Learning through Explorations in Robot Sensing and Navigation Authors: David J. Ahlgren, Department of Engineering, Trinity College, Hartford, CT 06106,
[email protected] Igor M. Verner, Dept. of Education, Technion – Israel Institute of Technology, Haifa, Israel, 32000
[email protected]
Abstract This paper considers learning in which students design and operate robots in order to explore the concepts of physical fields and principles of navigation. At Trinity College we have developed sensors that operate with the Lego Mindstorms Brick computer, which is programmed using the Interactive C (IC) language. Students have incorporated these sensors in a Lego-based robot that navigates reliably through the Trinity College Fire-Fighting Home Robot Contest maze. This exercise has strengthened students’ representation, mapping, and programming skills. An electronic development workshop course was developed in the Technion and taught as part of the “Electronics and Computers” subject in an Israeli high school (grade 11). The idea implemented in the course is to study electronic systems in the context of their application to operating a mobile robot. The students equip a mobile robot platform with sensor systems and perform assignments of automatic detection of electrical, magnetic, sound, and other fields. Assessment results indicated that experiments with different physical environments throughout the course contributed to the students’ better understanding of physical field concepts. Our case studies in Technion and Trinity showed that through robot development workshops students acquire system design skills and improved understanding of important physical concepts, algorithms, and coding methods that apply to many areas of engineering.
Index Terms Robots, experimentation, learning, navigation, physical fields INTRODUCTION Many educators believe that robotics is a suitable subject for project-based education at undergraduate and high school levels. Learning through designing, building and operating robots can lead to the acquisition of knowledge in high-tech engineering areas and promote development of systems thinking, problem solving, and teamwork skills that are in high demand in industry. Involvement of students in a robot contest can offer additional educational benefits of a focused, open-ended, interdisciplinary project that is a strong motivator of student creativity, self-directed learning, and research [1]. Educational robotics relies on the concept of constructionism [2]. This concept characterizes learning processes in which a learner is involved in the creation of external and sharable artifacts. The learner uses artifacts as “objects to think with” in order to explore, embody, and share ideas related to the topic of inquiry. Case studies show that the constructionist approach can be effectively used to educate students of all ages and levels of experience and to stimulate their intellectual maturity [3]. Robotics assignments lead students to purposeful activities of designing a robot prototype. One didactic problem of guiding projects in robotics is keeping the balance of two goals: to create a working robot prototype, and to provide systematic learning and understanding of engineering subjects by the students. Our educational surveys of robot soccer and fire-fighting competitions [4-7] indicated differences in the evaluation of progress in disciplines by students of different robot teams, and even within one team. We see a possible reason for this dispersion in different methods of project guidance and believe that they can be optimized towards achieving both design and learning achievements. This paper presents our case studies in which students learned the concepts of physical fields and navigation through robot design and activities and demonstrated their better understanding after the project.
ROBOT DESIGN AND EXPERIENTIAL LEARNING Many studies of experiential learning in engineering education use the model proposed by Kolb [8]. Accordingly, knowledge is acquired through the transformation of experience if the learning process consists of four stages: active experimentation, concrete experience, reflective observation, and abstract conceptualization. The process of experiential learning practice is represented as a sequence of advancing Kolbian coils [9]. The problem of facilitating learning through design experiences is considered in [10]. The premise was that design experiences do not provide meaningful learning automatically but through reflection and conceptualization activities of the experiential learning cycle. Our previous studies showed that interdisciplinary design experiences of robotics projects such as creating a fire-fighting robot can be examined using the "total design" model of Pugh [11]. In this setting different activities are performed and International Conference on Engineering Education
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different subjects are learned in parallel and through dividing responsibilities. Therefore, identifying key concepts of the project and facilitating their learning by each of the team-members becomes essentially important part of the project guidance. Next sections of the paper present our project guidance experience at the college and high school levels. Special attention is paid to robot operation for physical experimentation and measurement. Experimentation and measurements are essential in robot design, especially with regard to characterizing sensor response. Equipped with multi-channel analog-to-digital conversion, microcomputers used on autonomous robots read sensor data and respond to it in order to achieve such performance tasks as navigation, obstacle avoidance, and rescue. It is possible, then, to use the robot itself as a platform for measurement and characterization.
