Dissertation Defense By Abraham L. Howell Thursday, March 29, 2012

Committee Members Roy T.R. McGrann, Chair - ME Dept. Bruce T. Murray, Member ME Dept. Richard R. Culver, Member - ME Dept. Richard R. Eckert, Member - CS Dept. Harold W. Lewis III, Member - SSIE Dept. Patrick P. Madden, Outside Examiner CS Dept.

Defense Outline Background and motivation Review of engineering education literature Statement of Research Developed teaching and robotics research tool, Open-Robot Initial investigative research results for Open-Robot Open-Robots use in swarm research Final research results for Open-Robot Final conclusions and future work

Background and Motivation Robots as a Teaching Tool Captivate Motivate Reinforce Concepts & Theories Real World Context Extend Concepts & Theories STEM Careers

Background and Motivation Robots as a Research Tool for Swarm Robotics Validate Simulator Results Test New Hardware Designs Cost Centralized & Decentralized Test New Algorithms Real World Interactions

Review of Engineering Education Literature Educational Spectrum K-12Undergraduate Engineering Robotics, Programming, Engineering Science, Technology, Engineering & Mathematics (STEM) Classroom, Laboratory, Problem-Based, and Project-Based Learning, Design Competitions and Early Research Involvement Summer CampsCapstone Design Projects Attitudinal Surveys Pre-Post Tests Assessment

Statement of Research The primary focus of this dissertation has been to develop and validate a low-cost, flexible, open source robot that can be used as a teaching tool across the educational spectrum and as a research tool in the area of swarm robotics. Question 1 Can a specially developed robot teaching tool positively impact student learning? Question 2 Can a specially developed robot research tool be used to validate swarm robotics algorithms and hardware designs? Hypothesis 1 A symbiotic relationship between engineering educators and swarm robotics researchers can be created when a common robot platform is utilized by both parties.

Developed Teaching and Robotics Research Tool - Open-Robot

Initial Investigative Research Results for Open-Robot Use in undergraduate bioengineering course. Autonomous Agents course. Use in undergraduate computer science course. Microcontrollers and Robotics course. Use in high school programming classes. Java and C++ classes. Use in high school physics class. Tele-research with Minnesota High School.

Use in Undergraduate Bioengineering Course In the Spring of 2006, an earlier version of Open-Robot, BIObot, was used in BE380B Autonomous Agents as part of a National Science Foundation (NSF) Curriculum and Laboratory Improvement (CCLI)Grant. All the laboratories were designed to leverage BIObot as a teaching tool. Specially designed Pocket PC software provided students with a means of interacting and controlling BIObot wirelessly. Course focused on the following: habituation, sensitization, reflexes, classic control theory, fuzzy logic systems, neural networks, genetic algorithms and genetic programming.

1.Working with the BIObot robots helped me to understand the course concepts better. 2.The software interfaces for the BIObots and the Pocket PC's were easy to work with. 3.Using the BIObots was fun. 4.The BIObots helped me to learn how to use the various methods and techniques learned in class. 5.The concepts and ideas from the lectures were not clear to me until we used them with the BIObots in the lab. 6.The BIObots made me want to learn more so that I could make the robots do more things. 7.It was easy to figure out how to program the BIObots. 8.The BIObots made me appreciate the difference between a classroom example and running a system in the real world.

Use in High School Programming Classes An earlier version of Open-Robot, BIObot, was leveraged in (2) high school programming classes that focused on Java and C++. Robot intervention occurred near the end of the class and just prior to the final project. A total of (3) lectures and corresponding laboratories provided an introduction to the robots, sensors, serial communication and specially designed C++ and Java class libraries. First lecture and lab focused on introducing robot and its sensors. Second lecture and lab familiarized students with corresponding class library. Third lecture discussed how to create behaviors such as obstacle avoidance and light tracking.

1.Using BIObots was fun. 2.It was easy to figure out how to program BIObot. 3.The BIObots made me want to learn more so that I could make the robots do more things. 4.The BIObots made me appreciate the difference between a classroom example and running a system in the real world. 5.After working with BIObot I am more interested in science. 6.The use of BIObots in this class is a good idea. 7.Using BIObots enhanced my interest in this class. 8.After working with BIObot I am more interested in engineering.

Use in Undergraduate Computer Science Course In the Spring of 2007, an earlier version of Open-Robot, BIObot, was integrated with a microcontrollers and robotics course. Robot was used as a teaching tool in (3) out of the (12) laboratories. Low-Level Serial I/O Wireless Communication with Bluetooth High-Level Robot Behaviors Results of this work were published and presented at the 2008 ASEE Annual Conference and Exposition, Pittsburgh, PA

1.I enjoyed working with the BIObot robot in this lab. 2.BIObot helped to clarify the concepts associated with this lab. 3.It was easy to interface BIObot with the QwikFlash microcontroller board. 4.BIObot helped me to better understand real-world hardware/software interaction. 5.Working with BIObot increased my interest in this lab. 6.BIObot helped me to better understand serial communication. 7.I recommend using BIObot in this lab for future course offerings. 8.I would like to work with BIObot again.

