A CONTROL SYSTEM MODEL FOR AUTONOMOUS SAILBOAT NAVIGATION

William H. Warden, PE Georgia Institute of Technology

School of Industrial and Systems Engineering Atlanta, Georgia 30332-0205

Abstract

Single handed sailors and fighter pilots are both subjected to situations were information overload occurs. This paper reports on research in the areas of Decision Support for real time information rich environments and Integrated Automated Control. This research has applications in manufacturing systems and space systems.

Under current maritime law, a person is required to stand watch at all times; however, single handed sailors can not stand watch continuously during long offshore passages. It is hoped that systems like the one discussed in this paper, will reduce the demands on the single handed sailor and increase safety at sea.

Introduction

People have been sailing for thousands of years. Three hundred years ago, sailors had little more than a sextant and maps generated from previous voyages. Today, there are numerous electronic aids to navigation. This paper discusses a control system model for the analysis of fully autonomous sailboat navigation systems. The architecture allows an expert system shell to develop good routes while avoiding obstacles and exploiting environmental conditions (winds, tides, currents).

Size and weight are constraining factors whether in space or aboard a yacht. Space borne applications cost tens of thousands of dollars per pound per mission. AGVs do not have a weight constraint, but a high premium is placed 011 low power consumption due to the limited battery capacity (480 Amp- Hours typically). AGVs are capable of utiattendetl operation, but the cost of conducting experiments on a factory floor is prohibitive due to potentially lost production.

The author would l i k e t o thank T . Govindaraj , C . Mi t che l l , and A . Kirlik of t h e Georgia Tech Center for Human Machine Systems Research for t h e i r a s s i s t a n c e and encouragement d u r i n g the prepa ra t ion of t h i s pape r .

The author would a l s o l i k e to t h a n k William K . Meade of Michigan S t a t e Universi ty for h i s a s s i s t a n c e during t h e i n i t i a l s t a g e s of t h i s r e s e a r c h .

Automated Guided Vehicles (AGVs) need guidance updates every 6 to 40 milliseconds to maintain +/- .5 cm accuracy at 1 meter per second, but completely eliminate the need for human intervention. Fighter Aircraft automation demands millisecond response times for +/- 150 meter accuracy at high speeds. Sailboats generally have the luxury of loose positional accuracy and relatively long response times; therefore, sailboats make an excellent platform for demonstrating the feasibility of autonomous vehicles.

Traditionally. simple wind vane steering mechanisms have been used due to their low power consumption and high reliability. Work is underway on a navigational aid composed of an integrated control system and an imbedded expert system (shown in Figure 1) that will substantially reduce the navigational workload. The completed system will be able to operate autonomously in the offshore sailing environment for extended periods (days).

Figure 1. High Level Controls Architecture (Expert System Boundary shown by dotted line)

Sailing Simulator

A sailing simulator, supplying the environmental inputs and displaying the results of control decisions, was designed and coded in Pascal. The simulator supplies the navigator with information on boat speed and position relative to a background displaying wind speed & direction and water depth. The boat moves over the electronic chart to iudicate position relative to the initial and goal positions. line course gives the navigator a sense of competition. The wind conditions come from information available during the 1990 Cleanvater to Key West race. The navigator’s task is to reach the goal position (Key West) as quickly as possible.

A pace boat following the rhumb

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A sample display from the sailing simulator is shown in figure 2. The left side of the screen displays the electronic cliai-t. The lower right corner displays the current system state :md a sailing polar diagram with moving boat icon is displayed i i i the upper riglit comer of the screen. A sailing polar diagram sliows a boat's speed potential as a function of wind angle and wind speed. The navigator may use the information displayed on the polar diagram or his/ her understanding of wind patterns (based on weather forecasts), currents (based on local knowledge) and intuition to select his/ her course. The navigator's actions are logged to a file for analysis. Given the weather history from the 1990 Clearwater to Key West race, sailing the rhumb line (shortest path from Clearwater to Key West) is not tlie fastest course.

Polat- Diasran

Elansed Tine o:oo:oo:oo B o a t ! Speed D i r e c t i o n 5.000 195

Wind: Speed Dit -ectmn 1o.uw 0

Posit ion: La: 27.975 Lo: 82.850

Lgure 2 ~ n i t i a l Sailing Simulator Screen

At the time of this writing, the project had not begun to iiieasiire human navigating performance scientifically. About half of the people that have used the sailing simulator eventually find a strategy using tides and evening sea breezes that beats the boat following the rhumb line.

'Tile simulator will be enhanced to include displays for tide, sea state and offshore currents as data for these conditions is collected. When a high fidelity simulator has been validated, tlie expert system shell will replace the human navigator so that t h r expert system can be evaluated. If the expert system prrfonns satisfactorily it will be installed on a cruising sailboat (coiinected to real sensom and real actuators) where its real world usefulness will be assessed.

