Coordinating a Fleet of Autonomous Underwater Gliders Using

Coordinating a Fleet of Autonomous Underwater Gliders Using a Decision-Theoretic Approach in a Multi-agent System Chhaya Mudgal 1, Scott Glenn 1, Oscar Schofield 1, Clayton Jones 2, Douglas Webb 2, Gary Kirkpatrick 3 Introduction Data Sets “I walk into our control room, with its panoply of views of the sea. There are the updated global pictures from the remote sensors on satellites, there the evolving maps of subsurface variables, there the charts that show the position and status of all our Slocum scientific platforms, and I am satisfied that we are looking at the ocean more intensely and more deeply than anyone anywhere else. ” - Henry Stommel, The SLOCUM Mission The Slocum Glider tested at LEO was equipped with a Sea. Bird CTD. Numerous navigation and control tests were conducted during 2000, including station keeping in strong currents, steering a course with and without current corrections, conducting a cross-shelf survey transect, and monitoring the location of a front overnight until a boat could return the next day. Before the Glider was recovered, three different shipboard CTDs were profiled as the Glider undulated nearby. What is a Slocum Glider? The Slocum Glider is an Autonomous Underwater Vehicle (AUV) that undulates in the water column by changing its buoyancy. The Coastal Electric version changes its buoyancy using an electric hydraulic pump to inflate and deflate an internal diaphragm. Wings translate the vertical motion into a forward velocity, resulting in a sawtooth sampling pattern along a subsurface transect. The vehicle is steered to a specific location, either by a rudder (for fast response), or by lateral changes in its center of gravity (similar to a hang-glider). The Slocum Glider is equipped with a payload bay that nominally contains a CTD. At regular intervals, the Glider surfaces to obtain another GPS fix and communicate with a shorebase using the antennas located in the tail fin. Sensor, vehicle status, and position data can be transmitted to shore, and new missions can be downloaded. The Slocum Glider is named after Joshua Slocum, the first person to sail solo around the world. University, New Brunswick, NJ, 2 Webb Research Corp, Falmouth, MA, 3 Mote Marine Lab, Sarasota, FL Figure 3 : Comparison of Glider and three different shipboard CTD profiles Figure 3 shows the temperature and salinity profile comparison, and Figure 4 illustrates the temperature section from the cross-shelf transect. The LEO Glider missions were conducted in the vicinity of a cross-shelf array of bottom-mounted ADCPs and ADPs deployed for model validation. Whenever the Glider was within two kilometers of one of the validation array moorings, the Glider pressure record was used to average the observed velocities. Figure 5 compares the along-shelf and cross-shelf components of the depth average velocity computed from the current meters and the Gliders. Approach Agent Oriented Software Implementation of the Glider Mission Control Center is based on Agent Oriented programming. What are Software Agents? A Software Agent is a concept from Artificial Intelligence. Software agents are computational entities which are capable of working autonomously in environments inhabited by other agents. Software agents can: • perceive their environment • act upon the environment • communicate with other agents Figure 6: Abstract Agent Model Slocum specifications: Weight: Hull Diameter: Vehicle Length: Depth Range: Energy: Endurance: Range: Navigation: 56 Kg 21. 3 cm 1. 5 m 4 - 200 m Alkaline Batteries 30 days 1500 km GPS and internal dead reckoning, altimeter Horizontal Speed: 35 cm/sec (30 km/day) Figure 1: Slocum Glider An academic/industry partnership between Rutgers University and Webb Research Corporation resulted in a series of test flights of the Slocum Coastal Electric Glider at Rutgers' Longterm Ecosystem Observatory (LEO) during the 1999, 2000 and 2001 summer Coastal Predictive Skill Experiments. The first open ocean tests of the Coastal Electric Glider were conducted with a safety tether in 1999 and unteathered in 2000. A new version with faster steering response was tested in 2001 in the steep ridge and swale topography to the south of LEO. Figure 4: Temperature section July 19, 2000 Figure 5: ADCP versus Glider Drift Comparison Communication Webb Research Corporation and the University of Washington/APL are collaborating on the development of Iridium satellite phone modems to provide Autonomous Underwater Gliders the global communication capabilities required for long-term deployments. Glider communications currently rely on range-limited cell phone or RF modems. At LEO, the RF modem repeater located on the 64 m Tuckerton meteorological tower provides a 30 km x 30 km operating area centered on the highresolution CODAR surface current field. Tests are normally conducted in the middle of this field, so if a Glider is lost at night, it is unlikely to drift out of range before daybreak. For backup, a second RF repeater was installed on the tail of the PHILLS survey aircraft also operating at LEO. During the 2001 steering tests over the steep topography, the Glider stopped reporting in at its scheduled intervals. A boat search of the vicinity guided by the CODAR current fields revealed nothing, leading us to believe that the Glider had grounded. In this situation, the Glider drops a ballast weight after 12 hours and returns to the surface. The expected release time was during a scheduled survey mission of the PHILLS aircraft. A boat was sent to the last known location. At the expected release time the Glider surfaced and transmitted RF Repeater its location through the repeater on the aircraft directly to the vessel at sea. This rapid rescue is one demonstration of the utility of long-range communication systems. LEO Glider Deployments The bathymetry map at left illustrates the Glider GPS track for the 2000 (white) and 2001 (red) deployments. Two-way communications between the Glider and the Tuckerton field station were achieved using a radio modem repeater located on the 64 m high meteorological tower. Figure 2: Glider maps from 2000 and 2001 Motivation With global satellite communication capabilities, Autonomous Underwater Gliders will have the ability to patrol the subsurface ocean for long durations with reduced fear of being lost at sea. A small fleet of Gliders flying beneath the satellite- and CODAR-derived surface fields will provide 3 -d information for assimilation in forecast models. Full water column undulations will provide data on the temperature and salinity structure below the satellite SST’s. Undulations above or below thermocline will provide depth average current estimates to improve the assimilation of CODAR surface