flight trajectory planning for fixed wing aircraft in loss ... · atr-72 ditched into the...
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S A S WATA PA U L
F R E D E R I C K H O L E *
A L E X A N D R A Z Y T E K
C A R L O S A . VA R E L A
W O R L D W I D E C O M P U T I N G L A B O R A T O R Y
D E P A R T M E N T O F C O M P U T E R S C I E N C E
* D E P A R T M E N T O F M E C H A N I C A L , A E R O S P A C E , A N D N U C L E A R E N G I N E E R I N G
R E N S S E L A E R P O L Y T E C H N I C I N S T I T U T E
S e c o n d I n t e r n a t i o n a l D D D A S C o n f e r e n c e
M a s s a c h u s e t t s I n s t i t u t e O f Te c h n o l o g yA u g u s t 7 , 2 0 1 7
Flight Trajectory Planning for Fixed Wing Aircraft in Loss Of Thrust
Emergencies
Motivation
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Loss of thrust emergencies may be induced by bird strikes, fuel exhaustion, etc.
Pilots need to make quick decisions and choose a path to land the aircraft safely on the ground.
This calls for an efficient system to help the pilots choose the best course of action by calculating available options and displaying the same to the pilots.
Image courtesy: dailymail.uk
Air France Flight 447
8/26/20173
June 1st 2009, Flight 447 from Rio de Janeiro to Paris
Thunderstorm caused airspeed sensors (pitot tubes) to ice and fail
Autopilot system not able to deal with data failures---disengaged
Pilots unable to react to erroneous data in a timely manner, eventually stalling the plane into the Atlantic Ocean
http://upload.wikimedia.org/wikipedia/commons/4/4a/Air_France_Flight_447_path.png
http://www.bea.aero/en/enquetes/flight.af.447/rapport.final.en.php
Data Redundancy
Primary cause of the AF447 accident: incorrect airspeed
Airspeed could have been recomputed from ground speed and wind speed
Take advantage of data redundancy between independently produced inputs
ground speed = airspeed + wind speed
8/26/20174
wind speed
ground speedwind
airspeed
Tuninter 1153 Flight Accident
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Flight from Bari, Italy to Djerba, Tunisia on August 6th, 2005
ATR-72 ditched into the Mediterranean sea
16 of 39 people on board died
http://www.airdisaster.com/photos/ts-lbb/5.shtml
“Final Accident Report for TS-LBB”http://www.ansv.it/cgi-bin/eng/FINAL%20REPORT%20ATR%2072.pdf
Bari, Italy
Palermo, Italy
x
Djerba, Tunisia
Actual route
Planned route
“Mayday” TV Series on Tuninter 1153https://youtu.be/aCrZwctnNWo?t=1904
Initial Cause of the Accident
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Incorrect fuel quantity indicator (FQI) installment
FQI for ATR-72 was not working properly (LED failure)
Technicians replaced the FQI with one designed for ATR-42
FQI showed 2,700 kg of fuel, but fuel actually weighed 550 kg
Pilots did not realize data error eventually leading to fuel exhaustion
“Final Accident Report for TS-LBB”http://www.ansv.it/cgi-bin/eng/FINAL%20REPORT%20ATR%2072.pdf
Complex Dependencies Between Data Streams
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va
vg
vw
w fq
h
pwcf
T
(angle of attack, flaps, landing gear, pitch, roll, yaw)
Air France 447 Model
Tuninter 1153 Model
vg : ground speedvw : wind speedva : airspeed
fq : fuel quantityw : aircraft weighth : altitudeT : temperaturepw : engine powercf : aircraft
configuration
Dynamic Data-Driven Avionics Systems
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To facilitate development of smarter (flight) data streaming systems, we investigate:
1. Programming technology to facilitate modeling spatio-temporal data streaming applications
PILOTS (ProgrammIng Language for spatiO-Temporal data Streaming applications)
2. Error detection using error signatures and error correction based on data redundancy
3. Machine learning techniques to infer
relationships from dataOffline supervised training
Online prediction and learning
US Airways Flight 1549
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Need for real-time decision support: US Airways flight 1549
Caused by birds strike damaging both engines
The pilots successfully ditched in the Hudson river
Image courtesy: cnn.com
Image courtesy: wikipedia.com
Dynamic Data Driven Avionics
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Our Goal: To create a dynamic data driven trajectory generation system to assist pilots in such situations by taking into consideration dynamic aspects such as partial power, wind, etc.
Our model uses geometric criteria for generating trajectories using different bank angles and corresponding radii of turn and glide ratiosfor different drag configurations.
Our aircraft model is parameterized on a Baseline Glide Ratio for clean aircraft configuration assuming best gliding airspeed in straight flight.
We dynamically infer the baseline glide ratio to update our model to meet the current performance of the aircraft.
Best gliding angle of attack (airspeed)
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In order to maximize the gliding distance of the aircraft, the L/D ratio must be maximized by flying at the optimal speed.