1.I enjoyed working with the BIObot robot in this lab. 2.BIObot helped to clarify the concepts associated with this lab. 3.Connecting BIObot with a desktop computer was not difficult. 4.BIObot helped me to better understand wireless robot control. 5.Working with BIObot increased my interest in this lab. 6.Programming BIObot was not difficult and it helped me to better understand the issues associated with wireless control. 7.I recommend using BIObot in this lab for future course offerings. 8.After working with BIObot, I am more interested in learning about wireless control.

1.I enjoyed working with the BIObot robot in this lab. 2.BIObot helped to clarify the concepts associated with this lab. 3.It was easy to program BIObot for obstacle avoidance. 4.Programming BIObot for two competing behaviors (obstacle avoidance and light tracking) is difficult. 5.Working with BIObot increased my interest in this lab. 6.BIObot helped me to better understand how robots sense objects and navigate in unknown environments. 7.I recommend using BIObot in this lab for future course offerings. 8.BIObot helped me to better understand how to program robots for real-world applications.

Use in High School Physics Class Long Distance Educational Research In 2009 a total of (10) Unassembled Open-Robot kits were leveraged by a Minnesota high school. Robots were used as part of a project-based learning opportunity for a physics teachers class. A total of (30) students worked in small groups to perform all the required soldering and mechanical assembly. Also had to perform initial electrical debug. In order to satisfy the final deliverable each group had to develop a program that controlled their Open-Robot with a behavior of their own design.

S1: Ability to identify a capacitor, resistor, and voltage regulator. S2: Knowledge and understanding of common electronic components i.e. capacitor, resistor, and voltage regulator. S3: Ability to solder through-hole components. S4: Knowledge and understanding of robot sensors. S5: Ability to assemble a programmable robot. S6: Knowledge of robot programming.

1.I enjoyed assembling OPEN-ROBOTs circuit boards. 2.I enjoyed assembling OPEN-ROBOTs mechanical components. 3.I did not know how to solder until working with OPEN-ROBOT. 4.It was fun to work with a real-world robot like OPEN-ROBOT. 5.Working with OPEN-ROBOT has increased my interest in this class. 6.I want to learn how to program OPEN-ROBOT. 7.I recommend using OPEN-ROBOT in future classes. 8.I would like to work with OPEN-ROBOT again.

Use in High School Physics Class Comments from Students Awesome This was probably the best part of my senior year. Thanks for the opportunity. It was a great experience!!! Fun. Robot = learning physics. This was the highlight of my senior year in high school. It was a good experience.

Initial Research Results Issues with Assessment Methodology Question 1: Can a specially developed robot teaching tool positively impact student learning? Based upon the initial results we have gained additional insight and say yes this appears to be possible! However, these results only reveal that students overall perceived an increase in knowledge, skills/abilities and interest in the subject matter under investigation. We must directly quantify changes in student knowledge and correlate this to working with the teaching tool, so that this work can be deemed a rigorous validation. How do we achieve this goal?

Open-Robots Use in Swarm Research Using RFID and Open-Robot to evolve foraging behavior. Using ZigBee to control a swarm of Open-Robots. Cultural Transmission in a swarm of Open-Robots.

Using RFID and Open-Robot to Evolve Foraging Behavior As part of the Swarm Robotics Research component, Open-Robot was used in an experiment to evolve foraging behavior. RFID tags were loaded with virtual food and then embedded in a 4x8 foot environment. Custom C# software was developed and leveraged Genetic Programming as a means of evolving foraging behavior for Open- Robot. Results of this work were published and presented at the 2006 Genetic and Evolutionary Computation Conference, which was held in Seattle, WA

Using ZigBee to Control a Swarm of Open- Robots A total of (3) Open-Robots were outfitted with a custom-designed ZigBee circuit board. ZigBee is a low-cost, low-power wireless alternative to WiFi 802.11. Allows for one-to-one and one-to-many network communication. A custom centralized software controller was developed and used to provide foraging behavior to all (3) Open-Robots. Demonstrated how ZigBee could be used to wirelessly control a swarm of robots. Results of this work were published and presented at the 4th International Conference on Cybernetics and Information Technologies, Systems and Applications (CITSA 2007)

Cultural Transmission in a Swarm of Open- Robots A total of (12) Open-Robots were used as part of a graduate students masters degree thesis work in BUs Bioengineering department. This work focused on developing a novel, decentralized control technique for swarms of robots. RFID tags were leveraged as a mechanism for robots to transfer their respective behaviors indirectly to other robots in the swarm. Robots searched the environment using different motion behaviors. Results of this work were presented and published at the 2nd IEEE Symposium on Artificial Life (2009), which was held in Nashville, TN.

