random combinations in all noise levels; ③System nonlinearity makes estimation accuracy sensitive to both state noises and measurement noises. The above results, therefore, support the necessity and feasibility of the g-function algorithm and its sampling points＇ selection rules.