current maps Glider Mission Control Center This research focuses on the design and implementation of a centralized control system to coordinate a fleet of Gliders based on data available from the fleet or other scientific systems. The Glider Mission Control Center is: • Autonomous • Adaptable • Adaptive • Responsive • Flexible Sample Products Figure 9 is a sample plot produced by the Glider Data Agent using NOAA’s SGT showing a temperature profile collected by a Glider. The Glider Data Agent finds the location of the maximum temperature gradient (green star) and passes this thermocline location on to the Mission Control Agent. The Mission Control Agent uses this information to determine the vertical extent of the undulations. Similar plots are implemented for conductivity, salinity and density. Mission Control Center is a Multi-Agent Decision Making System consisting of following Agents : Glider Data Agents: A new Glider Data Agent thread is spawned for every Glider deployed. Each Glider Data Agent monitors their specific Glider for the availability of new data. When new data is acquired, it is processed, analyzed, and graphically displayed. Results are communicated to the Mission Control Agent. Figure 9: Glider Temperature Profile Figure 10 is an example produced by the Mission Control Agent using Open Map to track the status of a hypothetical fleet of 8 Gliders conducting a coordinated cross-shelf survey of the New Jersey Shelf. The intended flight lines are shown, with the current location of each Glider marked by the green arrow. Mission Control Agent: The Mission Control Agent is the central decision making entity of the Control Center. After receiving data products and status information, initial tasks are to update a status board locally and on the World Wide Web, and to send email warnings of potential problems to controllers. It then evaluates the data products to decide whether to continue the present mission or to design and start a new mission. Who uses Agent technology? Software Agents are being used to construct selfaware, self-controlled and self-operated robots, exploring rovers and intelligent machines. The most celebrated use of software agents is by NASA’s Deep Space 1 spacecraft. Deep Space 1’s “Remote Agent” is the first artificial intelligence software to command a spacecraft. Remote Agent detected, diagnosed and fixed problems, making decisions based on high level goals to keep the mission on track. Remote Agent’s most recent achievement was the successful navigation of the spacecraft through the tail of comet Borrelly. It is envisioned that spacecraft equipped with agent oriented software will enable NASA to pursue missions that once were considered too elaborate, too costly, or too dependent on large teams of ground controllers. Figure 10: Map showing Glider Status Ongoing Work 1) Continue to expand the functionality of the mission control center a) Vertical control b) Horizontal control c) Coordinated fleet operations 2) Improve satellite communications 3) Addition of the Hydroscat-2 Fluorometer/Backscatter sensor for phytoplankton and particle concentration. Figure: Deep Space 1 flyby of comet Borrelly Inter-Agent Communication Freshwater Plume As Software Agent technology becomes more prevalent, the demand for efficient and versatile agents will grow. Designing a single agent to perform all the tasks increases the complexity of development. An alternative is to design a society of agents with each agent handling its own task efficiently. For the society of agents to be able to function coherently, cooperation and collaboration is an important requirement. Inter-agent cooperation and collaboration is highly dependent on a common communication medium and a common communication language. There are many agent frameworks, languages and protocols available. The software package for our agent implementation “Java Agent Toolkit” (implemented at Stanford University) uses “KQML” Knowledge Query Manipulation Language for inter-agent communication. Seawater intrusion Density kg m-3 4) With Mote Marine Lab, miniaturization and installation of a spectrophotometer to measure absorption signatures that delineate phytoplankton load and composition. To be tested for Red Tide detection and tracking. Decision Making Decision making methodology must be incorporated in the semi-autonomous software tool that coordinates the fleet of underwater Gliders. The Decision Theoretic approach will be used to implement decision making in our system. Decision Theory is a mathematical framework for determining the best action given a decision problem. It is a process of sequential decision making that provides structure to a complex problem, identifies important objectives and generates alternative courses of action. In our context, decision making is data, knowledge and communication driven. Mixing event 1 Rutgers Figure 8: Flowchart diagram for the Mission Control Center Implementation Day 195 The software implementation for graphics and data analysis uses Sun’s Java Development Toolkit 1. 3 (JDK 1. 3) Software packages used for the graphical representation and Agent Implementation are : • NOAA’s-Scientific Graphics Toolkit (SGT). SGT is a Java library package that provides platform independent and highly interactive graphics for scientific data. Figure 7: Types of Decision Making Whenever there is incomplete knowledge or no knowledge, it creates a situation of uncertainty. For example, setting the depth of the Glider undulations based on thermocline must take into consideration the possibility that there is no thermocline or thermocline is not distinct. The Decision Theoretic tool adopted here is an Influence Diagrams are an extension of Bayesian Belief Networks containing decision and utility variables. Actions are selected by evaluating the decision network for possible choices. • BBN Technologies’-Open. Map is a package based on Java. Beans. Its library package allows one to view and manipulate geospatial data. • Stanford University’s-Java Agent Template (JAT Lite). JAT Lite is a package of programs written in Java that allows one to create software agents. These software agents can send and receive messages using the standard agent communication language KQML. JAT Lite has been used to implement the Glider Control Agent and the Mission Control Agent. Acknowledgements The Rutgers/Webb partnership was initiated by NOPP in 1999 and is currently maintained by ONR and the Great State of New Jersey. The partnership was expanded to include Mote Marine Lab in 2002 by NSF. Additional thanks to Dr. David Fratantoni (WHOI) and his group for their contributions to the summer 2000 Glider deployment at Tuckerton. This poster can be viewed online http: //marine. rutgers. edu/Cool. Results/agu 2002.