Integration of a strapdown gravimeter system in an Autonomous Underwater Vehicle

Similar documents
Automated Tuning of the Nonlinear Complementary Filter for an Attitude Heading Reference Observer

Development and Field Deployment of a Novel AUV Gravimeter

FLUVIA NAUTIC DATASET (16 March 2007) Description of the sensor logs

Fundamentals of High Accuracy Inertial Navigation Averil B. Chatfield Table of Contents

1 Kalman Filter Introduction

TTK4190 Guidance and Control Exam Suggested Solution Spring 2011

Isobath following using an altimeter as a unique exteroceptive sensor

with Application to Autonomous Vehicles

UAV Navigation: Airborne Inertial SLAM

EE565:Mobile Robotics Lecture 6

GRACE Gravity Model GGM02

A Study of the Effects of Stochastic Inertial Sensor Errors. in Dead-Reckoning Navigation

A Study of Covariances within Basic and Extended Kalman Filters

Problem 1: Ship Path-Following Control System (35%)

r( θ) = cos2 θ ω rotation rate θ g geographic latitude - - θ geocentric latitude - - Reference Earth Model - WGS84 (Copyright 2002, David T.

Rao-Blackwellized Particle Filtering for 6-DOF Estimation of Attitude and Position via GPS and Inertial Sensors

VN-100 Velocity Compensation

A Low-Cost GPS Aided Inertial Navigation System for Vehicular Applications

Energy-Aware Coverage Path Planning of UAVs

Autonomous Navigation, Guidance and Control of Small 4-wheel Electric Vehicle

Model Reference Adaptive Control of Underwater Robotic Vehicle in Plane Motion

MARINE biologists, oceanographers, and other ocean researchers

Fundamentals of attitude Estimation

Autonomous Navigation for Flying Robots

Accurate measurements and calibrations of the MICROSCOPE mission. Gilles METRIS on behalf the MICRSCOPE Team

Distance Determination between the MINOS Detectors for TOF Measurements. Dr. Virgil Bocean Alignment & Metrology Department Fermilab

Autonomous Helicopter Flight via Reinforcement Learning

Design of Adaptive Filtering Algorithm for Relative Navigation

A Combined DGPS/INS and Synthetic Aperture Radar System for Geoid Referenced Elevation Models and Ortho-Rectified Image Maps

NAWCWPNS TM 8128 CONTENTS. Introduction Two-Dimensinal Motion Three-Dimensional Motion Nonrotating Spherical Earth...

Vehicle Dynamic Control Allocation for Path Following Moritz Gaiser

New satellite mission for improving the Terrestrial Reference Frame: means and impacts

Attitude Estimation Version 1.0

2D Image Processing. Bayes filter implementation: Kalman filter

2D Image Processing. Bayes filter implementation: Kalman filter

Navigation Mathematics: Kinematics (Earth Surface & Gravity Models) EE 570: Location and Navigation

GPS/INS Tightly coupled position and attitude determination with low-cost sensors Master Thesis

SLAM for Ship Hull Inspection using Exactly Sparse Extended Information Filters

EE 570: Location and Navigation

An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors

EKF and SLAM. McGill COMP 765 Sept 18 th, 2017

Inertial Navigation and Various Applications of Inertial Data. Yongcai Wang. 9 November 2016

Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey

Czech Technical University in Prague. Faculty of Electrical Engineering Department of control Engineering. Diploma Thesis

EE 570: Location and Navigation

On Board Mission Planning, Autonomous Software, LEO Satellite

Measurement Observers for Pose Estimation on SE(3)

Investigation of the Attitude Error Vector Reference Frame in the INS EKF

Gravity 3. Gravity 3. Gravitational Potential and the Geoid. Chuck Connor, Laura Connor. Potential Fields Geophysics: Week 2.

Application of state observers in attitude estimation using low-cost sensors

CS491/691: Introduction to Aerial Robotics

EE 570: Location and Navigation

Adaptive Unscented Kalman Filter with Multiple Fading Factors for Pico Satellite Attitude Estimation

Autonomous Mobile Robot Design

UAVBook Supplement Full State Direct and Indirect EKF

An Adaptive Filter for a Small Attitude and Heading Reference System Using Low Cost Sensors

Evaluation of different wind estimation methods in flight tests with a fixed-wing UAV

Nonlinear State Estimation! Extended Kalman Filters!

Trajectory Tracking of a Near-Surface Torpedo using Numerical Methods

Autonomous Mobile Robot Design

X t = a t + r t, (7.1)

TOWARDS AUTONOMOUS LOCALIZATION OF AN UNDERWATER DRONE. A Thesis. presented to. the Faculty of California Polytechnic State University,

A Mission to Planet Mars Gravity Field Determination

Attitude Estimation for Augmented Reality with Smartphones

STOCHASTIC MODELLING AND ANALYSIS OF IMU SENSOR ERRORS

Control of the Laser Interferometer Space Antenna

Robot Localisation. Henrik I. Christensen. January 12, 2007

Bayes Filter Reminder. Kalman Filter Localization. Properties of Gaussians. Gaussians. Prediction. Correction. σ 2. Univariate. 1 2πσ e.

