气垫式越野机器人土壤参数分步识别

Step-by-step Identification of Soil Parameters by Air-cushion-typed Off-road Robots

土壤参数在线识别需要解决由多参数耦合引起的估值多解问题以及由状态噪声和测量噪声引起的估值准确性问题。为了解决估值多解问题,利用气垫式越野机器人的垂向力控制自由度,分别设计g-EKF算法、对数均值法和最小二乘法对3个推力相关土壤参数、3个垂向接地压力相关土壤参数和2个推土阻力相关土壤参数进行分步识别。为了解决估值准确性问题,在第一步g-EKF算法中,采用采样点选择、样本重排和测量值修正等多种措施抑制状态噪声和测量噪声的影响;在第二步对数均值法中,利用对数变换抑制非对称分布噪声对均值计算的影响。开展多角度的试验研究,包括各步估值算法的可行性和准确性、分步估值准确性的递次影响、土壤参数估值准确性对能耗优化的影响。试验结果表明,在各种噪声水平的工况下,提出的土壤参数分步识别策略及相应算法均能够有效解决多解问题和准确性问题。

On-line identifying soil parameters should address the multiple-solution problem caused by the coupling of soil parameters and the accuracy problem caused by state noises and measurement noises. For the multiple-solution problem, by taking advantage of the additional degree of control freedom of air-cushion-typed off-road robots for vertical forces, the g-EKF algorithm, logarithmic-mean algorithm and least-squares method are designed to identify, step by step, the three tractive forces-related soil parameters, the three vertical forces-related soil parameters and the two bulldozing resistances-related soil parameters. For the accuracy problem, several measures are implemented within the g-EKF algorithm in step 1 to reduce the influences of the state noises and measurement noises, consisting of selecting reasonable sampling points and rearranging and adjusting measurements; as well, in the logarithmic-mean algorithm in step 2, the logarithmic transformation is used to inhibit the impact of asymmetrically distributed noises on the mean calculation. Comparative experiments are conducted to examine the feasibility and accuracy of the designed estimation algorithm.s in each step, the propagated impacts of estimation accuracy of the forward step（s） on that of the later step（s）, and the impact of estimation accuracy of the soil parameters on energy consumption optimization. The results show that the proposed step-by-step identification strategy and the corresponding algorithms are competent to solve the multiple-solution problem and the accuracy problem in the examined working conditions with different levels of noises.