EXPERIMENTS IN ROBOT NAVIGATION AT TRINITY COLLEGE Background Experiments reported here have been motivated by participation in the Trinity College Fire-Fighting Home Robot Contest (TCFFHRC) (http://www.trincoll.edu/events/robot/). The contest’s main goal is to increase awareness of robotic fire fighting while encouraging use of robotics as a theme for teaching engineering design [4, 5]. Robots competing in the TCFFHRC must navigate through a maze and extinguish a candle in a race against the clock. In the contest’s Expert Division, robots are presented with a different maze on each run, requiring generalized maze solving algorithms. In the Junior, High School, and Senior Divisions, the robots know the maze geometry in advance. The maze for these three divisions includes four rooms and connecting hallways (Figure 1). The candle is placed randomly in one of the four rooms, and the robot must navigate autonomously to within 30 cm. of the flame before putting it out. This paper focuses primarily on work carried out by students enrolled in ENGR 120 (Introduction to Engineering Design—Mobile Robotics, an entry-level course aimed at first-year students who are prospective engineering majors. As reported in [5, 6], ENGR 120 introduces students to the engineering field, informs them about the discipline and philosophy of design, and engages teams in a semester-long fire-fighting robot design project. Desired educational outcomes are 1) development of an awareness of the engineering profession, 2) development of communication skills, and 3) development of basic engineering skills through hands-on robot design. Such engineering skills develop naturally by working with robots; they include use of lab instruments, software development using Interactive C (www.kipr.org/ic), and teamwork. In 2003, 24 students (8 teams of three students) enrolled in the course, and each team is assigned a mentor, an undergraduate who has taken the course, who acts as team advisor and facilitator. Teams attend a one-hour weekly workshop that focuses on robot design techniques, development of lab skills, and programming. A well-written text by Fred Martin [1] serves as the primary reference. To prepare for fire-fighting robot design, students carry out several projects from the book including development of Braitenberg vehicles and wall following algorithms. Navigation is a central problem in autonomous mobile robotics, and the navigation problem posed by the TCFFHRC maze challenges the first year students. Robots starting at the home position (labeled with ‘H’) in Figure 1 may travel down the hallway adjacent to room 1 by using dead reckoning or by following the left wall. Students have also navigated to room 4 as the first step. Their robots follow the right or left wall, turn left, and find the entrance to room 4. Note that the disconnected room 4 discourages simple wall following as a means to solve the maze. Contest rules impose a penalty whenever a robot touches a wall, so most robots continuously monitor their positions relative to walls. In addition, robots recognize entrances to the rooms by detecting white lines that mark the entrances. Once in a room, the robot can search for the candle flame using, for example, IR phototransistors or a Hamamatsu UV photomultiplier (www.hpk.co.jp/eng/products/ETD/uvtrone). Thus, the primary sensing tasks are determining proximity to walls and other obstacles, finding white room entrance stripes on the maze’s black floor, and finding the flame. Experimental Approaches in ENGR 120 ENGR 120 teams carry out a sequence of projects and experiments that lead to the completion of their fire-fighting robots. These activities are introduced out over the course of the semester during weekly workshops led by teaching assistants in a well-equipped robot engineering laboratory [4-5]. Project 1: Introduction to programming with Interactive C (IC). Students write IC programs that control Phoenix, a firefighting robot that placed first in the 1998 TCFFHRC. The exercise exposes students to dead reckoning, wall following, and maze navigation. Project 2: Introduction to the Handy Board. This workshop introduces students to the Handy Board (www.gleasonresearch.com) and to PC-based cross-software development. They develop IC programs that run on the Handy Board. International Conference on Engineering Education