COS Lecture 16 Autonomous Robot Navigation

Verification of a Dual-State Extended Kalman Filter with Lidar-Enabled Autonomous Hazard- Detection for Planetary Landers

CHAPTER 3 PERFORMANCE

Dependences in the pillar Earth s gravity field of

2D Image Processing (Extended) Kalman and particle filter

Improving Adaptive Kalman Estimation in GPS/INS Integration

State-Estimator Design for the KTH Research Concept Vehicle

Refinements to the General Methodology Behind Strapdown Airborne Gravimetry

Two dimensional rate gyro bias estimation for precise pitch and roll attitude determination utilizing a dual arc accelerometer array

2010 Small Satellite Systems and Services Symposium - Funchal, Madeira, Portugal 1

Instrumentation Commande Architecture des Robots Evolués

CS491/691: Introduction to Aerial Robotics

Mobile Robot Localization

Underwater glider navigation error compensation using sea current data

Potential Theory and the Static Gravity Field of the Earth

Localization and Correlation in Ensemble Kalman Filters

Kalman Filters with Uncompensated Biases

mdu G = Fdr = mgdr Dr. Clint Conrad POST 804 Gravity, the Geoid, and Mantle Dynamics Lecture: Gravity and the Geoid U G = G M r

The GOCE User Toolbox

Optimization-Based Control

Miscellaneous. Regarding reading materials. Again, ask questions (if you have) and ask them earlier

Position and Velocity USBL/IMU Sensor-based Navigation Filter

1128 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 19, NO. 5, SEPTEMBER 2011

316 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 36, NO. 2, APRIL Øyvind Hegrenæs, Member, IEEE, and Oddvar Hallingstad, Member, IEEE

Development and Design of the Landing Guidance and Control System for the S20 UAV

GOCE. Gravity and steady-state Ocean Circulation Explorer

Dynamic models 1 Kalman filters, linearization,

Ensemble Kalman Filter

Contents. Preface. 1 Introduction State of the art Outline... 2

CHAPTER 3 PERFORMANCE

EEE 187: Take Home Test #2

Transcription:

Integration of a strapdown gravimeter system in an Autonomous Underwater Vehicle Clément ROUSSEL PhD - Student (L2G - Le Mans - FRANCE) April 17, 2015 Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 1 / 24

Plan 1 Introduction Definition Interests & Applications Principle 2 Design & Equation Instrumentation & Carrier Equation of moving-base gravimetry 3 Performance assessment & Filtering Numerical simulations Filtering strategy 4 Further work Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 2 / 24

Introduction What is gravimetry? scalar gravimetry: g vectorial gravimetry: g x g y g z Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 3 / 24

Introduction Why do we measure gravity in the subsea domain? in geodesy: to improve the determination of the geoid in geophysics: to determine the distribution of masses in the ocean crust in navigation: to improve underwater navigation Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 4 / 24

Introduction How do we measure gravity in the subsea domain? Gravity is usually measured in units of acceleration [1 m.s 2 = 10 5 mgal] An instrument used to measure gravity is known as a gravimeter One can regard gravimeters as special-purpose accelerometers Unmanned Underwater Vehicle (UUV) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 5 / 24

Design & Equation Instrumentation LiMo-g : Light Moving gravimetry system Geodesy and Geomatic Lab (L2G) & Geodesy Lab (LAREG) Doctoral Thesis of Bertrand de Saint-Jean (2008) GRAVIMOB : MOBile GRAVImetry system Oceanic Domains Lab (LDO) Marcia Maïa (Head scientist) & Jean-François d Eu (Instrumentation engineer) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 6 / 24

Design & Equation Instrumentation Strapdown sensor Six electrostatic accelerometers Two triads (α & β) installed in a waterproof sphere of about 40 cm diameter Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 7 / 24

Design & Equation Carrier Autonomous Underwater Vehicule: AsterX IFREMER: French Research Institute for Exploitation of the Sea Navigation: INS + DVL + USBL able to dive down to 3, 000 m & travel up to 100 km total mass of 800 kg & scientific payload of 200 kg Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 8 / 24

Design & Equation Equation of moving-base gravimetry Application of Newton s Second Law: X i α Ẍ i α = g i α + a i α (1) position vector of the proof mass M α Ẍ i α g i α a i α second-order derivative of X i α gravitationnal acceleration restoring force per unit of mass projected onto the i-frame Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 9 / 24

Design & Equation Frames i-frame inertial-frame e-frame earth-frame (in rotation with respect to the i-frame) n-frame navigation-frame (defined by the geographic coordinates of the AUV: longitude λ P, latitude ϕ P and ellipsoidal height h P ) b-frame body-frame (defined by the attitude angles of the AUV: heading δ, pitch χ and roll η) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 10 / 24

