Simplified Online Q-Learning for LEGO EV3 Robot

Total Page:16

File Type:pdf, Size:1020Kb

Simplified Online Q-Learning for LEGO EV3 Robot Simplified Online Q-Learning for LEGO EV3 Robot Ke Xu∗, Fengge Wuy, and Junsuo Zhaoz Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences ∗Email: [email protected] yEmail: [email protected] zEmail: [email protected] Abstract—Q-learning is a kind of model-free reinforcement on the Lego’s embedded CPU[5], [6]. The main drawback of learning algorithm which is effective in Robot’s navigation the Lego’s embedded computation resources is its file writer applications. Unfortunately, Lego Mindstorms EV3 robot’s file speed. It will take Lego about 1 minute to write a 20 KB writing speed is sometimes too slow to implement Q-learning file to its memory. When it comes to large state-action space, algorithm. In this paper, an approach is proposed to simplify reinforcement algorithm’s running time will be intolerable. Q-learning discrete value table into a new version that stores From the above discussion, it appears that at present no only one optimum action and its Q-value instead of storing every action’s Q-value in each state. Exploration and contrast algorithms that can deal with large state-action space with experiments show that our algorithm learns much faster than Lego’s embedded CPU have been proposed. the original Q-learning without losing the ability to find a better In this paper, the primary goal is to simplify the RL policy in navigation task. algorithm under thousands of states and make it possible to learn online tasks with Lego’s embedded CPU. As a result, I. INTRODUCTION a simplified online Q-learning(SOQ) algorithm that cost less memory is proposed to make the learning process faster Applying reinforcement learning(RL) to a Robotic control than the original Q-learning algorithm. Finally, a navigation system with limited computational resources, limited memory, experiment tests the performance of SOQ algorithm on a limited I/O etc, is a challenge task. Both conventional RL Lego Mindstorm EV3 robot system. Empirical result indicates algorithms such as Q-learning, SARSA and more advanced that SOQ algorithm can make the robot perform better in methods like gradient descent temporal difference algorithms navigation experiment and reduce learning time at the same have O(n) computation and memory complexity, nevertheless time. they sometimes cost too much for a Robotic control system to learn online tasks[1]. So there is a need for simplifying the The remainder of this paper is organized as follows: current RL approach to improve the performance of current Section II explains the proposed simplified online Q-learning robotic control system. algorithm. Section III describes navigation experiment setup. Section IV describes experimental results. Section V concludes Over recent years the increasing availability of low-cost the paper. programmable robotics system has lead to a growth in the implements of reinforcement learning control experiment. The II. SIMPLIFIED ONLINE Q-LEARNING ALGORITHM Mindstorms robots manufactured by Lego is one of this kind of robotics systems that have relatively low cost, programmable, A. Q-Learning and could accomplish a variety kinds of tasks with their In reinforcement learning, the agent learns a behavior based flexible physical structure[2]. The approaches that implement on its expected reward. In the Q-learning implementation of RL experiments for Lego Mindstorms robots may be classified reinforcement learning, expected rewards are stored in a state- into three categories. The first approach is to use computer sim- action pair array. The elements of this array is called Q-values. ulation before real robot experiment[3]. Simulation is easier to Q-learning is a kind of temporal-difference method that learns set up, less expensive, fast, more convenient to use, and allows an state-action value instead of learning state value. Q-values the user to perform experiments without the risk of damaging are directly related to value function as follows: the robot. However, many unpredictable robot behaviours are V ∗(s) = max Q(s; a) (1) not perfectly modeled by robotics simulation software. The a second approach is to use computer as robots’ brain with wire or wireless connections to robots instead of robots’ embedded CPU and memories[4]. Computer has much larger memories Q-functions have a very important property: a temporal- and much faster CPU which makes implementation of a much difference agent that learns a Q-function does not need a model 0 stronger RL algorithm become possible. The main drawback of the form Pa(s; s ), either for learning or for action