scholarly journals Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Cuifeng Wang ◽  
Wenjun Lv ◽  
Xiaochuan Li ◽  
Mingliang Mei
Keyword(s):  
Adaptive Estimation ◽  
Traditional Approach ◽  
Transition Probability ◽  
Estimation Method ◽  
Temporal Correlation ◽  
Learning System ◽  
Terrain Classification ◽  
Integrated Sensor ◽  
Bayesian Filter ◽  
Kinematics Model

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.

A reliable adaptive estimation method for the installation error of optical angular encoder in outfield tests

Measurement ◽  
2021 ◽  
Vol 174 ◽  
pp. 109035
Author(s):  
Xuxing Zhao ◽  
Renjian Feng ◽  
Yinfeng Wu ◽  
Ning Yu ◽  
Xiaofeng Meng ◽  
...  
Keyword(s):  
Adaptive Estimation ◽  
Estimation Method

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Adaptive calibration of Diesel engine injection for minimising fuel consumption with constrained NOx emissions in actual driving missions

2020 ◽  
pp. 146808742091880
Author(s):  
José Manuel Luján ◽  
Benjamín Pla ◽  
Pau Bares ◽  
Varun Pandey
Keyword(s):  
Diesel Engine ◽  
Fuel Consumption ◽  
Time Window ◽  
Transition Probability ◽  
Estimation Method ◽  
Engine Performance ◽  
Driving Cycle ◽  
Vehicle Speed ◽  
Adaptive Calibration ◽  
Emission Limits

This article proposes a method for fuel minimisation of a Diesel engine with constrained [Formula: see text] emission in actual driving mission. Specifically, the methodology involves three developments: The first is a driving cycle prediction tool which is based on the space-variant transition probability matrix obtained from an actual vehicle speed dataset. Then, a vehicle and an engine model is developed to predict the engine performance depending on the calibration for the estimated driving cycle. Finally, a controller is proposed which adapts the start-of-injection calibration map to fulfil the [Formula: see text] emission constraint while minimising the fuel consumption. The calibration is adapted during a predefined time window based on the predicted engine performance on the estimated cycle and the difference between the actual and the constraint on engine [Formula: see text] emissions. The method assessment was done experimentally in the engine test set-up. The engine performace using the method is compared with the state-of-the-art static calibration method for different [Formula: see text] emission limits on real driving cycles. The online implementation of the method shows that the fuel consumption can be reduced by 3%–4% while staying within the emission limits, indicating that the estimation method is able to capture the main driving cycle characterstics.

scholarly journals Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6550 ◽  
Author(s):  
Chen Cheng ◽  
Ji Chang ◽  
Wenjun Lv ◽  
Yuping Wu ◽  
Kun Li ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Classification Accuracy ◽  
Concept Drift ◽  
Dynamic Environment ◽  
Temporal Correlation ◽  
Classification Methods ◽  
Terrain Classification ◽  
Localization Accuracy ◽  
Learning Framework ◽  
Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

Adaptive state estimation for cyber physical systems under sparse attacks

2018 ◽  
Vol 41 (6) ◽  
pp. 1571-1579 ◽  
Author(s):  
Hao Zhang ◽  
Chen Peng ◽  
Hongtao Sun ◽  
Dajun Du
Keyword(s):  
State Estimation ◽  
Adaptive Estimation ◽  
Computing Time ◽  
Estimation Method ◽  
Cyber Physical Systems ◽  
Estimation Problem ◽  
Step Size ◽  
Estimation Errors ◽  
Physical Systems ◽  
Dynamic Estimation

This paper investigates the state estimation problem for cyber physical systems under sparse attacks. Firstly, the fundamental state estimation problem is transferred to an optimization problem with a unique solution. Secondly, an adaptive estimation method for sparse attacks is proposed, which convergence property is well proved. The advantage of proposed method is that the step-size can be adaptively adjusted based on the dynamic estimation errors. Therefore, the computing time is less than some existing methods while guaranteeing the desired performance. Then, a suitable state feedback is designed to improve the computing speed while enhancing the resiliency for the destroyed system. Finally, the speed performance and accuracy of proposed algorithm are verified by two numerical examples.

A nonlinear adaptive estimation method based on local approximation

1997 ◽  
Vol 45 (7) ◽  
pp. 1831-1841 ◽  
Author(s):  
Y. Iiguni ◽  
I. Kawamoto ◽  
N. Adachi
Keyword(s):  
Adaptive Estimation ◽  
Estimation Method ◽  
Local Approximation

Remote Access to Scientific Laboratory Equipment and Competency-Based Approach to Science and Technology Education

2014 ◽  
Vol 10 (3) ◽  
pp. 19-33 ◽  
Author(s):  
M.I. Mazuritskiy ◽  
S.A. Safontsev ◽  
B.G. Konoplev ◽  
A.M. Boldyreva
Keyword(s):  
Experimental Work ◽  
Traditional Approach ◽  
Science And Technology ◽  
Remote Access ◽  
Learning System ◽  
Skilled Workers ◽  
Laboratory Equipment ◽  
Practical Applications ◽  
E Learning ◽  
Competency Based

This article describes the competency-based approach to e-learning education that utilizes remote access to the laboratory equipment. The main focus of the paper is the structure and design of the e-learning system used in the Southern Federal University (Russia). The article discusses the related pedagogical strategies and presents system's features in the context of the education for skilled workers. This approach uses access to the scientific and technology laboratory equipment either for provisioning of the individualized educational programs or to enable the students who are unable to attend a conventional laboratory for a variety of reasons, such as disability, and part-time study to conduct the experimental work. It will be shown that the experimental work involving the modern scientific equipment is an important aspect of the learning process in the areas of material science and nanotechnology. The learning strategy is the reverse of that used in the traditional approach. The authors suggest to start with the introduction to the practical applications of science and technology relevant to the current job market, and study the general laws and theoretical principles afterwards, to deepen understanding and achieve the educational goals.

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