Abstract—This paper describes the development of a short

introductory underwater robotics course, aimed at college

freshman and high school and middle school students. During

these courses, students work in teams to build and program

underwater robots using a combination of LEGO and other

simple materials. As an introduction to ideas of artificial

intelligence and robot programming, students undertook a

practical exploration of the concepts developed by cybernetician

Valentino Braitenberg in his famous book “Vehicles: Experiments

in Synthetic Psychology”. Over five laboratory sessions, students

gradually evolved their own designs for waterborne “robotic

amoebas” through a series of progressively more complex design

challenges. These courses build on our previously reported work

in which students have built underwater Remotely Operated

Vehicles using similar materials and educational strategies. This

work is now being adapted for dissemination to large numbers of

middle and high schools across New Jersey through a grant from

the National Science Foundation.

I. INTRODUCTION

Valentino Braitenberg’s famous text “Vehicles-experiments in

synthetic psychology”, [1], uses a series of elegant thought

experiments, involving simple imaginary vehicles equipped

with motors and sensors, to explain how seemingly complex

animal behaviours such as attraction, repulsion, fear and

aggression, can result from combinations of simple

mechanisms. Braitenberg’s explanations are profound in their

implications for roboticists and neuro-scientists, yet so simple

and intuitive that they are immediately accessible to readers of

all levels, without any prior knowledge or expertise.

This paper describes the development of a short introductory

course, aimed at college freshmen, high school and middle

school students, enabling a practical exploration of

Braitenbergian ideas through constructing, programming and

testing a series of progressively more complex waterborne

robot vehicles, also known as Autonomous Underwater

Vehicles (AUVs), e.g. figure 1.

Manuscript received August 10th, 2007. We thank Costas Chassapis, Dir.

Dept. Mech. Eng., Stevens Institute of Technology, for funding the equipment

and materials to test and develop this project. R. Stolkin is a research

Assistant Professor at the Center for Maritime Systems, Stevens Institute of

Technology, Phone: 201-216-8217; e-mail: RStolkin@stevens.edu. Richard

Sheryll is an instrumentation designer and also a PhD candidate in Ocean

Engineering at the Center for Maritime Systems, Stevens Institute of

Technology, email: cyclopsRD@aol.com. Liesl Hotaling is Assistant Director

of the Center for Innovation in Engineering and Science Education at Stevens

Institute of Technology, email lhotalin@stevens.edu.

II. MATERIALS

Students were provided with a selection of LEGO including

several motors, battery boxes and leads, gearing, structural and

mechanical components. Also provided, were a selection of

plastic propellers (obtainable from hobby stores) mounted on

LEGO axles. Additional materials included Styrofoam,

modeling clay, a selection of weights (nuts and bolts work

well), rubber bands, string and duct tape. A 30 inch deep

inflatable pool was used to test the designs.

For programmable robot control, students used the LEGO

NXT controller (figure 2), sealed inside a plastic box, LEGO

robotics sensors, including touch sensors and light sensors

(which can be waterproofed using simple materials such as

clingflim), and the simple icon based NXT-G programming

system.

Braitenbergian experiments with simple aquatic robots Rustam Stolkin, Richard Sheryll, Liesl Hotaling

Stevens Institute of Technology Hoboken, NJ 07030, USA

Figure 1. A programmable AUV with light sensors, built using a

combination of LEGO and other simple materials.

Figure 2. The LEGO NXT programmable brick set in a watertight housing.

Rubber buttons, set in the housing, enable the controls on the NXT to be

pressed. Alternatively a diver’s “pelican” box with snap shut lid can be

used (figure 1). A LEGO plate is bonded to the underside of the housing so

that students can add their own LEGO structures and motors.

III. WHY BUILD UNDERWATER ROBOTS?

When students design, build and program underwater robotic

vehicles, they are learning engineering fundamentals which

span virtually every engineering discipline. Additionally,

students are motivated by an exciting and stimulating design

scenario.

The use of projects based on small robotic vehicles is now

widespread in engineering curricula, however these are

predominantly wheeled, terrestrial vehicles. Such projects often

reduce to little more than exercises in applied programming,

losing valuable opportunities to present substantial mechanical

challenges or to incorporate real interdisciplinary engineering

design. In contrast, the underwater environment presents

unique design challenges and opportunities. The motion of an

underwater vehicle, through a three dimensional space with six

degrees of freedom, is more complex. Additional engineering

issues include propulsion, drag, buoyancy and stability.

Practical construction problems include how to waterproof

electrical components. The challenge of creating a robot which

can be sent to explore a hostile and inaccessible environment is

also motivating and stimulating to many students.