selection. of this approach is that the robot is not totally independent. That is to say, it requires no model of state transitions, all Robots will not work if losing the connection with computer. it needs are the Q-values. The update equation for temporal- The third approach is to reduce the computation and memory difference Q-learning is: request of the program. In some wandering, line following and Q(s; a) = Q(s; a) + α(R(s) + γ max Q(s0; a0) − Q(s; a))(2) obstacle avoidance experiments, there are less than 20 states a0 and 10 actions in the program, which are small enough to run where α is a learning rate parameter. Compared to equation robot will be able to memorize former result. Unfortunately, 0 (1), it is obvious that Q-learning could get rid of Pa(s; s ), the robot need about 8 minutes to refresh all the Q-values which means that computation of Q-values does not need state to the memory file. This is intolerable for an online learning transitions model. task. One possible way to improve it is using off-line learning: connect the robot to a computer and use it to compute all tasks. Algorithm 1 Q-Leaning-Agent returns an action[7] But sometimes the learning need to be finished online. For 1: Q; a table of action values index by state and action example, when the robot explores a harsh environment such 2: s; a; r; the previous state, action, and reward, initially null as desert or outer space, it is difficult to do off-line learning. 3: if s is not null then 0 0 4: Q(s; a) Q(s; a) + α(r + maxa0 Q(s ; a ) − Q(s; a)) 5: end if 6: if s0 is terminal then 7: s; a; r null 8: else 0 0 0 0 9: s; a; r s ; arg maxa0 f(Q(s ; a )); r 10: end if 11: return a In the discrete state-action value table implementation of Q-learning, the number of Q-values is jSj × jAj size, which means in each state, every action’s Q-value should be calculate. For example, if there are 1000 states and have 10 possible actions, that will lead to 10000 Q-values to keep. To get the optimum policy, an agent should follow the action whose Fig. 2. State-action Q-values in simplified online Q-learning. Q-value table Q-value is the maximum in each state. In this approach, it keeps every state’s Q-value and its best action. did not come up to any policies, the solution is state-action pair’s maximum value. So this approach is more likely a value iteration rather than a policy iteration algorithm for A simplified online Q-learning is proposed to reduce mem- reinforcement learning. Another Q-learning problem is a so- ory cost to accelerate learning speed. Instead of keep every called exploration-exploitation trade-off problem. Since go action’s Q-value in each state which is shown in algorithm through every action in each state to find their Q-values is 1, algorithm 2 only keep one best action’s Q-value in each impossible in practice, the agent have to exploit the predicted state. When the robot find a better action whose Q-value is rewards stored in the Q-table before it finished exploring all bigger than previous best action, it will replace previous best the state-action reward space with a random possibility. That action to a new one. In this method, there is only one action is to say, at each time step, if the agent randomly choose needs to keep in each state. So the Q-value table’s size will exploitation, it will use the best available action. If not, an be about 8 times smaller than the original Q-value version in action which has not been explored will be selected. the navigation experiment. Now SOQ algorithm need about 1 minute to refresh Q-value table in each iteration of exploration. It is about 8 times faster than the original Q-learning approach. III. NAVIGATION EXPERIMENT A. Lego Mindstorms EV3 Robot The experiment implemented SOQ algorithm in Java lan- guage using the LeJOS implementation for the Lego Mind- storms EV3 robot.[8] Figure 3 shows that the robot has two pedrails and an infrared sensor. Each pedrail can move back and forth freely, so the robot could move forward, backward and turn around. The infrared sensor could detects objects and track the remote infrared beacon. In the experiment, the robot’s Fig. 1. State-action Q-values in original Q-learning. Q-value table keeps goal is to find the remote infrared beacon as fast as possible. every state-action’s Q-value. The detected distance of the infrared sensor beacon mode is said to be 200cm in the EV3 guide book. In the practical measurement, the valid measure distance is about 80cm with B. Simplified Online Q-learning the display number from 0 to 100. And between 80-200cm, The main drawback of the original implementation of the infrared sensor distance display number remains maximum.