Final Research Results for Open-Robot Assessment of student performance. Specially developed pre/post/post testing methodology. Course layout. Learning modules and objectives. Results of Pre/Post testing. Results of Sentence Stem surveys. Final conclusions and future work.

Assessment Methodology Need for a Novel Quantitative Method The initial investigative research phase solely leveraged attitudinal surveys in an attempt to solicit student feedback with respect to Open- Robots educational effectiveness in specific learning environments across the educational spectrum. Attitudinal surveys or student-centric perspectives do not actually quantify the degree to which a students learning is affected, but instead they quantify each students attitude or perception regarding the usage of Open-Robot as a teaching tool. The above-statements greatly illuminate the need for a novel quantitative methodology that will rigorously measure changes in student knowledge throughout the learning process. How do we achieve this goal?

Specially Developed Assessment Methodology Ideal Model Student Knowledge Progression of Time within Learning Module Pre-Lecture Test Post-Lecture Test Post-Laboratory Test Learning Module

Learning Objectives- Learning Module#7 Serial IO 1. Demonstrate the fundamentals of synchronous and asynchronous serial communication. a. Be able to represent asynchronous and synchronous serial communication data frames in a graphical manner. b. Be able to calculate baud rates and character transmission rates. 2. Interface a microcontrollers UART with a desktop or laptop computers RS-232 serial port. 3. Configure a microcontrollers UART for asynchronous serial communication using assembly level programming. 4. Develop and debug assembly level code for receiving and sending ASCII readable characters.

Learning Objectives- Learning Module#8 Embedded C 1. Understand the fundamentals of A/D hardware and how it can be used to interface robotic sensors. 2. Interpret non-linear sensor outputs and correlate to real-world units. 3. Understand the fundamentals of robotic odometry and how wheel encoders can be used for control feedback. 4. Develop and debug C code for a mobile robot that receives serial commands, executes commands, and sends back command responses. 5. Debug C code using an in-circuit programmer/debugger tool.

Learning Objectives- Learning Module#9 Wireless Robot Control 1. Understand the fundamentals of wireless control in the context of robots. a. WiFi, ZigBee, Bluetooth and infrared communication. b. Communication protocols and how to interface with hardware. 2. Understand the fundamentals of simple control algorithms and develop high- level programs that control a robot across a wireless connection. 3. Understand the fundamentals of proportional control algorithms and develop high-level programs that control a robot across a wireless connection. 4. Be able to tune the proportional gain parameters of a given proportional control system. 5. Be able to select a suitable control algorithm based upon system requirements.

Learning Objectives- Learning Module#10 Control Architectures 1. Understand the fundamentals of reactive, deliberative and subsumption control architectures. a. Be able to select a viable control architecture for a given problem. b. Design suitable subsumption control architecture for a given problem. 2. Develop and debug high-level code that implements subsumption control for a wireless robot. a. Leverage multithreading or simple timers along with a task coordinator.

Learning Objectives- Learning Module#11 Fuzzy Inference Systems for Robot Control 1. Understand the fundamentals of fuzzy inference systems (FIS). a. Define a suitable FIS using a block diagram. b. Setup and configure a FIS. c. Define and represent fuzzy relations for linguistic variables. d. Calculate triangular and trapezoidal membership functions. e. Evaluate If-Then Rules. f. Calculate fuzzified and defuzzified outputs. g. State the difference between Mamdani style and Fuzzy Singleton defuzzification. 2. Design, implement and tune a FIS that supplies different behaviors that control a wireless robot.

Results of Pre/Post/Post Testing Each student was assigned a number, so that their corresponding pre- lecture, post-lecture and post lab results could be analyzed. This provides a more in-depth view into how a specific students knowledge evolved throughout the learning process. Unfortunately there were only a total of 9 students that participated in this study. In order to deem the results from each learning module statistically significant they were first analyzed for normality using Anderson-Darling. A Paired T-Test was used to determine whether or not the post-lecture and post-lab score changes were significant.

Results of Pre/Post/Post Testing Testing for Normality using Anderson-Darling Test H 0 : Data follows a Normal Distribution H 1 : Data does not follow a Normal Distribution If P-Value >.05 & AD < Critical Value (CV), then accept null hypothesis and conclude that data follows a normal distribution. If P-Value .05 & AD > Critical Value (CV), then reject null hypothesis and conclude that data follows a non-normal distribution.

Results of Pre/Post/Post Testing Results of Paired T-Test H 0 : Test A to Test B Differences are not significant H 1 : Test A to Test B Differences are significant If P-Value

Documents

Dissertation Defense By Abraham L. Howell Thursday, March 29, 2012