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Project 3: Design of Handy Bug. In this workshop students build a wall-banging robot using Legos and the Handy Board following the outline presented in Martin, Ch. 2 [1]. Project 4: Braitenberg Vehicle Design. Teams develop a robot that follows light sources and responds to collisions. They build two light sensors and acquire data form them via the Handy Board’s A/D inputs. The workshop introduces students to applications of circuit laws (Ohm's Law, Kirchoff's Laws) and it develops practical skills (soldering, programming, interfacing). Project 5: Ranging Sensors. In two workshops, teams test ranging sensors and develop a wall following algorithm. This project is described in detail below. Project 6: Flame and Stripe Sensor Design. Over a two-week period, teams add a flame sensor (an infra-red phototransistor mounted in a reflector), and a stripe sensor. The latter detects the white stripes that mark room entrances. Project 7: System Integration. Students add a software-controlled fire extinguisher, and they integrate all hardware elements of the robot. Project 8: Navigation. Teams fine-tune their robot’s software. Navigation is the most challenging part of TCFFHRC robot development and, indeed, is the fundamental problem in mobile robotics. Teams solve the fire-fighting maze navigation problem by integrating knowledge of sensors, computer interfacing, and programming. Successful maze navigation depends on sensing devices; useful devices include IR rangers from Sharp Electronics (http://sharp-world.com/products/device/ctlg/esite23/table/), imaging cameras (www2.cs.cmu.edu/~cmucam/), or ultrasonic devices (www.acroname.com/robotics/parts/). Project 5 investigates the Sharp GP2D12, which offers a useful range of roughly 10 cm to 50 cm and is well suited for navigating in the TCFFHRC maze. A recently introduced variant, the Sharp GP2D120, is equipped with a wide-angle lens and can be used as close as 4 cm. The GP2D12 emits a modulated IR beam that reflects off of nearby surfaces to a position-sensitive detector (PSD) contained within the unit. The PSD approach provides insensitivity to changes in reflectance and color. The Sharp sensor has a threewire analog interface consisting of power (+5 Volts), Ground, and signal out. In Workshop 5, students build a cable to connect these lines to the Handy Board, and they add a low pass filter to reduce switching transients. They write programs to acquire sensor data, and they measure the sensor's response over the 10-50 cm range using the test station shown in Figure 2. In the second part of this exercise, each team develops and codes a wall-following algorithm that relies on data from the Sharp sensor. The wall following exercise introduces the concepts of negative feedback, closed loop control, and timing issues related to the sensor’s internal conversion delays. Experimental measurement of the response versus distance curve of the GP2D12 sensor proves several important benefits to students: • They extend their knowledge of computer interfacing and analog-to-digital conversion • They program their Handy Board to perform real-time sensor readings • They measure and record the response curve of the Sharp sensor. Figure 3 presents a family of response curves measured by eight teams of ENGR 120 students. Examination of this curve helps students to understand the main characteristics and limitations of the GP2D12: (1) the device exhibits high sensitivity in the 10 – 30 cm range, but the response curve flattens as distance increases, so distance discrimination may suffer; and (2) the output values are not unique—two distances, one less than 10 cm and one greater than 10 cm, yield the same reading. From this observation students learn that they must mount the sensors away from the edges of their robots so that the sensors cannot get closer than 10 cm from a maze wall. Navigation Code The measurements taken in Project 5 provide students with fundamental knowledge they need to develop maze navigation codes. First, they develop and test left and right wall following codes and develop codes for basic turns. Students are taught that accuracy of dead reckoning suffers as motor supply batteries discharge, and they are encouraged to invent sensor-based turns using multiple GP2D12 sensors. By combining wall following codes, turning codes, and stripe detection codes, they develop Interactive C codes for navigating from room to room. In a program developed by Trinity student Amir Tamrakar ’01, a function home_to_one causes the robot to: (1) navigate from the home spot (see Figure 1) into room 1, (2) search for the candle, and (3) extinguish the candle if found. If the candle is found in room 1, the home_to_one function returns the value 1; if not, it returns 0. Other functions (two_to_three, three_to_four, two_to_home, etc.) operate in a similar way. Each of these functions relies on lower-level functions that implement wall following, turning, candle sensing, and extinguishing. Tamrakar’s code, listed below, motivates a discussion of functions in C and demonstrates top-down top-down programming ideas that organize thinking and simplify debugging.