Data Collection

1:or the initial sitnulation models there were two primary sources of data on sailboat performance characteristics: measured licbeliiig niunient froin an IMS (Intemational Measurement Systciii) rating certificate and sailing polar diagrams from the Iliiitcd Slates Yacht Racing Union (USYRU). Sailing polar diaplaiiis are graphical representations of a sailboat's speed p ~ ~ ~ c n t i d as a function of wind speed and relative angle as suiiiniarixd in figure 3.

Since this data does not represent the interaction effects that :Ire cwxwitercd i n our niodel, additional data was collected :iroiintl Sanibel Island, Florida between December 14-21, 1990. A incasiired polar diagram for a wind speed of 5 knots is shown i l l figure 4. witliout manipulating crew weight or ballast (autonomous stcering and sail trim).

The nieasured data reflect actual boat performance

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I Figure 3 Data from published Polar Diagram.

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Figure 4 B o a t Speed versus T r u e Wind Angle f o r wind speed of 5 k n o t s .

Sailboat Subsystem Models

Sub models were developed to represent the ballast control subsystem, the sail trim subsystem, and the helm control subsystem. There is not sufficient room in this paper to describe them adequately. They are discussed in detail in Clark [90] and Warden [90].

Research Issues

1) How to avoid Cognitive Overload during single handed sailing?

Psychologist have studied the issues surrounding cognitive overload, (too many tasks, too little processing power) and determined that people use various strategies to cope with information overload. People tend to ignore some sources of

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information and prioritize the remaining information. This allows people to fail gracefully. In time petsistent situations, people tend to look for ways of off loading some tasks so that they may process the remaining tasks tnore effectively.

Many real time applications are interrupt driven and typically have a means of prioritizing the interrupts; however, in the past machines did not posses the capability off load arbitrary tasks. Distributed processing architectures have changed the situation and now machines routinely off load tasks. A finite processing network still has a finite amount of processing capability. If the requirements exceed this capability, then the entire network degrades, but possibly gracefully.

The key to successful mastery of the information burden comes from selecting the appropriate world view as discussed in Henriksen [81] and Warden [91]. black board architectures with a Process or Object orientation tend to focus attention on the individual processor selecting only the information required to execute the processor’s cuiTent task. Event oriented programs tend to encapsiilate the necessary input information with the event packet, this allows an event to select a processor.

Shared common memory or

An example demonstrates the point more clearly. On a moving AGV, there are at least three concurrent tasks: communications (to avoid collisions), following the guide path, and piloting (selecting the next movement subroutine).

In the object orientation, the on board Communications manager continuously looks for incoming data, acknowledges the messages and places the messages in a queue for the navigation manager. The navigation manager continuously checks the input from the path following circuitry and controls the actuator circuitry, hut also evaluates the incoming messaees for routing information an to avoid collisions.

In the event orientation, the on board cotnmunications manager waits for a signal from the UART (1Jniversal Asynchronous Receiver/ Transmitter) indicating that incoming data is available (an event). Then, the communications manager builds a message (a character at a time). When the communications manager has a complete message (an event) the communications manager routes the message to the Navigation manager and acknowledges the message. The guidance circuitry provides feedback directly to the actuator circuitry with the navigation manager setting the goal (follow center of stripe or follow frequency number 2). The navigation manager queues the incoming messages and selects the appropriate guidance goals. When the AGV reaches a way point (sub goal on route to destination) the navigation manager selects the next sub goal.

In the event orientation, it appears that the processes are inactive most of the time. In the object orientation, the processes were continuously busy. Why? The processes were looking for work rather than working!

2) What tasks are best handled by automation?

People do not perform well on tasks involving continuous monitoring/ correction (steering); they fatigue and their attention wanders. People also do not perform well on tasks that require constant vigilance and quick reactions t o infrequent events (wind shifts). People are great planners, but they dislike the administrative overhead. For instance, selecting way points and taking star sights are pleasurable, but reducing a star sight to get a line of position is error prone.

Automation does not get bored or tired. Automated equipment can trim sails and/ or steer a sailboat. Navigation computers are available to quickly reduce star sights. Electronic instrumentation such as LORAN (Long RAnge Navigation), SatNav (Satellite Navigation), or GPS (Global Positioning System) can automatically collect positional information and plot the ship’s position on electronic charts. Electronic instruments can warn of shallow water or when coupled with the electronic charts, warn of impending grounding. Computer based optimization methods are even used to plan long passages.

What then, are people good for? People are flexible. A person can detect impending collision with another vessel and deterniine acceptable alternatives quickly. A person can be contractually responsible for a vessel and its cargo; a machine c m not. A person can enjoy sailing; a machine can not. A person can adapt to local personalities, when clearing customs; a machine can not. People are necessary to deal with other people and unexpected situations.

3) What is the state of industry vs state of the art?