Recommended publications
  • A Zipliner's Delight
    A Zipliner’s Delight Subject Area(s) Physical Science, Science and Technology Associated Unit Yellow highlight = required component Associated Lesson Activity Title A Zipliner’s Delight Figure 1 ADA Description: Caption: Figure 1: Zipline robot basic assembly and components Image file: figure_1.tiff Source/Rights: Wikimedia Commons (left) Copyright © 2011 Polytechnic Institute of NYU. Grade Level 06 (05-07) Activity Dependency Time Required 2x(40-60 minutes) Group Size Expendable Cost per Group US$___ Summary Students learn about potential energy, as expressed as the height of an object along a linear on- dimensional zipline track. A robot, designed to traverse the track, converting stored potential energy into kinetic energy, also is capable of monitoring the instantaneous speed of the robot using various sensors. Thus, students are able to quantify and compare the starting potential energy (height) of a robot and the conversion thereof into kinetic energy (linear displacement). The system that is presented is that of a single robot, which is built using the LEGO Mindstorms robotics platform and installed with Lejos 0.9 firmware. Engineering Connection Engineering Category = #2 Keywords speed, potential energy, kinetic energy, robot, data collection Educational Standards Choose from http://www.jesandco.org/asn/viewer/default.aspx. ITEEA (provide standard number, grade band, benchmark letter and text): State/national science/math/technology (provide source, year, number[s] and text): NY Science Standard 1.2 Scientific Inquiry NY Science Standard 6.2 Models Pre-Requisite Knowledge Teacher should be familiar with LEGO NXT Mindstorms as a building and programming platform. Knowledge of pairing NXT Intelligent Bricks via Bluetooth is necessary.
    [Show full text]
  • Educational Hands-On Testbed Using Lego Robot for Learning Guidance, Navigation, and Control ?
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Loughborough University Institutional Repository Educational hands-on testbed using Lego robot for learning guidance, navigation, and control ? Seungkeun Kim, Hyondong Oh ∗ Amir Kolaman ∗∗ Antonios Tsourdos, Brian White ∗ Hugo Guterman ∗∗∗ ∗ Autonomous Systems Group, Dept of Informatics and Systems Engineering, Cranfield University, Defence Academy of the UK, Swindon, SN6 8LA UK (e-mail: s.kim@ cranfield.ac.uk, h.oh@cranfield.ac.uk, a.tsourdos@cranfield.ac.uk, b.a.white@cranfield.ac.uk) ∗∗ Department of Electro Optical Engineering, Ben Gurion University of the NEGEV, P.O.B 653 84105 Beer Sheva, Israel, (e-mail: [email protected]) ∗∗∗ Department of Electric Engineering, Ben Gurion University of the NEGEV, P.O.B 653 84105 Beer Sheva, Israel, (e-mail: [email protected]) Abstract: The aim of this paper is to propose an educational hands-on testbed using inexpensive systems composed of a Lego Mindstorms NXT robot and a webcam and easy-to-deal-with tools especially for learning and testing guidance, navigation, and control as well as search and obstacle mapping, however the extendibility and applicability of the proposed approach is not limited to only the educational purpose. In order to provide navigation information of the Lego robot in an indoor environment, an vision navigation system is proposed based on a colour marker detection robust to brightness change and an Extended Kalman filter. Furthermore, a spiral-like search, a command-to-line-of-sight guidance, a motor control, and two-dimensional Splinegon approximation are applied to sensing and mapping of a complex-shaped obstacle.