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void main() /* Function home_to_one navigates robot from starting /* point to room 1 and searches for candle. home_to_one /* returns value of: 1 if candle found and extinguished /* 0 if candle not found in room 1 /* Robot returns to home spot after finding the candle { if (/* Robot will navigate through maze once if (home_to_one()) one_to_home(); else if(one_to_two()) two_to_home(); else if(two_to_three()) three_to_home(); else if (three_to_four()) four_to_home(); }
*/ */ */ */ */ */
Low-Cost Lego Robot ENGR 120 teams use the Lego Mindstorms kit (http:// mindstorms.lego.com) as a quick prototyping medium. The Mindstorms kit includes a variety of interlocking blocks and plates, wheels, and gears. This $200 kit also includes a small microcomputer, the Mindstorms Brick, along with PC-based GUI-based development tools for the Brick. ENGR 120 teams have not yet used the Brick, having substituted for it a more capable, self-contained microcomputer, the Handy Board. Based on the Motorola MC68HC11, the Handy Board offers seven A/D ports, eight general-purpose digital inputs, several generalpurpose digital outputs, a 2 x 16 LCD screen, and IR and serial ports for communication and software downloading (www.gleasonresearch.com). Since addition of the Handy Board increases the cost per design team by $300, we attempted to solve the fire-fighting problem using the Mindstorms Brick. At least two C-language systems, Not Quite C (NQC) (www.baumfamily.org/nqc/) and Interactive C are available; for our experiments we chose IC since we had used it with the Handy Board. The main bottleneck was a lack of suitable sensors for use with the Brick. The Brick has only three sensor ports; students had used at least five Handy Board ports in fire-fighting robots. Sensors may be connected to the Brick through a unique two-wire interface that is time-division multiplexed to deliver power and accept signals; in a cyclical manner power is applied to the sensor and the sensor output is sampled [13]. Thus it was necessary to develop an interface that supervises power distribution and signal acquisition. This interface allowed us to multiplex analog data from three GP2D12 rangers and digital data from stripe and flame sensors. The Brick-based fire-fighting Lego robot “Firebrick” competed in the 2003 TCFFHRC and performed reliably, capturing 15th place in the Senior Division and completing three successful runs (Figure 5).
ELECTRONICS AND COMPUTERS STUDIES IN HIGH SCHOOL The robotics course presented below has been developed in the Technion and since 2000-2001 included in the curricula of the Nesher Senior High School for students of the eleventh grade, who study an optional matriculation subject “Electronics and Computers”. The subject is given in grades ten, eleven and twelve as a sequence of cources: switching systems, analogue electronics, microcomputers, and electronic development workshop. The course “Electronic development workshop” is intended to support the theoretical courses by a series of experiments with electronic systems. The guidelines of the Ministry of Education specify the topics of experiments to be conducted in the course, but leave development of experiments to schools. This gave us an opportunity to transform shape the electronic development workshop as a robotics course. Our idea implemented in the course is to study electronic systems in the context of their application to designing behaviors of a mobile robot in different physical environments. The students equip a mobile robot platform with sensor systems, to be used for automatic detection of electrical, sound, and other physical fields. The mobile robot platform used in our experiments is considered in the next section. Robot Platform We selected the Rug Warrior Pro [14] as a platform for electronics experimentation. This robot platform is distributed as a kit, including mechanical parts and electronic components: a MC68HC11A micro-controller, photocells for light sensing, an IR system, a sound detector (microphone), collision detectors (bumpers), and shaft encoders. The robot is equipped with two DC motors controlled by a H-bridge circuit. It has two driving wheels and a caster ball to be mounted on a chassis plate. An on-board Liquid Crystal Display (LCD) screen provides information on robot operations in real time. Robot behaviors
