1. Deliberative control for satellite-guided water quality monitoring
Fadi Halal and Marek B. Zaremba
Département d’informatique et d’ingénierie
Université du Québec en Outaouais
Gatineau, QC, Canada

2. Paper structure introduction
data acquisition for environmental monitoring
deliberative navigation control architecture
path planning
conclusions
06/05/2014
CIVEMSA-2014

3. INTRODUCTION Application domain: Monitoring of environmental phenomena
Use of remote sensing data complemented by in-situ measurements
Objectives:
Optimize the in-situ data acquisition process through the planning of optimal ship trajectory.
Autonomous and adaptive operation of the water monitoring system.
Hybrid intelligent control of the data acquisition platform, with an inclusion of the deliberative level.
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CIVEMSA-2014

4. DATA ACQUISITION FOR ENVIRONMENTAL MONITORING
12:56
14:25
16:25
13:32
15:24
Water sample acquisition Example of a trajectory for a ship equipped with the data acquisition equipment and a laboratory setup for measuring water quality parameters
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CIVEMSA-2014

5. DATA SOURCES Satellite sensors (MERIS & MODIS)
MODIS spectral bands
Land and cloud boundaries/property bands
Ocean colour bands
Atmosphere/cloud bands
Thermal bands
Thermal bands for cloud height & fraction
BAND l
Bandwidth
PURPOSE 8
412 nm
15 nm
Chlorophyll 9
443 nm
10 nm 10
488 nm 11
531 nm 12
551 nm
Sediments 13
667 nm
Sediments, 14
678 nm 15
748 nm
Aerosol 16
869 nm
Aerosol/Atmospheric
MODIS ocean colour bands Lake Winnipeg MERIS spectral characteristics
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6. Ancillary sensors
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7. Multi-layer map
Hybrid maps consist of topological maps and grid-based maps. Topological maps are used to interpret the global environment for path planning and decision making, while grid-based or feature maps are used to describe the local environment to validate the information in topological maps
Hybrid multi-layer map
Bathymetric map– layer 1
Chlorophyll A – layer 2
DOC - layer 3
TSS - layer 4
Wind speed – layer 5
Wave height - layer 6
Temperature – layer 7
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CIVEMSA-2014

8. DELIBERATIVE NAVIGATION CONTROL ARCHITECTURE
Feature
Generation
Behaviour selector
Target
reaching
&
Obstacles avoidance
Global map
Multi-bands map
Basic
Behaviours
Real time
Actuators
Planning module
Long time horizon
Deliberative model
Sub Tasks
Conflicting Behaviours
Boundary
following for a specific Chl-a class
Local
Conditions
Coverage of a specific Chl-a class
Environment
Reactive model
Follow the Chl-a class boundary / Walk at the peak of
a Chl-a class
Time horizon
Hybrid control architecture
In our case the deliberative control is predominant, given the complexity of the planning task and the type of the mobile sample acquisition platform
Planning
Reactive
World model dependent
World model free
Slower response
Real-time response
High level AI
Low level AI
Variable latency
Simple computation
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CIVEMSA-2014

9. Mapping and environment modeling
Deliberative control P
Mapping and environment modeling α
Planning E
Context
Reactive Control ΨE π ψ
Logic Statement
Cost function
Reactive level
Deliberative level ΨR
The deliberative level control architecture DC is formally defined
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CIVEMSA-2014

10. Environment context modeling Chlorophyll concentration
Pattern recognition Neural Network
Wave reflectance indices
Class A
Class B
Class D
Class C
NN-based Winnipeg water classification
MERIS Baltic Sea radiance study
KLM mask (Google Earth)
MERIS Winnipeg MCI map
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11. Path planning Path optimization
Start point
Island
High chl-a concentration
zone
Global path
Dynamic Obstacle
TSS Class
End point
High value
of chl-a concentration
DOC CLASS
Maximum gradient of chl-a concentration
High chl-a concentration movement
Path optimization
The path planning system generates an optimal path with the goal of maximizing the number and the value of the collected samples during the acquisition mission.
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CIVEMSA-2014

12. Cost Function Limits the latest returning times The samples values
constraints
Limits the latest returning times
The samples values
Time window constraints
Hazard constrain(weather, wind, haze)
Maneuverability constrain(dynamic and static obstacles )
06/05/2014
CIVEMSA-2014

13. Path planning depending on cost function
Maximum and maximum gradient of chlorophyll concentration Classes
Start point
Island
High concentration chlorophyll
Global path
DOC Class
Virtual Obstacles
TSS CLASS
High concentration chlorophyll movement
The end of global path
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CIVEMSA-2014

14. Adaptive path planning
Date and Time
Wind Speed
Wind Direction (°N)
17/07/12 08:08
2.3
130
17/07/12 09:08
1.8
144
17/07/12 10:08
1.1
141
17/07/12 11:08
1.4
176
17/07/12 12:08
1.3
195
17/07/12 13:08
240
17/07/12 14:08
2.5
228
17/07/12 15:08
220
17/07/12 16:08
229
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15. Reactive control
This control has direct connections between the sensors and the actuators which provide information about the surrounding environment.
Hard obstacles in the form of islands, coastal areas, ships, and other floating objects
Soft obstacles such as haze and fog can affect the local navigation and the global navigation. These obstacles reduce the travel speed and can affect the detection of the target position.
Virtual obstacle, such as the cloud zone in the satellite image, would affect the deliberative navigation.
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CIVEMSA-2014

16. Conclusions
A hybrid navigation approach was proposed for the problem of the optimal acquisition of environment data in inland waters by a ship equipped with measurement sensors.
The design of deliberative control level as the dominant problem in this application. The deliberative control architecture features a multi-model classification/regression system for the determination and forecasting of spatial distribution of selected water pollutants, in particular chlorophyll-a, and a cost optimizing path planner.
A multi-layer map was applied to interpret the global environment and build the world model. The hybrid map integrates satellite, meteorological and ancillary data, providing the perceptive system to identify the context for optimal interpretation of remote sensing data.
Reactive control navigation strategies execute the global path by dividing this path into many local paths. A behavior selector makes a decision on the appropriate behavior for a specific local path.
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CIVEMSA-2014

17. Thank you
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