    [Show full text]
  • Experiences with the LEGO Mindstorms Throughout The
    Session T2F Experiences with the LEGO MindstormsTM throughout the Undergraduate Computer Science Curriculum Daniel C. Cliburn Assistant Professor of Computer Science Hanover College Hanover, Indiana 47243 [email protected] Abstract - The LEGO Mindstorms Robotics Invention the Mindstorms sections were actually less likely to declare a Systems are contemporary learning tools that have been major in computer science than those in the traditional used in a number of courses throughout the sections. The author of this paper has used the Mindstorms in undergraduate Computer Science curriculum. However, several undergraduate computer science courses with a mix of with a few exceptions, very little information is available success and failure. The purpose of this paper is to provide describing the degree to which Mindstorms projects what the author feels is missing from the literature: an improved (or hindered) the learning process in these informal discussion of the types of projects and courses for courses. This paper describes personal experiences and which the Mindstorms can be effective teaching tools, and provides practical advice for successfully incorporating those courses in which the Mindstorms should not be used. Mindstorms projects into an undergraduate computer The next section of this paper describes the efforts of science course, as well as highlighting the types of other instructors who have incorporated the Mindstorms into assignments that have not worked well. Specifically, the their courses. Following this discussion, the paper will author has incorporated Mindstorms projects into five describe the author’s own experiences with the Mindstorms in courses: Fundamental Concepts of Computer Science (a a number of classroom settings, and comment on their non-major Computer Science course), Programming I, usefulness as teaching tools in these courses.
    [Show full text]
  • Environment Mapping Using the Lego Mindstorms NXT and Lejos NXJ
    Environment Mapping using the Lego Mindstorms NXT and leJOS NXJ Gerardo Oliveira 1, Ricardo Silva 1, Tiago Lira 1, Luis Paulo Reis 1,2 1 FEUP – Faculdade de Engenharia da Universidade do Porto, Portugal 2 LIACC – Laboratório de Inteligência Artificial e Ciência de Computadores da Universidade do Porto, Portugal Rua Dr. Roberto Frias, s/n 4200-465 Porto, Portugal {ei04106, ei03087, ei04085, lpreis}@fe.up.pt Abstract. This paper presents a project of simultaneous localization and mapping (SLAM) of an indoor environment focusing on the use of autonomous mobile robotics. The developed work was implemented using the Lego Mindstorms NXT platform and leJOS NXJ as the programming language. The NXJ consists of an open source project which uses a tiny Java Virtual Machine (JVM) and provides a very powerful API as well the necessary tools to upload code to the NXT brick. In addition, the leJOS NXJ enables efficient programming and easiness of communication due to its support of Bluetooth technology. Thus, exploiting the mobile robotics platform flexibility plus the programming language potential, our goals were successfully achieved by implementing a simple subsumption architecture and using a trigonometry based approach, in order to obtain a mapped representation of the robot's environment. Keywords: Lego Mindstorms, NXT, leJOS, NXJ, Java, Robotics, Mapping, Subsumption Architecture, Behavior-Based, Client-Server. 1 Introduction One of the major problems in the field of robotics is known as SLAM (Simultaneous Localization and Mapping) [3], consisting of a technique used by robots and autonomous vehicles to build up a map within an unknown environment while at the same time keeping track of their current position.
    [Show full text]
  • LEGO MINDSTORMS NXT Programming