The aquatic environment is also preferable for investigations

of Braitenbergian ideas since it more closely resembles the

“primordial soup” in which Braitenberg envisions the evolution

of simple amoeba-like vehicle behaviours.

IV. WHY USE LEGO?

Our students work with a combination of LEGO and

additional simple materials. LEGO is particularly suited to

discovery based learning due to its ease and speed of assembly,

[2], [3]. This speed reduces the time between conception of an

idea and its implementation, enabling students to discover

through trial and error, rapidly test a range of alternative

designs and evolve their designs iteratively by observing the

relationship between structure and function. In contrast, when

students use conventional materials, which must be sawed,

drilled, glued, screwed or welded, the construction process is

lengthy and frustrating. Time constraints prevent students from

evolving their designs through multiple iterations of testing and

modification. Often there is no time allotted for the students to

fail, analyze the failure and then modify their design. In

contrast “We know that students will learn most deeply and

profoundly when they…have an opportunity to try, fail and

receive feedback on their work”, [4].

V. DISCOVERY BASED LEARNING

As far as possible we try to build our LEGO underwater

robotics classes upon “discovery learning” principles.

Discovery learning, [5], is a cognitive instructional model in

which students are encouraged to learn through active

involvement with concepts and principles, and teachers

encourage students to have experiences and conduct

experiments that permit them to discover principles for

themselves.

Although discovery learning is frequently employed in an

early childhood development setting, the instructional model

offers several advantages to a high school or undergraduate

setting. It arouses students’ curiosity, motivating them to

continue to work until they find answers, [6]. Students also

learn independent problem solving and critical thinking skills

because they must independently analyze and manipulate

information.

Students often benefit more from being able to engage in

active learning by “seeing” and “doing” things than from

passive learning by listening to lectures. Tackling material

from several perspectives and persevering with unresolved

problems improves students’ core intellectual skills - they learn

how to learn independently. Cognitive development is not the

accumulation of isolated pieces of information; rather, it is the

construction by students of a framework for understanding

their environment. Teachers should serve as role models and

facilitators by solving problems with students, explaining the

problem solving process and talking about the relationships

between actions and outcomes. Observing students during their

activities, examining their solutions and listening carefully to

their questions can reveal much about their interests, modes of

thought and understanding or misunderstanding of concepts,

[7].

Discovery based learning is a particularly effective means of

teaching the iterative approach to engineering design. Our

students are encouraged to approach engineering problems

through an iterative sequence of steps: Design/Test/Modify

(figure 1). In contrast, surprisingly little of conventional

engineering curricula are devoted to this design process, with

the learning experience of engineering students often bearing

little resemblance to the activities of professional engineers in

industry.

VI. OVERVIEW OF THE STEVENS “INTRODUCTION TO

UNDERWATER ROBOTICS” PROGRAM

Educators and engineers at Stevens Institute of Technology

are currently engaged in developing a set of educational

modules, which teach fundamental engineering principles

through the design, construction and testing of underwater

robotic vehicles. The strategies incorporated into our

underwater robotics projects foster an active, discovery

learning environment that integrates many mathematical,

scientific and engineering principles and will support

conceptual and skill-based learning, application of principles to

novel situations, collaborative learning and cooperative group

skills.

Initially we developed a Remotely Operated Vehicle (ROV)

project in which students build wire guided underwater

vehicles equipped with mechanical grabbers. Students then

used their ROVs to retrieve objects from the bottom of a pool.

This paper describes the initial trial of a follow on course in

which students build programmable Autonomous Underwater

Vehicles (AUVs) which respond intelligently to sensor

stimulus to complete a series of simple autonomous tasks.

These projects were initially pilot tested with high school

junior students who participate in our Exploring Career

Options in Engineering and Science (ECOES) summer

program. Following positive feedback from ECOES students,

the ROV course has now been introduced to our freshman

mechanical engineering curriculum. With a major grant from

the National Science Foundation ITEST program, these

projects and materials are being adapted and disseminated to

large numbers of middle and high school students across New

Jersey.

VII. PREVIOUS WORK – WIRE GUIDED ROV COURSE

Our previous work, [8], describes short courses, in which

students design, build and test wire guided Remotely Operated

Vehicles (ROVs) equipped with a mechanical grabbing device.

This same course has now been used successfully with middle

school, high school and university level engineering students.

