A New Paradigm forIntegrated Navigation of UAVsBased on Vehicle Dynamic Model
Mehran Khaghani & Jan Skaloud
2Introduction
NavigationDetermination of Position, Velocity, and Attitude
X
Y
Z
Position
Velocity
Attitude
• As old as GPS (>30 years)• On most UAVs today (outdoor)
3Introduction
Integrated Inertial and Satellite Navigation (INS-GNSS)
INS: Inertial Navigation SystemGNSS: Global Navigation Satellite SystemGPS: Global Positioning System (American GNSS)UAV: Unmanned Aerial VehicleIMU: Inertial Measurement UnitPVA: Position, Velocity, AttitudePVT: Position, Velocity, TimeMEMS: Micro-Electro-Mechanical Systems
MEMS Tactical Navigatione e e>
Problem 1: GNSS outagePVA solution will drift over time
Problem 2: IMU failureNothing will work!
IMU
GNSS
Filter
∫ PVA+
( )PVA∆
PVT
,f ω PVA−
4Method
Our Proposition
Aerodynamic
Model
Navigation States
Wind velocity
UAV parameters
Control commands
Rigid Body Dynamics
Linear accelerationForcesMoments Rotational acceleration
Vehicle Dynamic Model (VDM)
Using VDM in navigation
5Method
From INS-based to VDM-based Navigation
VDM
GNSS
Filter
∫ PVA+
( )PVA∆
PVT
,f ω
,f ω PVA−
IMU
GNSS
Filter
∫ PVA+
( )PVA∆
PVT
,f ω PVA−
• VDM in process model• Physical constraints• No IMU data integration• Sensor setup• Self calibration• Platform dependence• Higher computational cost
(PC: 1.7 to 2.7 times)
• Platform independence• Low computational cost• Sensor (IMU) in process model
• IMU failure• Physical constraints
+−−
U
∫*nX
++ˆ
nX ˆ nX
ˆ wX
ˆ eX
ˆ pX++ˆ
pX
++
ˆwX
++ˆ
eX
IMUIMUZ
*eXIMU error
model (X )e
*pXParameters
model (X )p
*wXWind
model (X )w
+−
*nX
modelIMUZ
modelGNSSZ
Vehicledynamic
model (X )n nXδ
pXδ
wXδ
eXδ
Sensors(GNSS,Baro, ...)
SensorZ
Filter(e.g., EKF)
6Method
Some Other Architectures
IMU
VDM
Filter
∫
∫
( , )b bib∆ f ω ( )VA∆
( )VA∆
PVA∫
∫
( , )b bib∆ f ω
PVA
( , )b bib∆ f ω
Bryson 2004
IMU
VDM
Filter
∫
∫
PVA
( )PVA∆
PVA
Koifman 1999
Current research StillINS-based…
Common issues:• No wind• No self calibration• Partial VDM• No significant improvement
in experiments
PixHawk PCLOG
IMU
EKFVDM
Matlab Script
Sensors
GPSAir
Speed BaroGecko 4 Nav
via diff. interfaces
USB/WiFi
On line
Off line
TOPOPlane 2
7Method
Experimental Setup
Payload capacity 0.8 kgFlight endurance 45 minutes
Nominal airspeed 15 m/s
Aircraft & Sensors Connections
• Waypoints from real flight• 3D wind velocity from real data• Error statistics of real sensors• Flight simulation Sensor data Reference data
• 100 Monte-Carlo runs
8Results
GNSS Outage: Emulation Scenario
MEMS IMUStandalone GNSS
9Results
GNSS Outage: Experimental Scenario
VDM parameters calibration:• On a separate flight:• Start with nominal values in
simulations• IMU and cm-level GNSS data fused• Navigation states as observations• VDM parameters estimated
MEMS IMUStandalone GNSS
Emulation
GNSS Outage: Emulation vs Experimental Scenario
10Results
Experiment
53 m
2076 m
38 m
681 m
11Results
GNSS Outage: Experimental Scenario
• Waypoints from real flight• 3D wind velocity from real data• Error statistics of real sensors• Flight simulation Sensor data Reference data
• 100 Monte-Carlo runs
12Results
IMU failure: Emulation Scenario
MEMS IMUStandalone GNSS
13Results
IMU failure: Emulation Scenario100 Monte-Carlo runs
14Results
IMU failure: Experimental Scenario
VDM parameters calibration:• On a separate flight:• Start with nominal values in
simulations• IMU and cm-level GNSS data fused• Navigation states as observations• VDM parameters estimated
MEMS IMUStandalone GNSS
15Results
IMU failure: Experimental Scenario
The main suspect: Residual error in VDM parameters estimationSolution: Absolute attitude reference via photogrammetry (TBD)
Realization matters!• Estimation architecture,
VDM/IMU priors & on-linere-fining
• Platform, actuators, IMU + mount + models, time-stamping
GNSS outage, 2.2 kg fixed-wing MAV• Emulation: up to 100x better
without additional sensor • Experiment: 20-40x better
(e.g., @3min: 50 m vs 2 km)
Attitude estimation• Emulation:
<1° for roll & pitch, <2° for yaw• Experiment:
Mean ~5°, Max ~20-40° => Need to improve VDM calibration
Outlook• Attitude reference from
photogrammetry• Redundant IMUs• Direct actuation measurements• Online implementation
16Conclusion
Conclusion & Outlook
Acknowledgements: DDPS – CTI (Innosuisse)
17Publications
Selected Publications
Thank you for your attention• Concept / Monte-Carlo simulation
[Navigation, 2016] Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs
• Enhanced modeling / Experiments[Robotics and Autonomous Systems, 2018] Assessment of VDM-based Autonomous Navigation of a UAV under Operational Conditions
• Mapping[ISPRS Archives, 2016] Application of Vehicle Dynamic Modeling in UAVs for Precise Determination of Exterior Orientation
• Wind effects[ION GNSS+, 2016] Evaluation of Wind Effects on UAV Autonomous Navigation Based on Vehicle Dynamic Model
• Sensitivity Analysis[IEEE Transactions TAES, 2018 (under review)] Global Sensitivity Analysis ofVDM-based Navigation for UAVs