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

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

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. 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

Ancillary sensors 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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. 06/05/2014 CIVEMSA-2014

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

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 06/05/2014 CIVEMSA-2014

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 06/05/2014 CIVEMSA-2014

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. 06/05/2014 CIVEMSA-2014

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. 06/05/2014 CIVEMSA-2014

Thank you 06/05/2014 CIVEMSA-2014