    LEGO MINDSTORMS NXT Programming Bernhard Buchli Andreas Schranzhofer Bernhard Buchli, [email protected], ETZ G 75, +41 44 63 27038 Andreas Schranzhofer, [email protected], ETZ G 77, +41 44 63 20454 09/29/10 Andreas Schranzhofer / Bernhard Buchli 1 NXT Tutorial ± Outline . Software Installation . get the USB ± Stick Folder ¹PPSª . "Hello World!" on NXT . ¹Sensor and Motorª on NXT . References, Documents, Links 09/29/10 Andreas Schranzhofer / Bernhard Buchli 2 Software Installation . NXT connects via USB (or Bluetooth) to PC . Driver available at: http://mindstorms.lego.com/support/updates/ . Programming Environments . Mindstorms NXT Software . Robolab (LabVIEW) . Lejos, RobotC, BricxCC) . Default: BricxCC http://bricxcc.sourceforge.net/ 09/29/10 Andreas Schranzhofer / Bernhard Buchli 3 Starting BricxCC . Connect NXT to the PC . Turn on NXT . Pop-Up Dialog Properties: . port: USB . brick type: NXT . firmware: Standard 09/29/10 Andreas Schranzhofer / Bernhard Buchli 4 Hello World! . C-like programming language: NXC (Not Excactly C) . The obligatory "Hello Worldª: #include "NXCDefs.h" task main() { ClearScreen(); PlayTone(440, 200); TextOut(0, LCD_LINE3, "Hello World!"); Wait(1000); PlayTone(440, 200); Wait(200); } 09/29/10 Andreas Schranzhofer / Bernhard Buchli 5 Compile Upload 09/29/10 Andreas Schranzhofer / Bernhard Buchli 6 The LEGO NXT 09/29/10 Andreas Schranzhofer / Bernhard Buchli 7 Sensor and Motor #include "NXCDefs.h" #define SPEED 70 task main() { int touch; SetSensor(S1, SENSOR_TOUCH); while (true) { touch = SENSOR_1; if (touch == 1) { OnFwd(OUT_A, SPEED); } else { Off(OUT_A); } } } 09/29/10 Andreas Schranzhofer / Bernhard Buchli 8 Mindstorms NXT Software 09/29/10 Andreas Schranzhofer / Bernhard Buchli 9 Lego Digital Designer 09/29/10 Andreas Schranzhofer / Bernhard Buchli 10 Mike©s Lego Cad 09/29/10 Andreas Schranzhofer / Bernhard Buchli 11 References .
    [Show full text]
  • Lego Mindstorms: an Mbeddr Case Study
    Lego Mindstorms: an mbeddr Case Study Markus Voelter1 and Bernd Kolb2 1 independent/itemis 2 itemis Abstract. This case study looks at the first large-scale demo case built with mbeddr: a set of C extensions for programming Lego Mindstorms robots. We have developed several robots (and the respective software) based on a common set of C extensions. Since C-based Mindstorms pro- gramming is based on the OSEK operating system, this case study has relevance beyond Lego. Last Changed: Feb 1, 2012 1 Contacts for more Info The Lego Mindstorms case study has been developed by the mbeddr team, in par- ticular Bernd Kolb ([email protected]) and Markus Voelter ([email protected]). 2 What is the problem/domain your system addresses? Lego Mindstorms provides the ability to program lego models. The system comes with an ARM processor and a number of sensors and actuators to build various kinds of computer-controlled machines. The software can be written in various ways, from a visual programming language provided by Lego, a version of Java, as well as C. In this case study we use the C-based way of programming mindstorms. The C-based way of programming Mindstorms is based on the OSEK oper- ating system . OSEK is used a lot in automotive embedded software, which is why this case study is relevant beyond Lego. The OSEK implementation used for Mindstorms in Lejos OSEK . 3 Why was mbeddr/MPS chosen as the basis? mbeddr was used as the basis because this case study was explicitly intended to be an mbeddr demo.
    [Show full text]
  • Programming LEGO MINDSTORMS with Java Fast Track 407 Index 421 177 LEGO Java Fore.Qxd 4/2/02 5:01 PM Page Xix
    177_LEGO_Java_FM.qxd 4/3/02 1:09 PM Page i [email protected] With more than 1,500,000 copies of our MCSE, MCSD, CompTIA, and Cisco study guides in print, we continue to look for ways we can better serve the information needs of our readers. One way we do that is by listening. Readers like yourself have been telling us they want an Internet-based ser- vice that would extend and enhance the value of our books. Based on reader feedback and our own strategic plan, we have created a Web site that we hope will exceed your expectations. [email protected] is an interactive treasure trove of useful infor- mation focusing on our book topics and related technologies. The site offers the following features: I One-year warranty against content obsolescence due to vendor product upgrades. You can access online updates for any affected chapters. I “Ask the Author” customer query forms that enable you to post questions to our authors and editors. I Exclusive monthly mailings in which our experts provide answers to reader queries and clear explanations of complex material. I Regularly updated links to sites specially selected by our editors for readers desiring additional reliable information on key topics. Best of all, the book you’re now holding is your key to this amazing site. Just go to www.syngress.com/solutions, and keep this book handy when you register to verify your purchase. Thank you for giving us the opportunity to serve your needs. And be sure to let us know if there’s anything else we can do to help you get the maximum value from your investment.
