Kinematic Modelling and State Estimation of Exploration Rovers

This is a post-peer-review, pre-copyedit version of an article published in IEEE Robotics and Automation Letters. The final authenticated version is available online at: http://dx.doi.org/10.1109/LRA.2019.2895393.[Abstract] State estimation is crucial for exploration rovers. It provides the pose and velocity of the rover by processing measurements from onboard sensors. Classical wheel odometry only employs encoder measurements of the two wheels in the differential drive. As a consequence, input noise can lead to large uncertainties in the estimated results. Also, the estimation models used in classical wheel odometry are nonlinear, and the linearization process that propagates the mean and covariance of the estimated state introduces additional errors in the process. This letter puts forward a novel wheel odometry approach for six-wheeled rovers. A kinematic model is formulated to relate the velocity of the wheels and the chassis, and later used to develop the corresponding estimation model. The components of the velocity of the chassis, decomposed in the chassis-fixed coordinate frame, are selected as the system state in the estimation, which results in a linear model. The motions of all wheels are fused together to provide the measurements. Wheel slip is considered random Gaussian noise in this kinematic model. The continuous-time Kalman filter is employed to process the model. Experimental validation with six-wheeled rover prototypes was used to confirm the validity of the proposed approach.MINECO; RYC-2016-2022

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Modelling and state estimation of exploration rovers

State estimation is an important element in rover exploration missions. The objective of state estimation is to determine the pose and velocity of the rover by processing measurements from onboard sensors. It usually includes fusing measurement data from different types of sensors. Wheel encoders represent one of the fundamental categories of sensors used for estimation.The so-called classical wheel odometry is widely used in various rover applications. It estimates the rover state by tracking the motions of the two middle wheels in the differential drive based on the related wheel encoders. However, this technique has certain limitations. First, it does not employ redundant measurements. As a consequence, input noise can lead to large uncertainties in the estimated results. Second, it contains a nonlinear estimation model because of the trigonometric functions of the rover orientation. The linearization process that propagates the mean and covariance of the estimated state introduces additional errors. More importantly, it cannot detect the wheel slip and accumulates large estimated errors for rover travelling on soft terrain.The objective of this thesis is to investigate how state estimation can be improved by combining wheel encoder measurements with kinematics, dynamics, and terramechanics modelling. Kinematic and dynamic models are developed in the thesis for a range of rover maneuvres. The interaction between the wheels and soft terrain is also modelled and analyzed employing terramechanics models. A procedure for online soil parameter identification is proposed based on the sensitivity analysis of the terrain traction forces with respect to the variations of the soil parameters.Three state estimation techniques at different levels are developed.The first one is kinematics-based estimation. An estimation model using the Kalman Filter approach is established based on the kinematic model of the rover that relates the velocity of the chassis to the motion all wheels.The chassis velocity components with respect to the chassis-fixed coordinate system are selected as system state variables. The encoder measurements from all wheels are fused together to provide redundant measurements. The longitudinal and lateral types of slip of all wheels are modelled as random Gaussian noise. The continuous-time Kalman Filter is employed to process the estimation model and measured data.The second technique proposed is dynamics-based estimation. The rover dynamics and terramechanics are combined to represent the relations between the rover motion and wheel/terrain interaction forces. The variables of the chassis velocity, wheel velocity, wheel slip, motor torque, and terrain reaction forces can be determined using the measurements from wheel encoders and the combined dynamic and terramechanics model.The third method developed puts forward a framework that integrates the previous two techniques. The accuracy of estimation is generally related to how well the slip under the wheels can be determined. A new decomposition of wheel slip is proposed here to conflicting and compatible slip components. This decomposition takes into account the rover kinematics that represents the geometric constraints among the wheels. These two slip components can then be considered separately using the two techniques described above. The state of the rover considering the conflicting slip component can be estimated using the kinematics-based technique and the compatible slip component can be solved for using the dynamics-based technique. This resulting combined method provides the best solution. The proposed estimation techniques were investigated and validated by experiments employing two rover prototypes.L'estimation d'état est un élément important dans les missions d'exploration de rovers. L’estimation de l’état a pour objectif de déterminer la pose et la vitesse du mobile en effectuant des mesures à partir de capteurs embarqués. Cela inclut généralement la fusion des données de mesure provenant de différents types de capteurs. Les codeurs de roue représentent l’une des catégories fondamentales de capteurs utilisés pour l’estimation. L'odométrie dite classique des roues est largement utilisée dans diverses applications mobiles. Il estime l'état du mobile en suivant les mouvements des deux roues centrales dans la transmission différentielle en fonction des codeurs de roue correspondants. Cependant, cette technique a certaines limites. Premièrement, il n’utilise pas de mesures redondantes. En conséquence, le bruit d'entrée peut générer de grandes incertitudes dans les résultats estimés. Deuxièmement, il contient un modèle d’estimation non linéaire en raison des fonctions trigonométriques de l’orientation du mobile. Le processus de linéarisation qui propage la moyenne et la covariance de l'état estimé introduit des erreurs supplémentaires. Plus important encore, il ne peut pas détecter le patinage des roues et accumule de grosses erreurs estimées pour les rovers se déplaçant sur des terrains meubles. L'objectif de cette thèse est d'étudier comment améliorer l'estimation de l'état en combinant les mesures de codeur de roue avec la modélisation cinématique, dynamique et terramécanique. Des modèles cinématiques et dynamiques sont développés dans la thèse pour une gamme de manœuvres de mobiles. L'interaction entre les roues et le terrain mou est également modélisée et analysée à l'aide de modèles de terramécanique. Une procédure d'identification en ligne des paramètres de sol est proposée, basée sur l'analyse de sensibilité des forces de traction du terrain vis-à-vis des variations des paramètres de sol. Trois techniques d'estimation d'état à différents niveaux sont développées.Le premier est une estimation basée sur la cinématique. Un modèle d'estimation utilisant l'approche du filtre de Kalman est établi sur la base du modèle cinématique du mobile qui relie la vitesse du châssis au mouvement de toutes les roues.Les composantes de la vitesse du châssis par rapport au système de coordonnées fixé par le châssis sont sélectionnées en tant que variables d'état du système. Les mesures du codeur de toutes les roues sont fusionnées afin de fournir des mesures redondantes. Les types de glissement longitudinal et latéral de toutes les roues sont modélisés comme un bruit gaussien aléatoire. Le filtre de Kalman en temps continu est utilisé pour traiter le modèle d’estimation et les données mesurées.La deuxième technique proposée est l'estimation basée sur la dynamique. La dynamique et la terramécanique du mobile sont combinées pour représenter les relations entre le mouvement du mobile et les forces d'interaction roue / terrain. Les variables de la vitesse du châssis, de la vitesse de la roue, du patinage de la roue, du couple du moteur et des forces de réaction du terrain peuvent être déterminées à l'aide des mesures des encodeurs de roue et du modèle combiné dynamique et terramécanique. La troisième méthode développée propose un cadre intégrant les deux techniques précédentes. La précision de l'estimation dépend généralement de la qualité de la détermination du glissement sous les roues. Une nouvelle décomposition du patinage des roues est proposée ici pour les composants antidérapants et compatibles. Cette décomposition prend en compte la cinématique du mobile qui représente les contraintes géométriques entre les roues. Ces deux composants de glissement peuvent ensuite être considérés séparément en utilisant les deux techniques décrites ci-dessus

