Huang Chao, Hong Meng, Wang Yaodong, Chai Hui, Hu Zhuo, Xiao Zheng, Guan Sijia, Guo Min
Hubei Provincial Engineering Research Center of Robotics & Intelligent Manufacturing, Wuhan University of Technology, Wuhan 430070, China.
School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Sensors (Basel). 2025 Jun 27;25(13):4026. doi: 10.3390/s25134026.
Wheel-legged robots combine the advantages of both wheeled robots and traditional quadruped robots, enhancing terrain adaptability but posing higher demands on the perception of foot-ground contact forces. However, existing approaches still suffer from limited accuracy in estimating contact positions and three-dimensional contact forces when dealing with flexible tire-ground interactions. To address this challenge, this study proposes a foot-ground contact state detection technique and optimization method based on multi-sensor fusion and intelligent modeling for wheel-legged robots. First, finite element analysis (FEA) is used to simulate strain distribution under various contact conditions. Combined with global sensitivity analysis (GSA), the optimal placement of PVDF sensors is determined and experimentally validated. Subsequently, under dynamic gait conditions, data collected from the PVDF sensor array are used to predict three-dimensional contact forces through Gaussian process regression (GPR) and artificial neural network (ANN) models. A custom experimental platform is developed to replicate variable gait frequencies and collect dynamic contact data for validation. The results demonstrate that both GPR and ANN models achieve high accuracy in predicting dynamic 3D contact forces, with normalized root mean square error (NRMSE) as low as 8.04%. The models exhibit reliable repeatability and generalization to novel inputs, providing robust technical support for stable contact perception and motion decision-making in complex environments.
轮腿式机器人结合了轮式机器人和传统四足机器人的优点，增强了地形适应性，但对脚底与地面接触力的感知提出了更高要求。然而，在处理柔性轮胎与地面的相互作用时，现有方法在估计接触位置和三维接触力方面的准确性仍然有限。为应对这一挑战，本研究提出了一种基于多传感器融合和智能建模的轮腿式机器人脚底与地面接触状态检测技术及优化方法。首先，利用有限元分析（FEA）模拟各种接触条件下的应变分布。结合全局灵敏度分析（GSA），确定聚偏二氟乙烯（PVDF）传感器的最佳布置并进行实验验证。随后，在动态步态条件下，利用从PVDF传感器阵列收集的数据，通过高斯过程回归（GPR）和人工神经网络（ANN）模型预测三维接触力。开发了一个定制实验平台，以复制可变的步态频率并收集动态接触数据进行验证。结果表明，GPR和ANN模型在预测动态三维接触力方面均具有较高的准确性，归一化均方根误差（NRMSE）低至8.04%。这些模型表现出可靠的可重复性和对新输入的泛化能力，为复杂环境中的稳定接触感知和运动决策提供了有力的技术支持。
