Position Estimation Method for Unmanned Tracked Vehicles Based on a Steering Dynamics Model

Abstract

A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems.

Keywords:

unmanned tracked vehicles; steering dynamics model; position estimation

1. Introduction

Unmanned tracked vehicles have broad development prospects in the agriculture [1] and fire protection [2] fields due to their good trafficability and mobility characteristics. Real-time and accurate positioning is important for ensuring the normal operation of unmanned tracked vehicles. Currently, most of the accurate position information obtained for unmanned tracked vehicles is dependent on high-precision positioning systems, such as inertial navigation components and differential positioning devices [3,4]. For unmanned tracked vehicles working in special environments, such as woodland, mountain, or underground environments, the reliability of high-precision positioning systems is difficult to guarantee. Some unmanned tracked vehicles use the positioning method of matching radar point cloud and point cloud map. This method reduces the dependence of unmanned tracked vehicles on their high-precision positioning systems to a certain extent when they already have point cloud maps [5,6,7]. For unmanned tracked vehicles working in unknown or open environments, it is difficult to obtain the required positioning accuracy when using the point cloud matching method. Therefore, improving the positioning accuracy in special working environments without relying on high-precision positioning systems is very important to the further development of unmanned tracked vehicles.

Many scholars around the world have conducted research regarding position estimation methods for unmanned tracked vehicles that do not rely on high-precision positioning systems. The most traditional of these methods involves using the speed data of the active wheel of an unmanned tracked vehicle to estimate the real-time position of the vehicle from the steering kinematics model [8]. Because the kinematics model ignores the relative sliding between the tracks and the ground, this method produces large positioning errors [9]. Martinez [10] established an equivalent-steering approximate kinematics model, processed the original trajectory data of a vehicle using the genetic algorithm, and estimated the vehicle position in real time using a reliable positioning system. Xiong [11] established a vehicle kinematics model that considered the sliding parameters. During a vehicle turning process, by comparing the position estimated by the kinematics model with the position measured by a high-precision positioning system, the Leven–Marquardt algorithm was used to estimate the real-time sliding parameters. Rogers [12] established a three-dimensional kinematics model of a vehicle and used the Kalman filter algorithm to estimate the sliding parameters in real time using a high-precision positioning sensor. Moosavian [13] carried out a large number of real vehicle experiments. The sliding parameters in the experimental data were linearly fitted with the corresponding steering radius, and the vehicle position was corrected in real time by feedforward compensation. Using a kinematics model to estimate the position of an unmanned tracked vehicle reduces the dependence on a high-precision positioning system to some extent. However, to improve the position estimation accuracy, it is still necessary to correct the results obtained from the kinematics model in real time [14]. This paper proposes a position estimation method for unmanned tracked vehicles that is based on a steering dynamics model and then compares the results with those of a position estimation method based on a kinematics model.

Research regarding the steering force model for tracked vehicles is becoming increasingly mature. Purdy [15] used Coulomb’s law to calculate the steering force of tracked vehicles under the assumption that the ground pressure generated by the tracked vehicle is uniform. However, when a tracked vehicle turns while traveling at a high speed, the track force calculated by this model does not change as the steering radius changes. Thus, the calculated force does not conform to the actual track force during the turning process [16]. Under the assumption that the track is subjected to uniform force, Wong [17] analyzed the steady-state steering of tracked vehicles by using the shear force–displacement model. Tang [18] assumed that the tracked ground pressure was trapezoidal distribution when the tracked vehicle turned, and based on this assumption, the steady-state steering process of the tracked vehicle was analyzed. Under the assumption that the ground pressure of the track is rectangular and concentrated in each load wheel, Wang [19] calculated the relationship between the force of the track on both sides and the shear displacement under the steady steering condition of the tracked vehicle by using the numerical iteration method. For the study of steady-state steering of tracked vehicles, only the motion parameters in the steering process of tracked vehicles can be analyzed. It is necessary to study the dynamic steering process of tracked vehicles for position estimation and motion control of tracked vehicles. Özdemir [20] improved the steering dynamics model established by Wong and realized the analysis of the dynamic steering process of tracked vehicles.

During this study, the relationship between shear displacement and the speed and yaw rate during the steering process of tracked vehicles was systematically analyzed. The shear force–displacement model was used to calculate the track force, and a steering dynamics model suitable for real-time positioning of tracked vehicles was established. The proposed position estimation method for unmanned tracked vehicles was verified by simulations and experiments with actual vehicles. The experimental results showed that the position estimation method proposed in this paper, which does not rely on a high-precision positioning system, exhibited a better position estimation accuracy than a position estimation method based on the kinematics model.

2. Steering Dynamics Model for Tracked Vehicles

In order to analyze the force of the track during the steering process of the tracked vehicle, three main assumptions are made:

(1)

The tracked vehicle does not deform during the turning process, and the position of the center of mass is always located at the geometric center.

(2)

In the steering process of the tracked vehicle, the stretching and bulldozing effects of the track are ignored.

(3)

The changes in the track force can be obtained from the shear force–displacement model, which can be expressed by Equation (1):