Sergio Ruiz Navarro...Sergio Ruiz Navarro Director of research: Dr. M.A. Piera Universitat Autònoma...
Transcript of Sergio Ruiz Navarro...Sergio Ruiz Navarro Director of research: Dr. M.A. Piera Universitat Autònoma...
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Sergio Ruiz Navarro
Director of research: Dr. M.A. Piera
Universitat Autònoma de Barcelona
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Outline
Introduction
Conflict Detection: State of the Art Pairwise paradigm
Spatial Data Structures paradigm
Improvements: Relational Spatial Data Structures
Time‐Space Data Structures
Performance analysis
Conclusions and Future Work
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Introduction
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Expected changes in the future ATM:
Trajectory‐based operations (4D Trajectories)
Higher level of air traffic (expected x2)
Bigger air sectors (FABs)
IntroductionIntroduction
STREAM project:aims to fill the current existing gap between the strategic andthe tactical planning in the ATM
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Conflict Detection & Resolution Systems are needed to organize traffic flows in the ATM:
Conflict Detection (CD): detect 4DT’s that do not preserve safety distances
Conflict Resolution (CR): recalculate a set of new conflict‐free (efficient) trajectories
IntroductionIntroduction
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State of the Art of CD
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State of the Art
Pairwise paradigm: Usually CD algorithms are based on pairwise strategies
(quadratic complexity: O(n2))
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State of the Art
Paradigm based on Spatial Data Structures (SDS)
The main idea is to collect information about a spatial region.
We discretize the continuous space to be computer‐tractable, like in a “digital snapshot” (resolutionmatters).
Each discrete coordinate of the space can be represented by a unique memory position in a database
Databases storing information sorted according to the position in the space are called Spatial Data Structures.
4th dimension (time) can be stored inside
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A SDS is a database that represents a spatial region (e.g. an air sector) by using individual memory positions to represent each of the discrete (3D) coordinates of the sector.
Such memory positions are sorted in a way that, given a certaincoordinate, the information stored inside the SDS is easily recoverable applying simple mathematical formulas:
SDSrow (x,y,z) = x∙Y∙Z + y∙Z + z
Y,Z: order or size of dimension Y and Z.
State of the Art
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State of the Art
Physical concept of a SDS Logical concept of a SDS
Physical implementation of a SDS
…Byte 1 Byte 2 Byte 3 Byte n
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2 possible configurations of the SDS (already tested):
Clustering space for reducing Pairwise comparisons
Developed for videogames apps (Craig Reynolds)
CD through 4D Tubes
Developed under ATLANTIDA project
State of the Art
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State of the Art
Clustering to reduce the amount of pairwise comparisons:
Pairwise comparison only between neighbours
It reduces the O(n2) complexity to O(n)
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State of the Art
Aircraft can be modeled with a unique point in the space surrounded by a safety ellipsoid
Wake vortex can be modeled, in a given instant, as a sphere
Both ellipsoid and sphere can be thought as a 4D tube when aircraft moves through the continuous (real) space.
CD through 4D Tubes
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State of the Art
Construction, rotation and displacement of the cylinder
(use of Homogeneous Transformation Matrices)
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State of the Art
These discrete points are theinput to the CD algorithm
The CD algorithm efficiently verifies a possible conflict each time a 4Ddata is registered at the DB
If two or more airplanes wants to use thesame geographical 4D point, then the algorithmcheks if there is a conflict in the time window
Conflict Detection with 4D tubes
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State of the Art
SDS content example
Detected conflictscan be poured out toanother database
CR SystemOr directly passed to CR to be solved
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Improvements for the SDS‐CD
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Main shortage of original SDS
We use RAM memory for efficiency purposes
Modeling a 3D scenario requires a lot of memory Example: TMA: 270 x 270 x 120 Km3
30 aircrafts
4 bytes per booking
2700*2700*120*30*4 = 105 Gbytes of RAM105 Gbytes of RAM !!!
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Relational SDS (RSDS)SDS (non relational)
RSDS (relational)
Important reductions of the memory needs using RSDS (about 98% less memory)
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Time‐Space Data Structure
Now each row of the Data Structure represents a time‐spatial region (i.e. a 4D resource)
The access to the position is calculated by:
SDSrow (x,y,z,t) = x∙Y∙Z∙T + y∙Z∙T + z∙T + t
RSDS + TSDS = RTSDS (it is possible to join the concepts)
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Performance of RTSDS
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Performance of RTSDS
Scenario: Configuration: pairwise pruning
400x400Km2
From 30 to 5000 4DTs
½hour duration each 4DT
Horizontal discr.: 20Km
Vertical discr.: 20FL
Temporal resol.: 1 sec.
Use of memory < 1GB
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Performance of RTSDS
With less congestion the performance would be better
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Storage of the State‐Space allows a very good integration with CR block:
Easy to detect conflicts between original trajectories and alternatives trajectories, and also between 2 alternative trajectories.
Could reduce the complexity of CR algorithms.
Enable the design of CR that explores the state‐space (e.g. Coloured Petri Nets).
Performance of RTSDS
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Conclusions
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Conclusions
Pairwise‐based CD algorithms, currently widely used on major real CD systems, may be improved by using of SDS‐based CD algorithms, since: they run at linear time (linear temporal complexity, O(n))
they store the state‐space of the problem, which may help CR systems (global strategy).
Relational SDS has been designed to reduce the exponential growth of memory up to 95‐99% of the original RAM memory requirements.
Time‐Space Data Structure introduce a new concept of SDSs by introducing a temporal dimension in the data structure.
Simulations with congested scenarios have illustrated that a combination of RSDS and TSDS, called RTSDS, has an excellent performance.
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Future Work
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Future Research
Add en‐route real traffic and consider strategic and tactical CD&R (STREAM project)
Integrate the SDS with Coloured Petri Nets to take advantage of the stored state‐space (optimality).
Add weather perturbations and more precise vortex modeling (WAKE4D project and FLYSAFE project)
Determine how to deal with errors due to Earth planar projection in big sectors (i.e. FABs)
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It should be taken into account the curvature of Earth
UTM projection allows making a planar projection with very few distortion
Two conversions: WGS‐84 UTM and after UTM WGS‐84
CD algorithm
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Thank you for your attention!
QUESTIONS?