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can be programmed in the Interactive C language. After compilation the programs are downloaded to the robot board via the serial port. Important advantages of the Rug Warrior Pro platform in electronics experiments are: software for robot self-testing, an easy way of attaching external circuits, functions for programming parallel processes, and tutoring supported by the textbook [14]. However, we found the need to improve the kinematic scheme of the robot in order to provide more accurate performance of robot movements. To eliminate generating torques by the back caster ball, we replaced it by forward and back omni-directional wheels. We also significantly reduced robot’s weight by using a suitable battery pack. The pack was mounted in a way to provide the right position of the robot’s gravity center. A special bearing base was mounted on the robot in order to facilitate integrating external circuits to the board. Course curriculum The course consists in part of general electronic experiments (40 hours), and of a new part (50 hours) in which experiments are conducted in a robotic context.. The topics, experiments and learning hours of the robotic part of the course are detailed in Table II. Topics 1-3 introduce a mobile robot as a mechatronic system. Topics 4-5 deal with sensor measurements and operating robot movements in different fields. In the next section we will consider experiments with a robot and how students study them. Robotics experiments and assignments In E1.1 the students apply a self-test program, which is part of the Rug Warrior’s software. This program displays parameters of robot subsystems in operation on the LCD. The students learn to download task programs to the robot, and examine robot operations in the self-test mode. The goal of E1.2 is to operate robot subsystems by means of basic control commands. The students learn to operate motors, sensors and encoders. In experiment E2.1 the students program functions to be used in kinematics calculations with focus on practice in Interactive C. The next experiment, E2.2, is in programming behaviors based on concurrent operation of robot subsystems. The students learn to write Interactive C programs with conditioning and loops. An example of such a behavior is the task of straightforward motion until obstacle detection by a IR sensor and changing direction of motion to avoid a collision. The E3.1 experiment deals with wheels’ velocity control. Robot motions are programmed using commands, which include velocity control parameters (VCP) of the wheel motors. In practice, dependence of an actual wheel velocity from the VCP is not linear. The students are assigned to develop a procedure of determining values of velocity control parameters for given linear wheel velocities (LWV). They perform this assignment through the following stages: • Calibration of shaft encoders for measuring LWV. • Determination of linear wheel velocities for different values of VCP. • Programming an interpolation procedure of calculating VCP as a function of LWV. In the E3.2 experiment the students programmed the robot to perform two basic movements: linear to a given distance and circular with given radius and angle. This included coordination of motor velocities, shaft encoder measurements, and calculation of a final position and orientation. The next assignment A1 required programming a trajectory of the robot to a destination position, as a combination of linear and circular movements. Experiments E4.1 and E4.2 with different sensors were performed through the following general sequence of steps: assembling and testing the sensor circuit, attaching the circuit to the robot and connecting it to the board, measuring field intensities in various robot positions and calibrating the sensor. In assignment 2 the students had to program the robot to find a field source location and reach it. They dealt with different field sources and developed procedures based on three different methods. The first was a method of polar search iterations. Each iteration consisted of the following steps: a) intensity measurements with a 360º rotation of the robot around its center, b) calculating the intensity gradient and estimating the source location, c) linear movement toward an estimated source location. This method was implemented in the procedure of light source finding. The second triangular search method was used to find heat source locations by means of a pyroelectric sensor. It consisted of four steps: a) detecting the direction to the field source (DFS) through a 360º rotation of the robot in its initial location, b) linear movement orthogonal to DFS over a certain distance, c) determining DFS in a new location, d) calculation of the source location in the intersection of the two DFS and liner movement to it. The third method was finding sound sources through an iterative gradient search. At each of the iteration steps the direction of the greatest increase of field intensity is calculated and the robot moves in this direction to a Newton step distance. The gradient vector is determined as follows: - The robot executes linear movements AB and BC in two directions orthogonal to each other (AB _ BC). International Conference on Engineering Education
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The directional derivatives of the field intensity function in point B along directions BA and BC are calculated by measuring intensity increments along the segments. These derivatives are the gradient vector coordinates of the field intensity function.
