Braitenbergian experiments with simple aquatic robots

Braitenbergian experiments with simple aquatic robots Rustam Stolkin, Richard Sheryll, Liesl Hotaling Stevens Institute of Technology Hoboken, NJ 07030, USA

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: [email protected]. 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: [email protected]. Liesl Hotaling is Assistant Director of the Center for Innovation in Engineering and Science Education at Stevens Institute of Technology, email [email protected].

Figure 1. A programmable AUV with light sensors, built using a combination of LEGO and other simple materials.

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.

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).

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. +

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Figure 4. Single motor Braitenberg vehicles with positive and negative sensory feedback (e.g. light-phobic and light-philic respectively).

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.

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.

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.

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 Figure 5. “Aggression” and “Cowardice” behaviors, using positive sensory feedback.

Figure 6. “Love” and “Explorer” behaviors, using negative sensory feedback.

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.

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.

motors

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.

Using the highly accessible NXT-G programming system, this behavior can be generated with a very simple program, figure 9.

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 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.”

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.

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.”

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.

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 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. Light sensors

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.

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

Figure 13. Underwater robot with two light sensors (waterproofed with clingfilm) for Braitenbergian light homing, built by high school students.

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.

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

Num. of responses

3

4

2

5

5 very interesting 7

Average response 4.4

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

2

Num. of responses

3

4

5 very fun

Average response

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

Robotics

2

1

7

4

Average response 3.9

Underwater technology Interdisciplinary engineering Computer programming Teamwork skills

1

1

5

7

4.3

3

7

4

4.1

2

6

2

4

3.6

1

5

3

5

3.9

Figure 15. Underwater robot submerged at bottom of pool.

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.

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

Num. of responses

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

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.

3

4

5 very much

Average response

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 Num. of responses

2

3

4

1

1

7

5 increased interest 5

Average response 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

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. REFERENCES [1] [2] [3]

[4] [5] [6] [7] [8]

V. Braitenberg. Vehicles: Experiments in Synthetic Psychology. MIT Press, 1984. M.D. Portsmore, C. Rogers. Bringing Engineering to Elementary School. Journal of STEM Education. Vol 5. 2004. Wang. E., LaCombe, J., and Rogers, C., Using LEGO Bricks to Conduct Engineering Experiments. Proceedings of the ASEE Annual Conference and Exposition. 2004. Bain. K., Creating a Natural Critical Learning Environment in Large Lecture Classes. Invited lecture, Stevens Institute of Technology. 2005. Bruner, J., Toward a Theory of Instruction. Harvard University Press. 1966. Berlyne, D.E., Curiosity and education. In J.D. Krumboltz (ed.), Learning and the educational process. Rand McNally. 1965. Slavin, R., Educational Psychology: Theory and Practice. Allyn and Bacon. 1994. R. Stolkin, L. Hotaling, R. Sheryll. A simple ROV project for the engineering classroom. Proc. IEEE / Marine Technology Society OCEANS conference, 2006.