Image courtesy: faa.gov
Effect of bank angle on glide ratio
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Fig: Steady-speed engines-out glide ratio vs. airspeed observed in the A320-200(Avrenli and Dempsey, 2015)
Loss of thrust aircraft model
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Bank angle has a significant impact on the radius of turn and glide ratio, hence we use different bank angles to generate possible trajectories.
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2D Dubins Path:
Four options with a straight line segment: LSR, RSR, LSL, RSL
The shortest one is chosenRSL LSL
LSR RSR
2D Dubins Paths
Improvements over Existing Work
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Fig: (Atkins, 2009) trajectory vs simulator for t+12 sec to LGA 22
Low altitude trajectories
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For a given runway and a given bank angle, we have two scenarios:
Low Altitude scenario:
In this case, the trajectory consists of a simple Dubins Airplane Path that brings the aircraft to the runway.
High altitude trajectories
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High Altitude scenario:
When a simple Dubins Airplane Path brings the aircraft too high above the runway.
We find an intermediate point to lose excess altitude by extending the final approach along the runway heading.
In this case, the trajectory consists of:
A simple Dubins Airplane Path to bring the aircraft over the runway
An integral [0,1,2….] number of spiral turns to lose excess altitude
An Extended Runway of length X ∈ [0, 2πR g/g0 ) if integral number of turns cannot bring the aircraft down to the runway at proper altitude.
A landing configuration glide ratio and airspeed is assumed for the extended runway segment.
When the number of spiral turns is 0, the high altitude scenario simplifies to a Middle Altitude Scenario.
US Airways 1549
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We then ran simulations attempting to recreate the circumstances of the US Airways 1549 incident as closely as possible.
We used bank angles of 20°, 30° and 45°.
The simulations were evaluated using data from the Flight Data Recorder on US Airways 1549
The simulated Airbus A320 was producing around a glide ratio of 19:1 in a clean configuration during straight flight.
The simulator was used to test the viability and accuracy of the generated trajectories for the US Airways 1549 case.
US Airways 1549 Results Summary
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We were able to generate trajectories up to 36 seconds after birds strike, but not for 40 seconds or thereafter.
Runway LGA 13 at La Guardia was reachable for all times up to t+36 seconds.
Runway LGA 22 was reachable for all times up to t+32 seconds. A left initial turn is recommended at t+28 and t+32.
Runway LGA 31 was reachable up to t+12 seconds.
Runway LGA 4 was not reachable.
US Airways 1549 Flight Simulation
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• Using a Precision Flight Controls CAT III Flight Simulator running X-Plane software with an Airbus A320.
• We were able to simulate a landing at Teterboro 24 (see video) even though trajectory generation fails.
Generated vs Sim-Flown LGA 22 (t+8)
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Fig: Computed trajectory for LGA 22 (t+8) vs trajectory flown in Flight Simulator.
Trajectory Safety Metrics
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• We introduce safety metrics to evaluate trajectories.
• Evaluation of trajectories makes it possible for a pilot to make a better informed choice in a shorter time.
• The Metrics are:• Average altitude• Average distance from runway• Total distance• Number of turns• Time in air• Average Bank angle over height
• measures the occurrence of steep turns near the ground• Extended final runway segment distance
• Each metric is normalized relative to the minimum or maximum (whichever is desired) and then evaluated in our safety metric equation
Further Experimentation and Results
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• We ran simulations loosely based on the US Airways 1549 incident.
• We used bank angles of 20°, 30° and 45°.
• The simulations were done for the same initial (latitude, longitude, heading) of the incident, but at different altitudes, for an Airbus A320 with a clean configuration straight flight glide ratio of 17:1, with no wind conditions.
• We evaluated our generated trajectories on a Precision Flight Controls CAT III Flight Simulator running X-Plane software with an Airbus A320 and no wind conditions.
• We evaluated trajectories using our safety metrics.
• It took a total of 0.360 seconds for generating and evaluating trajectories with three bank angles for an accident height of 10000 feet to LGA31 .
Low altitude trajectories
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Fig: Trajectories to LGA4 from an altitude of 4000 feet
Fig: Trajectories to LGA31 from an altitude of 4000 feet
High altitude trajectories
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Fig: Trajectories to LGA4 from an altitude of 10000 feet
Fig: Trajectories to LGA31 from an altitude of 10000 feet
Trajectory Ranking Summary
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From 4000ft, LGA 31 at 45 degrees of bank angle is ranked first, due to its extended runway segment, and consistent with avoiding steep bank angles close to the ground.
From 10000ft, lower bank angles are favored with LGA 31 at 20 degrees bank angle being chosen, closely followed by LGA 4 at 20 and 30 degrees of bank angle. This is despite being ranking low on average distance to runway.
The average bank angle / height metric proves really useful in ranking safer trajectories first.
Why DDDAS?
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• We can distil the aircraft model into purely geometric constraints: Glide Ratios and Radii Of Turn for different bank angles and drag configurations.
• We use a Baseline Glide Ratio g0 (the glide ratio for clean configuration with best gliding airspeed in a straight flight) to predict glide ratios for different bank angles and drag configurations.