Evaluation and assessment In the robotic part of the course we used three kinds of assessment. First, the students were tested in Interactive C and Kinematics topics. Second, the students were required to demonstrate actual performance of the robotic tasks, which they carried out in groups of three. Finally, the students were asked to present their personal contributions to the group work. Assessment results indicated that the students acquired knowledge and practical experience in electronics consistent with the learning objectives of the Electronic Development Workshop. In addition, the students gained important skills related to analyzing, building, and operating mechatronic systems. In the course evaluation, the focus of our ongoing educational study is on the following question: To what extent and how can the course contribute to improving students’ understanding concepts of physical fields? The idea of our study is that involvement of students in robotics activities, which integrate experiments with different physical fields, can help them to develop conceptual knowledge in this subject. The connection of practical activities and physical concepts was emphasized throughout the course. For example, in experiments with sensors the students studied characteristics of a field, mechanisms of their conversion to an electric signal by a sensor, and methods of data analysis. Additional lessons on mathematics and physics were given to students with lower prerequisites. Pre-course and post-course tests were conducted to assess the results of learning the subject. The tests comprised qualitative problems on understanding physical concepts similar to items of the Conceptual Survey in Electricity and Magnetism [15]. They also included tasks on applying methods of quantitative analysis. Test results were as follows: - Most of the students failed or showed low achievements in the pre-course test. - Most of the students succeeded in the post-course test. Their approaches to problems were correct and the mistakes related only to specific steps of the solution.
CONCLUSIONS We have presented case studies showing the richness of robotics as an experimental medium to teach fundamental concepts of design, navigation, physical fields, and algorithms. At Trinity College, work has focused on sensor development and applications, along with development of algorithms for navigating through the fire-fighting contest maze. Recent developments include design and application of sensor interfaces for use with the Lego Mindstorms Brick, and future work will focus on using the Brick in other physical experiments. At the Technion students have strengthened their knowledge of physical fields through robot-based mapping and gradient analysis. These experiments have naturally illuminated connections between the theoretical world and the physical world and have introduced students to sensors and sensor applications. Evaluation showed evidence of improved student learning and problem-solving skills as a result of robot design and robot-based experimentation. Our common experience is that robotics provides myriad open-ended opportunities for experiential learning and teamwork both at the university and high school levels. The rich learning environment that develops within team-based robotics recommends robot-based experimentation to educators at all levels. From our experiences conveyed in this paper we recommend that teachers who guide robot projects aim to achieve balance between the goals of building a working robot and the objectives of systematic learning and understanding subjects in the curriculum. Robot design experiences do not provide acquisition of knowledge automatically. Rather, students gain knowledge through a learning circle that starts with active experimentation and moves to acculumation of concrete experiences, reflective observation, and abstract conceptualization.
ACKNOWLEDGEMENT The authors thank Jake Mendelssohn, TCFFHRC Coordinator, Trinity students Trishan De Lanerolle ‘04 and Bozidar Marinkovic ‘05, who designed and built the Firebrick robot, and Technion student Igal Ushin who taught the robotics course in the Nesher High School.
REFERENCES [1]
Martin, F. "Robotic Explorations, a Hands-On Introduction to Engineering", Prentice Hall, 2001.
[2]
Harel, I. and Papert (eds.), S., "Constructionism", Ablex, Norwood, N.J., 1991.
[3]
Kafai, Y. and Resnick, M. (eds.), "Constructionism in Practice: Design, Thinking, and Learning in a Digital World", Lawrence Erlbaum, 1996.
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[4]
Ahlgren, D. and Verner, I. "An International View of Robotics as an Educational Medium", Proc. 2002 International Conference on Engineering Education, August 2002.
[5]
Ahlgren, D. J. "Fire-Fighting Robots and First-Year Engineering Design: Trinity College Experience." Proc. 31st ASEE/IEEE Frontiers in Education Conference, Reno, 2001.