    [Show full text]
  • LEGOLAND®, Playmobil- Funpark Y Europa-Park®
    · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ciones para familias serán Día 7° Europa-Park® elegir entre un “castillo” de Viajes en Familia abiertas dentro del parque Hoy continue disfrutando de la Edad Media, un “monaste- en el área del “Mundo de este apasionante parque . El rio” estilo portugués, el Hotel Precios por familia en € los faraones”, así que: más “Voletarium“, el cine en 4D y “Colosseo” y sentir la bella Temporada: 13/04/2019 - 27/10/2019 diversión que nunca! Aloja- las muchas otras estaciones Roma, el Hotel “Bell Rock” o el Salidas: A diario miento en Günzburg o en el temáticas interactivas son Hotel “El Andaluz” para sabore- Paquete base: con alojamiento en LEGOLAND® Feriendorf . igual de emocionantes . Y ar la elegancia mediterránea de Günzburg y Rust (fuera de los parques) quienes prefieran algo más una finca española. Lo más nue- 1 adulto 1 niño* 2 .415,- Día 5° Günzburg - Selva Negra tranquilo pueden sentarse al vo es el Hotel „Krønasår“, el „ho- 2 adultos 1 niño* 3 .025,- Dejamos atrás al mundo de volante de un automóvil histó- tel museo“, que abrirá sus puer- 2 adultos 2 niños* 3 .545,- LEGO® y continuamos nuestro rico y conducir apaciblemente . tas a finales de mayo del 2019. viaje hacia los bellos paisajes de También la gastronomía temá- Alojamiento en Rust o en el la Selva Negra . En nuestro cami- tica juega un papel importante Europa-Park® . Suplemento alojamiento en el no hacia Rust donde se ubica el a la hora de saborear el par- LEGOLAND® Feriendorf famoso Europa-Park® podemos que, así como el alojamiento Día 8° Selva Negra - Múnich temáticas Castillo/ hacer parada en la ciudad de en cualquiera de sus 6 hoteles .
    [Show full text]
  • Lejos NXJ Tutorial
    The leJOS NXJ Tutorial Introduction to leJOS and this Tutorial This tutorial will teach you how to use leJOS NXJ to run Java programs on your LEGO MINDSTORMS NXT ®. leJOS NXJ is a firmware replacement for the NXT and you will need to flash the leJOS firmware replacing the existing LEGO Mindstorms firmware. (Don’t worry you can put the original firmware back whenever you want). The tutorial gives step by step instructions for setting up the leJOS NXJ software up on your PC and replacing the firmware on the NXT. It covers all supported PC operating systems: Microsoft Windows, MAC OS X and Linux. The tutorial will teach you how to write Java programs that control a variety of different types of robots. You will learn how to program all the sensors and motors that come with the NXT. leJOS is not for beginners. You will need to understand at least the basics of the Java language to use it. However, it is much more powerful than the graphical programming environment that comes with the NXT: you will be able to program robots with more complex and interesting behaviors. leJOS NXJ gives you all the power of the Java language. You can you a variety of data types including strings, floating point number and arrays, including multi-dimensional arrays. You can do complex mathematical calculations using a all the standard Java math functions. You can program concurrent threads, and you can use Java events. Java is an Object Oriented environment that lets you define your own classes and objects.