Autonomous AGVs exists in many factories. In industries like newspaper printing and pharmaceutical almost all nen plant use them. Fly by wire exists and has been demonstrated i n Europe and the United States. Yamazaki operates a remotely controlled machine tool facility in Japan.

If the technology has been successfully applied in some areas for years, why are there no autonomous shipping fleets or automobile fleets? In part, it may he due to insurance r i s k . Who is to blame when a ship goes aground because one machine tells another rnachine to tell another machine, etc. something and somehow the message doesn’t get through in time or was the wrong message? People are either following orders or ineffectively assessing a situation when problems occur; the process as a clear chain of command. Once things go into a computer the distinction between system designer, programmer, hardware fault, software fault, sensor fault, etc. starts to get fuzzy. State of the industry and state of the art are advanced sufficiently for autonomous vehicles, but the state of society is not.

Development Goals

This research will produce a tool for comparing/ modifying actual boat handling performance relative to goals established by an integrated model of the vessel’s performance characteristics and the crew’s capabilities. The tool will require a minimal amount of training while providing a significant amount of utility. Experimentation is being conducted to validate intemal models and increase the effectiveness of the Operator Interface. The key features are listed below:

* Facilitate Navigation Decisions (,Graphic Display) Log real time information to storage device for later recall. Path tracing and playback (calculate environmental effects). Integrate Automated Functions (Integrated Support) Model boat dynamics (Discrete Control Model) Model human operator (Operator Functional Model) Quick Response to Dynamic Environment (Real Time) Control Automated Equipment (Steerage, Sail trim, Ballast) IJse NMEA 0183 interface for integrated information transfer.

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State of the Art

A review of leading edge sailing instrumentation systems tccliilology identified three general directions: Focused lilformation (Ockam), Decision Support (B&G), and Intelligent Iiistruinenlatiori (AutoHelni). For various reasons, an Expert Systenl monitoring intelligent instruments and selecting the appropriate control action has not been tried.

Research Direction

The main purpose of research is to pose and validate theories. A simulation model has been developed. The knowledge obtained from experimenting with the model will be in~pleniented on a cruising sailboat. Work on the following sub- tashs has begun:

Interactive Integrated Control System model of sailboat dynamics for the evaluation of human navigators and the Expert System's rule base. Expert System - Integrated Control System capable of quick response to dynamic Environment (Real Time) Automated Control System application (Steerage, Sail trim, Ballast) using Local Area Network (NMEA 0183 and Autohelm's SeaTalk).

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A high level data flow diagram is shown below. System inputs consist o f position (from LORAN), depth (from echo sounder), wind speed and direction (from Windex), speed (from hi111 transducer and LORAN), heel angle (from incline-o-meter), sail trim (from tell tails), and helm angle (from AutoHelm). 'l'lie System outputs consist of ballast pumps (port/ starboard), jib sheets (port/ starboard), main sheet, main traveler, and helm error (to AutoHelni).

The Ballast, Jib, Main sail and Helm Sub-systems can be operated under independent automatic control. The integrated iiiodel should be able to coordinate sub-system actions that produce more complicated actions, like a roll tack.

Figure 5. High Level Controls Architecture

A sailboat has the option of reverting to manual operation to increase crew activity or to conserve power. A passage making sailboat can be thought of as essentially unattended operation, with a Human Operator available for monitoring. Such a vessel would utilize automated controls for steerage, sail trim, and ballast adjustment. In order to produce a quick response to the dynamic environment, current shipboard automated controls tend to focus on a single function, ie keep heel at 20 degrees, hold course 036M, or keep the apparent wind abeam to port.

The single handed skipper needs an effective method of communicating current goals to the vessel and monitoring performance. Excellent packages exist for monitoring performance so that a crew can maintain optimal speed toward goal, but these packages use static "goals". They produce an error signal (descriptive) for interpretation by the ctew, ie (target boat speed - actual boat speed). A prescriptive package would incorporate a model of vessel performance and a crew model to improve overall performance relative to the dynamic environment. For example, approaching a land mass with land effect winds may indicate better performance on an alternate heading.

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'lgure 6 Sample Screen from Graphica Display

References

Clark, Russell [1990], Neural Network model of Sail Trimming, College of Computing Paper for 6361, Dec 90.

Henriksen, James 0. [1981], GPSS - Finding the Appropriate World-View, IEEE 198 1 Winter Simulation Conference Proceedings, pg 505-5 15.

Meade, William K. with W. Warden [1985], Critical Issues in AGV Problem Solving (WA26.3), Joint Meeting of ORSA/ TIMS, Boston, May 1985.

Warden, William H. [1990], Neural Network Model of Helm Control, College of Computing Paper for 6361, Dec 90.

Warden, William H. with Jerry Banks [1991], Three World Views of Simulation, Article submitted for publication in "Simulation".