Vibration analysis based feature extraction for bearing fault detection

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures. \ua9 (2012) Trans Tech Publications, Switzerland.Peer reviewed: YesNRC publication: Ye

Particle filtering-based methods for time to failure estimation with a real-world prognostic application

One of core technologies for prognostics is to predict failures before they occur and estimate time to failure (TTF) by using built-in predictive models. The predictive model could be either physics-based model or machine learning-based model. Machine learning-based predictive modeling is an emerging application of machine learning to machinery maintenance. Accurate TTF estimation could help performing predictive action “just-in-time”. However, the developed predictive models sometimes fail to provide a precise TTF estimate. To address this issue, we propose a Particle Filtering (PF)-based method to estimate TTF. After introducing the PF-based algorithm, we present the implementation along with the experimental results obtained from a case study of Auxiliary Power Unit (APU) prognostics. To our best knowledge, this is the first application of PF-based method to APU prognostic. The results demonstrated that the PF-based method is useful for estimating TTF for predictive maintenance and it greatly improved TTF estimation precision for APU prognostics

Structural basis of a histone H3 lysine 4 demethylase required for stem elongation in rice.

Histone lysine methylation is an important epigenetic modification in regulating chromatin structure and gene expression. Histone H3 lysine 4 methylation (H3K4me), which can be in a mono-, di-, or trimethylated state, has been shown to play an important role in gene expression involved in plant developmental control and stress adaptation. However, the resetting mechanism of this epigenetic modification is not yet fully understood. In this work, we identified a JmjC domain-containing protein, JMJ703, as a histone lysine demethylase that specifically reverses all three forms of H3K4me in rice. Loss-of-function mutation of the gene affected stem elongation and plant growth, which may be related to increased expression of cytokinin oxidase genes in the mutant. Analysis of crystal structure of the catalytic core domain (c-JMJ703) of the protein revealed a general structural similarity with mammalian and yeast JMJD2 proteins that are H3K9 and H3K36 demethylases. However, several specific features were observed in the structure of c-JMJ703. Key residues that interact with cofactors Fe(II) and N-oxalylglycine and the methylated H3K4 substrate peptide were identified and were shown to be essential for the demethylase activity in vivo. Several key residues are specifically conserved in known H3K4 demethylases, suggesting that they may be involved in the specificity for H3K4 demethylation