In accordance with the principles of discovery learning,

students are not given detailed instructions or pre-packaged

“kits” with which to build their ROV. Instead they are set a

series of design challenges for which they must independently

invent their own solution. These challenges begin very simply

and become progressively more complex until the student

arrives at a completed ROV by the end of the course. As a final

challenge, each team has to use their ROV to retrieve and

manipulate objects on the bottom of a pool of water (figure 3).

The intermediary design challenges include:

1) Design a surface vessel with a single motor and various

propeller options, optimizing gearing ratios to maximize

speed in a single direction.

2) Design a surface vessel with steering, using two

independently controlled motors. The challenge involves

negotiating a figure eight course, around two buoys, in the

least amount of time.

3) Add a third motor to the vehicle, enabling vertical motion

in the water column.

4) Design a motorized mechanical manipulator which can

grasp specified objects.

5) Combine the products of stages 3, 4 and 5 to produce a

vehicle which can retrieve the greatest number of objects

from the bottom of the pool within a five minute period

(figure 3).

Notice that these progressively more complex stages of the

robot design, naturally tend to correspond to adding each

successive motor or each additional degree of freedom to the

robot.

VIII. BRAITENBERG VEHICLES

“Vehicles – Experiments in Synthetic Psychology” is a book

by Valentino Braitenberg, [1], a famous cybernetician and

neuro-anatomist. Braitenberg seeks to explain how the brain

may have evolved, how complex behaviors can result from

simple mechanisms, and particularly why one side of our brains

controls the opposite side of our body. He does this through a

series of elegant thought experiments with imaginary robot

vehicles which consist of motors connected to sensors.

The simplest Braitenberg vehicle is shown in figure 4. A

single motor is connected to a single sensor (e.g. a light

sensor). A positive connection indicates that the motor runs

faster as the sensed quantity increases. If the sensed quantity

were light, the vehicle would speed up and “run away” when it

entered bright areas, and tend to slow down and settle in dark

areas. Somewhat like a cockroach, we might say that this

vehicle is “scared of light” and prefers darkness. Conversely a

negative connection between sensor and motor will result in a

vehicle that likes to bask in bright areas but “dislikes” darkness

and runs away from dark areas.

Braitenberg next describes a series of vehicles which consist

of two motors and two sensors. By either wiring same side or

opposite side sensors to the motors using positive connections,

the vehicles will speed up as they approach light, either veering

away (“cowardice”) or homing in on and ramming

(“aggression”) the light source, figure 5. Alternatively, using

negative connections results in vehicles which slow down as

they approach light, either homing in and stopping (“love”) or

spending some time near the light before being attracted away

again on a new journey (“the explorer”), figure 6.

Figure 3. A LEGO ROV with mechanical grabber, built by high

school students over five laboratory sessions. The ROV was used to

retrieve wiffle balls from the bottom of a pool.

+ -

Figure 4. Single motor Braitenberg vehicles with positive and

negative sensory feedback (e.g. light-phobic and light-philic

respectively).

Braitenberg’s ideas are very powerful. They are simple and

accessible to students without prior knowledge or training, yet

convey fundamental ideas of feedback control systems and hint

at basic principles of neural networks and artificial intelligence.

Our aim is to use these principles to convey basic ideas of

feedback systems that enable a robot to interact with the world,

figure 7.

Unfortunately, in our experience, relatively few simple

educational robotics curricula emphasize this feedback process,

which we believe encapsulates the fundamentals of real

robotics. There are now numerous kits, projects or “camps on

disk”, aimed at getting young students, from middle school

age, interested in science and engineering through robotics

projects. Frequently these involve students programming a

simple, pre-determined sequence of events, without creating a

robot that genuinely interacts with a changing and unknown

environment.

IX. A PROTOTYPE SHORT COURSE IN AUTONOMOUS

UNDERWATER VEHICLES

In summer, 2007, 33 high school students participated in a

short course of five laboratory sessions (2 hours each), building

and programming AUV robots, as part of the Stevens

Exploring Career Options in Engineering and Science summer

program.

The aim of this course was to preserve the educational

principles and progressive, step by step format of our

successful ROV course, while exploring some of the ideas of

Braitenberg vehicles. As with our ROV course, students were

set a series of progressively more difficult design challenges,

gradually adding more degrees of freedom of motion and

finally arriving at a fully functional autonomous underwater

robot.

For challenge 1, students were given a single motor and a

pair of touch sensors. They were told to build a simple vehicle

which moves in a straight line across the surface of a pool.

When the vehicle touches a wall of the pool, the robot’s

direction is reversed, figure 8. Because the vehicles tend to

deviate from straight line motion, this results in a primitive

amoeba-like behavior with the robot repeatedly transecting the

pool in a random fashion.

