Transcript AI Planning, Waiting for the Results?

Nationaal Lucht- en Ruimtevaartlaboratorium
National Aerospace Laboratory NLR
AI Planning, Waiting for the Results?
H.H. Hesselink ([email protected])
R.R. Seljée ([email protected])
2nd Gap-Bridging Seminar
PLANSIG 2002
TU Delft
21 November 2002
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NLR
• NLR is a non-profit foundation since 1937/1919
• NLR provides technical and scientific contributions
to activities in aerospace related areas
• NLR will independently serve public and private organisations
Government
– Ministries
Transport
– Defence
– Environment
NLR
Operator
– s
Royal NL Air Force
– Royal NL Navy
– KLM
– Air Traffic Control
– Amsterdam Airport
Schiphol
Industrie
– sAircraft
– Space
– Other, incl.
Electronics,
Remote Sensing
Education
– Universities
– Technical Colleges
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NLR
- Civil aerospace: 65%
- Military aerospace: 35%
- Aeronautics: 85%
- Space: 15%
- Operations: 60%
(non-aerospace: < 2%)
- Development: 40%
Turn over: 70 MEuro.
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NLR
• Large Wind Tunnels (2 low speed, 2 transonic, 1 supersonic,
50% shared in DNW)
• Simulators (flight, air traffic control, tower)
• Aircraft (Fairchild Metro II, Cessna Citation II)
• Computing environments (supercomputer,
Network, middleware)
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NLR
• NLR undertakes studies and implementations for Schiphol
• Safety cases
• Policy studies
• Simulator trials
• Implementations
• Co-operation with Dutch Industry (HITT)
• TCM (Taxiway Collision Monitoring)
• Triple-I: Intelligence Instead of Infrastructure
• Co-operation with Eurocontrol
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Projects with AI Planning and Scheduling
• MADS:Departure Planning Decision Support at Airports
• Runway Use Advisory and Inspection Systems: BGAS & BGCS
• Tools for Route Planning and Manoeuvre Planning
• Crew Assistant is a decision support system for a pilot
• Mission Support System Campal (MSS/C) is a powerful tool
for military mission planning
Planning in Air Traffic Management (ATM): the Big Picture
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ATM Planning: the Problem
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Artificial Intelligence and Planning
• The traffic increase gives the operational need
• Problem not recognised by controllers (the end users)
• Academia wants to experiment new techniques
• Academia wants complex methods for operator
modelling
• Side effects:
• Establishing better collaboration
• Acceptance of new technology in an organisation
• Studying other solutions,
e.g. use trains in case of Air Traffic Growth
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The Problem: Division of ATM
• Arrival management
• Departure management
• Surface movement
• Stand allocation
• Flight management
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The Problem: Many Actors
Many actors involved:
• Airlines/AOC
• Pilots
• Air Traffic Controllers
• En-route ATC
• Approach ATC
• ATC Departure/Tower
• Other Airports ATC
• Apron Controllers
• Ground Movement Controllers
• CFMU
• Meteo Service Providers
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Each airport is different but the same
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Airport traffic management control loop
aircraft
surveillance
planning
Guidance
control
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Collaborate Decision Making (CDM)
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How from FCFS to planning
RESEARCH
OPERATORS
• planning
• more advanced technology
• talk to operators
• increase efficiency
• first heard first served
• conservative (safety)
• no time / no interest
• one aircraft vs cowboys
The GAP
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How to bridge this gap?
• Start with foundations of the bridge
• Finances
• Interest from operators
• prototypes
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Foundation: finances
• Contributions from research (NLR itself)
• Contributions from subsidiary projects (EC)
• Contributions from Eurocontrol
• Where is industry to subsidise this work?
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Foundation: interest
• Make operators aware of their problem
• Make operators aware of solutions
• Convince operators that they will not be replaced
(by automation)
• Convince operators of new challenges
• Convince airport managers of the need
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Foundation: prototypes
• Build realistic prototypes
• Give operators the possibility to interact
• Large simulators, with controller-n-the-loop
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The bridge
• Integration projects
• Demonstrations
• Emotional response to new things must be broken
=> give the operator a “positive feeling” about
our work
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Where is industry?
• Industry is waiting for results (to sell)
• Operators are waiting (if at all) ...
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NLR work to demonstrate to planners
• Introduce a planning and scheduling function
at airports for scheduling traffic and assist
traffic controllers
• Evaluate the use and benefit of the tool
• We build a planner with constraint reasoning
(ILOG Solver and Scheduler)
• Symbolic representation of the problem
based on the “flight”abject
• Constraints: separation, meteo, runway length,
traffic distribution, ...
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Simplified model
A
2 min.
runway
holding A
SID
5 min.
structure
2 min.
runway
• runway assignment
• intersection take-off
• take-off time (sequences)
• SID allocation
exit
5 min.
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HMI
_
Active Departures
CALLSIGN STND CTOT EOBT TYPE/W DEST RWY SID
SSR ALERT
TAXIROUTE
-5
REMARK
0
+5
+10
STAT CLR
X
+15
EFS
ACTIVE
TVS338 N26
1947 B734/M LEPA 24 BANAS2A 0234
BCS916 N5 1945 1940 B722/M EDDF 24 KADNO
0237
TAR8861 N22 1940 1928 B732/M DTTA 31 KADNO
0344
TAR229 N27 1930 1924 A30B/M DTTA 31
FFR8105 N1 1920 1915 B733/M LDDU 24
CSA270 N9 1916 1909 B735/M HECA 24
CSA978 N20 1910 1904 AT72/M LZKZ 24
Delete
Edit
RAK
0332
BANAS2A
0232
RATIS
0331
RATIS
0336
NO PUSH
J-H-B
TWR/APP
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The result of our work
• Operators start to understand the idea of planning
• Operators are willing to express their knowledge
• Industry has been invited to participate and show
their operational products (radars and HMIs)
• Industry was invited for a “picnic day”
=> We brought industry in contact with end users
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And further ...
• More experiments in our tower simulator
• More demonstrations in real control towers
• Bring results to airport decision makers (presentations)
• Bring results to industry (software and design)
… and sell it!
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Questions ?