    [Show full text]
  • Brick Sorting Revisited*
    Session 1726 Brick Sorting Revisited* M. L. Neilsen Department of Computing and Information Sciences 234 Nichols Hall Kansas State University [email protected] Abstract With the rapidly advancing capabilities of computing hardware, it is now possible to embed computing capabilities in virtually all manufactured devices. Consequently, there is an increased demand for professionals trained to develop embedded electronic systems. However, the design and implementation of such systems requires a broad knowledge in areas traditionally not covered in any one discipline. This paper discusses how a conceptually simple brick sorting problem can be used to solidify and unify some of the theoretical concepts covered in a traditional real-time systems course. The problem is to sort Lego™ Bricks of different colors into separate bins using a standard set of sensors and actuators controlled using Lego Mindstorms™ Robotic Command Explorer (RCX) bricks. Although conceptually simple, the problem enables students to connect abstract design concepts with a concrete implementation and to better understand the importance of using an iterative design methodology. * In part, this material is based upon work supported by the National Science Foundation under NSF Grants No. 9980321 and 0227709. Any opinions, findings, and conclusions or recommendations expressed in this material are 10.270.1 Page those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education 1 Introduction The number of embedded electronic systems used in automobiles, industrial automation, and other control systems continues to increase dramatically.
    [Show full text]
  • Parent/Teacher Handbook and Trouble-Shooting Guide
    Lego Rovers: Parent/Teacher Handbook and Trouble-shooting Guide Louise A. Dennis July 15, 2019 ii Contents 1 Introduction 1 2 Installation and Set Up 3 2.1 How to get an SD Card with leJOS . 4 2.1.1 What You Need . 4 2.1.2 Formatting the SD Card . 4 2.1.3 Installing leJOS on the SD card . 4 2.2 Installing the SD card on your EV3 Brick . 5 2.3 Naming your EV3 robot (Optional) . 5 2.4 Installing and Configuring Lego Rovers on your Android tablet 7 2.4.1 Android Requirements . 7 2.4.2 Before Installation . 8 2.4.3 Installation . 8 2.4.4 Setting up Communication for your Android Device and the LEGO Rover . 9 2.5 Building the robot . 12 2.6 Calibrating the Sensors . 12 2.6.1 Calibrating the Obstacle Belief . 12 2.6.2 Calibrating the Path Belief . 12 2.6.3 Calibrating the Water Belief . 13 2.7 Starting the Activity . 14 2.8 Shutting Down . 16 3 Lesson Plans 17 3.1 Key Stage 2 Lesson Plan . 17 3.2 Key Stage 3 Lesson Plan . 19 iii iv CONTENTS 4 Sample Answers and Explanations 23 4.1 Connection . 23 4.2 Teleoperation . 24 4.3 Time Delay . 24 4.4 Sensors . 26 4.5 Rules . 26 4.5.1 Rule Triggers . 27 4.5.2 Rule Actions . 28 4.6 Tasks . 28 4.6.1 Avoiding Obstacles . 28 4.6.2 Following Lines . 29 4.6.3 Finding Water . 29 4.7 Final Challenges .
    [Show full text]
  • COMP329 Intro to Lejos
    COMP329 Robotics and Autonomous Systems Lecture 4: LeJOS and EV3 Dr Terry R. Payne Department of Computer Science Today’s Aims •Before the labs start, we will take a look at the basics of programming our EV3 robots. • There will be three elements: • Programming robots in the abstract. • The EV3 Environment • The LeJOS API •The EV3 is the robot we will use for the labs and the projects. • LeJOS is the language we will use to program the EV3. •The COMP329 RoboSim environment • Provides prototyping environment when Robotics Lab is closed 2 Original Source: M. Wooldridge, S.Parsons, D.Grossi - updated by Terry Payne, Autumn 2016/17 Basic Control Loop while(true){ read sensors update internal datastructures percepts actions make decisions set power on motors } see action Agent • Even robots with arms and legs move them by next state turning motors on and off. 3 Original Source: M. Wooldridge, S.Parsons, D.Grossi - updated by Terry Payne, Autumn 2016/17 Basic Control Loop •We'll get into more detail about how while(true){ read sensors exactly sensors work later in the update internal datastructures course. make decisions set power on motors • For now, just think of them as generating a value. } •We won't get into more detail about how motors work. • Just think of a motor command as setting the power on the motors. • Setting the voltage on a particular port. 4 Original Source: M. Wooldridge, S.Parsons, D.Grossi - updated by Terry Payne, Autumn 2016/17 Remember Timescales while(true){ Even slow processors work faster switch motors on • : do stuff than the robot.
    [Show full text]