Design & Equation Equation of moving-base gravimetry Only P position (λ P, ϕ P, h P ) is known: X i α = X i P + L i α = C i ex e P + C i bl b α (2) X P position vector of the vehicle reference point P L α C i e C i b lever arm PM α rotation matrix transforming e-frame into i-frame rotation matrix transforming b-frame into i-frame Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 11 / 24

Design & Equation Equation of moving-base gravimetry (neglecting the rotation of the Earth) ( ) gα n = Ce n Ẍ P e + Cb n Ω b ebω b eb + Ω b eb L b α Cb n aα b (3) g n α gravitational vector at point M α C n e rotation matrix transforming e-frame into n-frame (λ P, ϕ P ) Ẍ e P second-order derivative of X e P (λ P, ϕ P ) C n b rotation matrix transforming b-frame into n-frame (δ P, χ P, η P ) Ω b eb skew symmetric matrix associated with the rotation of the b-frame with respect to the e-frame (λ P, ϕ P, δ P, χ P, η P ) L b α a b α lever arm PM b α restoring force per unit of mass Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 12 / 24

Design & Equation Equation of moving-base gravimetry (neglecting the rotation of the Earth) ( gα n = Ce n Ẍ P e + Cb n Ω b ebω b eb + Ω ) b eb L b α Cb n aα b (4) g n β = C n e Ẍ e P + C n b And g n P? Under the assumption that L α = L β : ( Ω b ebω b eb + Ω ) b eb L b β Cb n aβ b (5) gp n g α n + gβ n 2 ( ) a b gp n Ce n Ẍ P e Cb n α + aβ b 2 (6) (7) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 13 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo) g n P C n e Ẍ e P C n b f : λ P, ϕ P, h P δ, χ, η a α, a β ( a b α + a b β f multivariate function mapping R 12 into R 3 2 ) (8) g P (9) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 14 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo) λ P + ɛ λ, ϕ P + ɛ ϕ, h P + ɛ h f : δ + ɛ δ, χ + ɛ χ, η + ɛ η a α + ɛ α, a β + ɛ β g P + ɛ g (10) ɛ θ ɛ g N random draws additive noise term, θ = λ, ϕ, h, δ, χ, η error on gravity vector E[ɛ g ] = 1 N ɛ g,i (11) N i=1 σ[ɛ g ] = E [(ɛ g E[ɛ g ]) 2 ] (12) Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 15 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo) Reference gravity field derives from a geological model of oceanic crust bathymetric survey (2.70 g.cm 3 ) distribution of mineral blocks (3.85 g.cm 3 ) Reference trajectory of the AUV derives from a test mission carried out by the IFREMER deterministic models polynomial & periodic functions 12 profiles, each about 3, 600 m long, h P = 2200 m Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 16 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo) 3 Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 17 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo): effect of the AUV positionning uncertainty uncertainty = 0.1 % of the travelled distance (manufacturer s informations) ɛ λ & ɛ ϕ are modelled by a double integration of a Gaussian White Noise process ɛ h is modelled by a simple Gaussian White Noise process Results are consistent with the fact that the uncertainty affecting the coordinates h P is more likely to perturb the restitution of the vertical component g u Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 18 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo): effect of the AUV attitude uncertainty uncertainty = 0.02 deg (δ) & 0.01 deg (χ & η) (manufacturer s informations) ɛ δ, ɛ χ & ɛ η are modelled by a Gaussian White Noise process Components g e & g n are more affected by the attitude uncertainty than the vertical component g u Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 19 / 24

Performance assessment & Filtering Numerical simulations (Monte-Carlo): effect of the accelerometer uncertainties Stochastic processes are identified thanks to Allan Variance ɛ α & ɛ β are modelled by a White Noise and a first order Random Walk processes The low pass filtering has no effect on the noise reducing because of the non-stationnary nature of the first order random walk process. Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 20 / 24

Performance assessment & Filtering Filtering strategy Kalman Filter only for linear system Extented Kalman Filter needs Jacobian matrix to be estimated (implementation errors & time-consuming) may lead to an improper estimation of the covariance matrix if the linearity hypothesis is not respected (divergence of the filter) Unscented Kalman Filter does not require the calculation of Jacobian matrix relies on a deterministic sampling method Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 21 / 24

Performance assessment & Filtering Filtering strategy Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 22 / 24

Further Work Calibration scale factor & bias of each accelerometer transformation matrix C b s Improve Unscented Kalman Filtering complex noise models spatial variability of the gravity field Test mission scheduled on March 2016 off the Mediterrean coasts in the south of France Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 23 / 24

Thank you for your attention We are indebted to the French Ministry of Defence and the Pays de la Loire Region for their support of this work. Jérôme Verdun - supervisor Marcia Maïa - co-supervisor José Cali Jean François d Eu www.clementroussel-portfolio.fr clement.roussel@cnam.fr Clément ROUSSEL ISPRS / CIPA Workshop April 17, 2015 24 / 24