In this research we present an algorithm for a six-wheeled robotic vehicle with articulated suspension (RVAS) to estimate the vehicle velocity and acceleration states, slip ratio and the tire forces. The estimation algorithm consists of six parts. In the first part, a wheel state estimator estimates the wheel rotational speed and its angular acceleration using Kalman filter, which is used to estimate the longitudinal tire force distribution in the second part. The third part is to estimate respective longitudinal, lateral, and vertical speeds of the vehicle and wheels. Based on these speeds, the slip ratio and slip angle are estimated in the fourth part. In the fifth part, the vertical tire force is then estimated. In the sixth part, the lateral tire force is then estimated. For a simulation test environment, the RVAS dynamic model is developed using Matlab and Simulink. The estimation algorithm is then verified in simulation using the vehicle test data and different test scenarios. It is found from simulation results that the proposed estimation algorithm can estimate the vehicle states, longitudinal tire forces efficiently. Moreover, a small prototype of the robotic vehicle is fabricated for experimental verification of the estimation algorithm. Various experiments are executed in pavement and off-road driving to estimate the wheel angular position, velocity and acceleration states and finally the slip ratio is estimated in these situations.

在这项研究中，我们提出了一种具有铰接式悬架（RVAS）的六轮机器人车辆的算法，以估算车辆的速度和加速度状态，滑移率和轮胎力。估计算法包括六个部分。在第一部分中，车轮状态估算器使用卡尔曼滤波器估算车轮转速及其角加速度，卡尔曼滤波器用于估算第二部分中的纵向轮胎力分布。第三部分是估计车辆和车轮的纵向，横向和垂直速度。基于这些速度，在第四部分中估计滑移率和滑移角。然后在第五部分中估算垂直轮胎力。然后在第六部分中估算侧向轮胎力。对于模拟测试环境，RVAS动态模型是使用Matlab和Simulink开发的。然后使用车辆测试数据和不同的测试场景在仿真中验证估计算法。从仿真结果发现，提出的估计算法可以有效地估计车辆状态，纵向轮胎力。而且，制造了机器人车辆的小型原型以用于估计算法的实验验证。在路面和越野驾驶中进行了各种实验，以估算车轮角位置，速度和加速度状态，最后在这些情况下估算滑移率。从仿真结果发现，提出的估计算法可以有效地估计车辆状态，纵向轮胎力。而且，制造了机器人车辆的小型原型以用于估计算法的实验验证。在路面和越野驾驶中进行了各种实验，以估算车轮角位置，速度和加速度状态，最后在这些情况下估算滑移率。从仿真结果发现，提出的估计算法可以有效地估计车辆状态，纵向轮胎力。而且，制造了机器人车辆的小型原型以用于估计算法的实验验证。在路面和越野驾驶中进行了各种实验，以估算车轮角位置，速度和加速度状态，最后在这些情况下估算滑移率。