Using the highly accessible NXT-G programming system,

this behavior can be generated with a very simple program,

figure 9.

Figure 5. “Aggression” and “Cowardice” behaviors,

using positive sensory feedback.

Figure 6. “Love” and “Explorer” behaviors, using

negative sensory feedback.

Figure 8. A simple “robot amoeba” uses touch sensors and “mechanical

wiskers” to reverse direction when it encounters the boundary of the

pool in which it lives.

Figure 7. An intelligent robot learns about a changing world via its

sensors and responds by using motors to intelligently exert changes on the

world (or its own position in the world). This leads to an iterative

feedback process. Unfortunately many educational robotics curricula do

not emphasize this feedback process, but instead have students program a

simple pre-determined sequence of actions.

motors

For challenge 2, the students begin to implement

Braitenbergian ideas. The behavior of challenge 1 is now

modified so that the robot’s speed is proportional to light

detected by a light sensor. This implements Braitenberg’s most

simple robot, as in figure 4. Now the robots move randomly

around the pool area, but dislike light and tend to settle in dark

regions. This behavior can be coded as in figure 10.

We can also explore negative Braitenbergian relationships

between sensed stimuli and motor speed, by setting motor

speed equal to “100 minus sensed light level” (where sensed

light level is also measured on a scale from 0-100), figure 11.

For challenge 3, students are given a second motor and a

second light sensor. They now begin to explore the more

advanced Braitenbergian attraction and aversion behaviors of

figures 5 and 6. These behaviors can be easily coded in the

NXT-G language by using two parallel threads, figure 12. The

code in figure 12 causes a robot to continuously update the

speeds of motor A and motor C with light levels measured by

sensor 4 and sensor 1. Depending on whether sensors 4 and 1

are placed on the same sides or opposite sides of the vehicle as

motors A and C, this robot will perform the “Agression”

behavior or the “Cowardice” behavior shown in figure 5.

We note that LEGO light sensors have a rather narrow field

of view, so that it can be frustrating to try to replicate the

scenario envisaged by Braitenberg, where robots are naturally

attracted to or averted from ambient regions of brightness or

darkness. Instead our students were issued with flashlights. The

robots are attracted to or repulsed by the flashlight beams. The

students thus can readily observe the Braitenbergian behaviors

but are also able to remotely steer their robot around the pool,

which they (the students and perhaps also the robots) find fun.

Figure 13 shows an example of a robot with two light

sensors for Braitenberg homing behaviors, built by high school

students. This behavior can also be used to make a robot follow

a line of lights, figure 14.

Figure 9. Icon based NXT-G programming language. “Within a

continuous loop, move forwards until a touch sensor is bumped,

then move backwards until a touch sensor is bumped. Repeat

indefinitely.”

Figure 10. “Move forwards while continually adjusting speed to be

proportional to sensed light level. Once a touch sensor is bumped,

repeat but in opposite direction.”

Figure 11. “Continuously monitor light levels. Set motor speed

proportional to 100 minus light level.” Hence in bright light, vehicle

moves slowly, whereas in darkness the vehicle will move fast.

Figure 12. 2D Braitenberg attraction and aversion behavior with the

NXT-G language. Depending on which side of the vehicle the

motors and sensors are placed, this code can result in the

“Agression” or “Cowardice” behaviors – robots home in on the light

or move to avoid the light.

Figure 13. Underwater robot with two light sensors (waterproofed with

clingfilm) for Braitenbergian light homing, built by high school students.

Light sensors

Figure 14. Braitenberg’s “aggression” behavior can also be used to

follow a line of lights.

For challenge 4, students begin sending their robots

underwater, modifying them to dive and surface. Students are

given additional motors and learn about buoyancy and

Archimedes’ principle. They modify the weights and floats on

their robots to achieve neutral buoyancy, and can then control

depth with motors connected to vertical propellers. The

students write a simple program that demonstrates this

capability by repeatedly diving to the bottom of the pool and

then re-surfacing, figure 15.

The first four challenges were completed in three laboratory

sessions. The fourth and fifth laboratory sessions were devoted

to a final challenge – to create a robot that can be deployed

anywhere in the pool and which will seek out and home in on a

light source placed on the bottom of the pool, figure 16.

The final challenge was attempted in various ways. Some

students tried to extend the Braitenburg behaviors and combine

them with search strategies. Some students tried random

searches followed by a dive command when a downwards

looking light sensor exceeded a threshold. Other students used

Braitenburg behaviors to guide their robots across the surface

of the pool using a flashlight, followed by a dive command

when a downwards looking light sensor exceeded a threshold.