• Allows us to account for dynamic factors such as partial power, wind and effects of surface damage and compute a trajectory corresponding to the current performance capabilities.
• For example, USA Airways 1549 data shows some power still available in Engine 1, providing some thrust, resulting in baseline glide ratios of up to 25:1. DDDAS therefore enables more accurate trajectory generation for the emergency at hand.
Using sensor data to refine model
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Fig: Trajectories to LGA4 from an altitude of 4551 feet. New trajectories computed using data from flight simulator
Fig: Trajectories flown in Flight Simulator to evaluate their quality.
Dynamic Data-Driven Avionics Systems
Using a data-driven feedback loop, DDDAS-based avionics continuously analyze spatio-temporal data streams from airplane sensors, identify potential failure modes, and correct erroneous data. Result is new layer of logical redundancy in addition to existing physical redundancy for safer flight systems.
New mathematical concepts:
Error signatures: Mathematical function patterns with constraints on specific data stream errors/anomalies.
Mode likelihood vectors: Stochastic selection of DDDAS system operation mode based on well-behaved sets of error signatures.
New DDDAS software: PILOTS programming language
Enables declarative (high-level) definition of DDDAS data streaming application models (input-output relationships between data streams), error signatures, and error correction functions.
PILOTS software detects specific (e.g., failure-induced) data errors based on signatures and corrects data before processing according to the application model.
We have confirmed effectiveness of our approach using data from commercial flight accidents
Air France AF447 accident in June 2009: Airspeed sensor failure of the AF447 flight successfully detected and corrected after 5 seconds from beginning of the failure. Overall error mode detection accuracy reaches 96.31%.
Tuninter 1153 accident in August 2005: The underweight condition due to the installation of an incorrect fuel sensor successfully detected with 100% accuracy during the cruise phase of flight.
US Airways 1549 incident in January 2009: Bird strike after takeoff requiring real-time trajectory generation assistance. We can successfully generate trajectories up to 36 seconds after birds strike. Sequential computation time: 120 ms per trajectory.
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Multi-Aircraft Collaborative Flight Assistant System
PILOTS* System Left engine is damaged …
Avionics Application
Measured error
Cloud3D terrain data
Updated weather
Information from other planes
Stochastic & Logic-based
Flight Assistant
(to be developed)
Aircraft sensors
Correctedinputs
Correctedoutputs
Identified failure
Failure &Recommended actions
We should land at airport X
immediately!
Airplane pilots
Expert-Level Flight AssistantExternal real-time
data inputs
*: ProgrammIng Language for spatiO-Temporal data Streaming applications
Failure Detection &Data Correction
(Mathematical function patterns used to identify failure modes)
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Previous flights data
Offline Training
Learning Engine
Prediction request
Prediction response
Research Challenges (1/4)
8/26/201750
A quantitative spatial and temporal logic as a formalism:
To enable reasoning about data streams that associate values to specific points or intervals of space and time. Reasoning about differential equations (location, velocity, acceleration) and derived data streams.
To enable geometric reasoning capabilities, in particular, trigonometric formulae to calculate with aircraft speeds, headings, range, and endurance.
Ground speed and crosswind as functions of airspeed, wind, and runway heading
v Speed (horizontal)
α Direction
a Aircraft
w,x Wind, crosswind
r Runway
Research Challenges (2/4)
8/26/201751
Extensions to logic programming to support stochastic reasoning.
Language extensions to standard Horn clause-based
knowledge bases to incorporate probabilities.
Special language support for spatial and temporal data streams.
Incremental reasoning algorithms to dynamically re-compute
logical queries efficiently as new data gets injected into the
application.
Dynamic Data-Driven Flight Plan Adaptation Examples
If… then…
New pilot report: icing
en route
New route
New winds aloft New altitude
New surface winds at
destination
New airport
Imminent engine
failure
Nearest airport
Research Challenges (3/4)
8/26/201752
Data streaming analytics in real-time using cloud computing
More data are expected to be available through the Internet and in-flight through Next Generation Transportation system (ADS-B by 2020).
Reason about spatial and temporal data in real-time
Give pilots better information to make more accurate judgments during crucial emergency moments
Offline and online components
Analyzing key historical data and relatively static data (e.g., terrain, aircraft models) offline
Combining it with dynamic data (e.g., failure conditions, weather) for real-time decision making
Research Challenges (4/4)
8/26/201753
Domain-specific programming languages are needed for data scientists
Easier data analyses, information generation, decision support.
Separation of concerns
Enables compiler (static) and middleware (dynamic) optimizations
Questions?
Download open-source PILOTS at:
http://wcl.cs.rpi.edu/pilots
Partial support from:
Air Force Office of Scientific Research
DDDAS Program
Dr. Frederica Darema, Dr. Erik Blasch
(AFOSR Grant No. FA9550-15-1-0214, FA9550-11-1-0332),
National Science Foundation
EAGER/Dynamic Data Program
(NSF Grant No. ECCS 1462342). MIT Press, June 2013
Consider textbook:
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