Experimental kinematics modeling estimation for wheeled skid-steering mobile robots

Wu, Yao; Wang, Tianmiao; Liang, Jianhong; Chen, Jiao; Zhao, Qiteng; Yang, Xingbang; Han, Chenhao

doi:10.1109/robio.2013.6739470

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The differential drive motor control technique has been taken from learning robotics using python as presented in [20]. Paper [21] explains the motion planning of the robot using the differential drive and trajectory smoothing using optimization techniques.Mathematical modeling and behavior of the robot are explained using the skid steering model, the tracking of the robot is also mentioned in paper [22].…”

Section: Related Workmentioning

confidence: 99%

Angular Orientation of Steering Wheel for Differential Drive

Megalingam¹,

Nagalla²,

Pasumarthi³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Several drive mechanisms for different robots are at hand in current days. Bicycle steering, Ackerman steering, differential drive are some principal drive mechanisms that are being deployed in robots these days. The differential drive needs the wheel rotations to be updated very frequently. But it is most commonly deployed on the robots with two wheels and casters, as discussed in this work. It also can be used to have an independent control for each of the wheels with independent control signals. This work deals with the modeling of the differential drive mechanism for a robot with two main drive wheels and two casters, which takes the angular orientation of the steering wheel as input. For simplicity, this work considers that casters have no influence on any aspect of the differential drive. An adaptive model, whose output depends on the real-time input from the gamers steering wheel and produces required output has been formulated. This work differs from the other differential drives in the context that the steering wheel and the robot wheels have no physical connection. The proposed model has been implemented in python and integrated with the Robot Operating System (ROS). The steering wheel, which is used to generate commands using, is attached to the controller at the control station and the ROS_Node thus created is used to read the values from the steering and generate commands for each of the left and right wheels. These commands are transferred to the controller on the mobile platform, which in turn generates control signals for actuators. This work also deals with the deployment of the proposed model using the Universal Robot Description Format (URDF) of the robot in the Gazebo simulation and evaluating it using the Nitho Drive Pro steering wheel. To prove that the differential drive mechanism can be used to control the robot efficiently in any type of terrain, a ROS python node is used to control and maneuver the robot.

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Section: Related Workmentioning

confidence: 99%

Angular Orientation of Steering Wheel for Differential Drive

Megalingam¹,

Nagalla²,

Pasumarthi³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

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“…The ICRs lie on or outside the tracks' central lines when slippage does or does not occur [10]. Hence, y ℓ − y r ≥ −W where W denotes the robot width.…”

Section: Icr Kinematics Modelmentioning

confidence: 99%

“…In [9], two different methods were presented for the offline estimation of ICRs: the stationary response simulation of the dynamics model within the whole range of velocity, and the genetic algorithm based model parameters extracted from the gathered readings of sensors. In [10,11], an empirical model combining ICRs and a robot's kinematics state (i.e., the forward speed and radius of the path curvature) was established experimentally, which has been proven to improve the performance of dead reckoning significantly. Similar work in [12,13] has been described.…”

Section: Introductionmentioning

confidence: 99%

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Wang

et al. 2018

Complexity

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As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.

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“…Due to the fact that the friction force depends on the linear and angular velocities, the force equilibrium equation perpendicular to the tracks becomes a non-integrable differential equality constraint [3]. Maneuvering capabilities also depend on the complex vehicle-ground interaction [7].…”

Section: Introductionmentioning

confidence: 99%

“…Some authors addressed this problem in their researches. For example, Wu et al [7] proposed a method for experimental estimation of the wheeled vehicle kinematics. In their work, they applied approximating function with identifiable parameters to derive the relationship between the instantaneous center of rotation of the vehicle, its speed and path curvature.…”

Section: Introductionmentioning

confidence: 99%

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Strawa¹,

Ignatyev²,

Zolotas³

et al. 2021

Electronics

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Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.

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Experimental kinematics modeling estimation for wheeled skid-steering mobile robots

Cited by 9 publications

References 12 publications

Angular Orientation of Steering Wheel for Differential Drive

Angular Orientation of Steering Wheel for Differential Drive

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

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