[6]
Verner, I. and Ahlgren, D. "Fire-Fighting Robot International Competitions: Education through Interdisciplinary Design", Proc. 2001 International Conference on Engineering Education, August 2001.
[7]
Verner, I. and Ahlgren, D. "Fire-Fighting Robot Contest: Interdisciplinary Design Curricula in College and High School", ASEE J. Engineering Education, Vol. 91, no. 3, 2002, pp. 355-359.
[8]
Kolb,D. "Experiential Learning – Experience as the Source of Learning and Development", Prentice Hall, N.J., 1984.
[9]
Cowan, J. "On Becoming an Innovative University Teacher: Reflection in Action", The Society of Research into Higher Education, Buckingham, U.K., 1998.
[10] Shooter, S. and Shooter, C. "Enhancing Design Education by Processing the Design Experience", Proc. 2000 ASEE Annual Conference, St. Louis. [11] Pugh, S. "Total Design. Integrated Methods for Successful Product Engineering", Addison-Wesley, 1991. [12] Verner, I. and Hershko, E. "School Graduation Project in Robot Design: A Case Study of Team Learning Experiences and Outcomes", Journal of Technology Education (in press). [13] D. Wilcher. Lego Mindstorms Interfacing. TAB Robotics, McGraw-Hill, New York, 2003. [14] Jones, J., Seiger, B. and Flynn, A. "Mobile Robots, Inspiration to Implementation", 2nd ed., Natick, Mass., A. K. Peters, 1999. [15] Maloney, D., O'Kuma, T., Hieggelke, C. and Van Heuvelen, A. "Surveying Students' Conceptual Knowledge of Electricity and Magnetism", American Journal of Physics, Vol. 69, no. 7, 2001, pp. S12-S23.
FIGURES AND TABLES FIGURE 1 TRINITY COLLEGE FIRE-FIGHTING HOME ROBOT CONTEST MAZE 1 meter Room 3
Room 4 Room 2
Room 1
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FIGURE 2 TEST RIG FOR MEASURING SENSOR RESPONSE
FIGURE 3 FAMILY OF MEASURED RESPONSE CURVES, GP2D12, 8 TEAMS
160 140
Converted Reading
120 100
80 60 40
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FIGURE 4 FIRE-FIGHTING ROBOT “FIREBRICK”
TABLE I OUTLINE OF THE ROBOTIC PART OF THE COURSE Learning topics
Experiments (2 hours each)
1. Mobile robot and its components (4 hours) 1.1. Robot’s block diagram. 1.2. Components and their functions.
E1.1. Functional testing of robot components in a self-test mode. E1.2. Basic commands for operating robot subsystems.
2. Interactive C language and programming robot behaviors (4 hours)
E2.1. Programming of simple functions. E2.2. Integrated robot behaviors.
2.1. Basics of Interactice C. 2.2. Parallel processes in operating robot subsystems. 3. Kinematics of a robot with two driving wheels (4 hours) 3.1. Shaft encoders. 3.2. Determining kinematic parameters of straight and circular movements. 3.3. Dependence of robot position coordinates on the kinematic parameters
E3.1. Calibrating wheels’ velocities by shaft encoders, and closed loop control programming. E3.2. Programming of straight and circular movements. Assignment A1 (4 hours): Programming of robot motion to a given destination position.
4. Sensors and detecting fields (4 hours) 4.1. Physical signals, their detection mechanisms and sensor circuits. 4.2. Analyzing circuits of light intensity, sound, and pyroelectric sensors.
E4.1. Design and building of photocell, microphone and pyroelectric sensor circuits. E4.2. Integrating sensor circuits into the robot, testing by a controller.
5. Robot operating in physical fields (8 hours). 5.1. Basic concepts of physical fields. 5.2. Vector fields: gravitational and electrostatic. 5.3. Light and sound intensity, and temperature fields. 5.4. Navigating a robot in physical fields.
Assignment A2 (6 hours): Finding point sources of light, sound and heat, and reaching them by a robot.
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