X. STUDENT FEEDBACK

Out of the first 17 high school students to try this underwater

robotics course, 14 completed anonymous questionnaires.

Q1) On a scale of 1 to 5, how interesting did you find the

course? 1

totally

boring

2 3 4 5

very

interesting

Average

response

Num. of

responses 2 5 7 4.4

Q2) On a scale of 1 to 5, how fun did you find the course? 1

totally

boring

2 3 4 5

very fun

Average

response

Num. of

responses 1 5 8 4.5

Q3) On a scale of 1 to 5, how much do you feel you learned

about the following areas of engineering? Rating 1

2 3 4 5

Average

response

Robotics 2 1 7 4 3.9

Underwater

technology 1 1 5 7 4.3

Interdisciplinary

engineering 3 7 4 4.1

Computer

programming 2 6 2 4 3.6

Teamwork

skills 1 5 3 5 3.9

Q4) On a scale of 1 to 5, would you have liked to do this

activity in your high school or middle school classroom? 1

certainly

not

2 3 4 5

very much

Average

response

Num. of

responses 3 1 10 4.5

Q5) On a scale of 1 to 5, has this course helped stimulate your

interest in pursuing an engineering degree? 1

put me off

engineering

2 3 4 5

increased

interest

Average

response

Num. of

responses 1 1 7 5 4.1

XI. LESSONS LEARNED AND FUTURE WORK

One of the key reasons for attempting an educational course

around the theme of programmable underwater robots, was that

it would provide a project in which mechanical issues and

programming issues were truly integrated and interdependent.

Our earlier ROV course successfully explored a range of

mechanical design problems over five laboratory sessions.

However, trying to squeeze both mechanical tasks and

programming / algorithmic tasks into the same short amount of

time proved problematic. We suggest that to explore both these

Figure 15. Underwater robot submerged at bottom of pool.

Figure 16. An underwater robot seeks out an underwater light source.

issues properly needs more time. One possibility is to run both

the ROV course and then the AUV course consecutively.

Students might first explore the mechanical issues of

developing a wire guided submersible. They might then begin

using the NXT computer to control the completed submersible,

progressing from a mechanical focus to a programming and

algorithmic focus.

Submerging computers in a classroom is risky and

problematic. It is difficult to waterproof a programmable

controller in a manner which is robust against heavy classroom

wear and tear, remains accessible and usable and is also cost

effective. Diver’s “pelican” boxes provide a very reliable seal

and a snap-open lid which enables the microprocessor controls

to be accessed. However, the easily openable lid is source of

worry in a classroom which will always have some disengaged

and inattentive individuals. We have also tried industrial,

waterproof boxes which bolt closed, with rubber buttons set

into the lid to enable operation of the microprocessor controls.

With these, we experienced several leaks due to rubber buttons

being torn by fingernails or other abuse. The manufacturers

seals also proved of poor quality and failed on several

occasions. In future work, care must be taken to experiment

with a wider variety of boxes and button covers, to determine

robust and reliable brands.

Another issue with controllers sealed in boxes, is how to

download new programs to the controllers. Our students wrote

their programs on laptop computers. These programs were then

downloaded to the LEGO NXT controllers via Bluetooth,

which is able to penetrate the plastic boxes without the need to

unseal and reseal them. The NXT controllers are fully

Bluetooth enabled and are capable of communicating

wirelessly with PCs as well as with each other at ranges of up

to 100 meters. This is a powerful capability, however

classroom use was problematic. PCs frequently lose contact

with their associated NXT and the reconnection process can be

highly temperamental, time consuming and frustrating. It is

hard to teach students to do this for themselves, especially in a

small number of lab sessions, and so it is necessary to have at

least one instructor dedicating a large proportion of class time

to helping students reconnect their controllers. For this reason,

this approach necessitates two instructors for each class. Note

also that Bluetooth will only transmit through air and cannot

communicate with a vehicle while it is underwater.

An alternative solution, which might solve all three of the

above concerns, may be to work with “semi-autonomous”

robots, i.e. keep the microprocessor outside the water and use it

to control the underwater vehicle by wire. This is frustrating in

that some of the autonomous nature of the robots would be

diminished, however a richer range of classroom activities may

be enabled with this approach. Partly, the success of this

approach would hinge on finding suitably thin and flexible

connecting cables for controlling motors and receiving data

from sensors.

Note, although several of our programmable NXT

controllers did indeed become a little damp from time to time

during this project, they all subsequently made a full recovery

and appear to have suffered no long term ill effects from their

underwater experience.

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