CEG 4392 : Maze Solving Robot Presented by: Dominic Bergeron George Daoud Bruno Daoust Erick Duschesneau Bruno Daoust Erick Duschesneau Martin Hurtubise.

Slides:



Advertisements
Similar presentations
EducateNXT The Corridor Challenge The Corridor Challenge requires programming of a robot to negotiate obstacles and the corridor walls in order to reach.
Advertisements

Unmanned Maze Solver Using aerial image processing and wireless connectivity to direct a robot through a maze. Nicholas Hoover Matthew Mitrik Edward Waxler.
The Bioloid Robot Project Presenters: Michael Gouzenfeld Alexey Serafimov Supervisor: Ido Cohen Winter Department of Electrical Engineering.
LSV2 Autonomous Chargers Design Team: (from the left) Branden Carpenter Wayne Romine Jon Stoker Dr. Hess (Advisor) Maggie Richardson.
1 Electrical and Computer Engineering Guitar Virtuos Justin D’Alessandro (EE) Jacob Dionne (CSE) Adam Montalbano (CSE) Jeffrey Newton (EE) Team Kelly Final.
Team GPS Rover Alex Waskiewicz Andrew Bousky Baird McKevitt Dan Regelson Zach Hornback.
M & M EE 296 Final Presentation Spring 2004 Presentation Overview Team Member Introduction Project Overview Overall Design Description Final Project.
PT 5000 Pooja Rao Ted Tomporowski December 7, 2004.
Efficient Path Determining Robot RIT Computer Engineering Senior Design Project Jamie Greenberg Jason Torre October 26, 2004 A motorized robot will navigate.
DO NOT FEED THE ROBOT. The Autonomous Interactive Multimedia Droid (GuideBot) Bradley University Department of Electrical and Computer Engineering EE-452.
Wireless Data Acquisition for SAE Car Project by: J.P. Haberkorn & Jon Trainor Advised by: Mr. Steven Gutschlag.
Tracking Rover Team Rubber Ducky Alex Chi Joshua Rubin Alexander Starick Ryan Ramos.
LEGO Mindstorms Hitachi H8-based RCX brick B.A. Juliano, R.S. Renner, F. Jauregui January 2004 California State University, Chico Intelligent Systems Laboratory.
Patent Liability Analysis Andrew Loveless. Potential Patent Infringement Autonomous obstacle avoidance 7,587,260 – Autonomous navigation system and method.
Deon Blaauw Modular Robot Design University of Stellenbosch Department of Electric and Electronic Engineering.
Chuang-Hue Moh Spring Embodied Intelligence: Final Project.
Autonomous Control of Scalextric Slot Car on User-Defined Track Siddharth Kamath Souma Mondal Dhaval Patel School of Electrical and Computer Engineering.
BLAKE DIDIER LESSAGE GABRIEL G.U.N.D.A.M.. What is it? A robot whose primary function is solving mazes of varying types while transmitting the layout.
June 12, 2001 Jeong-Su Han An Autonomous Vehicle for People with Motor Disabilities by G. Bourhis, O.Horn, O.Habert and A. Pruski Paper Review.
Computerized Train Control System by: Shawn Lord Christian Thompson.
ROBOT LOCALISATION & MAPPING: MAPPING & LIDAR By James Mead.
IMPLEMENTATION ISSUES REGARDING A 3D ROBOT – BASED LASER SCANNING SYSTEM Theodor Borangiu, Anamaria Dogar, Alexandru Dumitrache University Politehnica.
DAN ISASTERREAETWORK. DAN Goal: Goal: To be able to monitor, track the progress and guide Aid workers and casualties in a Disaster Area site. To be able.
Patent Liability Analysis March 28,  Project Overview  Product Infringements  Patent Infringements.
Anees Elhammali Michael Malluck John Parsons Namrata Sopory
Ruslan Masinjila Aida Militaru.  Nature of the Problem  Our Solution: The Roaming Security Robot  Functionalities  General System View  System Design.
© Janice Regan, CMPT 300, May CMPT 300 Introduction to Operating Systems Principles of I/0 hardware.
Autonomous Robot Project Lauren Mitchell Ashley Francis.
LITERATURE SURVEY 1.EFFICIENT PATH PLANNING IN SEMI- FAULT TOLERANT ROBOTICS 2. Caesar robot.
Outline Overview Video Format Conversion Connection with An authentication Streaming media Transferring media.
1 Advanced topics in OpenCIM 1.CIM: The need and the solution.CIM: The need and the solution. 2.Architecture overview.Architecture overview. 3.How Open.
CS 8903 Demo Wireless Interface for the Bioloid Robot Chetna Kaur.
Final Presentation.  Software / hardware combination  Implement Microsoft Robotics Studio  Lego NXT Platform  Flexible Platform.
ROBOT NAVIGATION By: Sitapa Rujikietgumjorn Harika Tandra Neeharika Jarajapu.
Phong Le (EE) Josh Haley (CPE) Brandon Reeves (EE) Jerard Jose (EE)
Wandering Standpoint Algorithm. Wandering Standpoint Algorithm for local path planning Description: –Local path planning algorithm. Required: –Local distance.
See3PO - A Visually Capable Path Finding Robot See3PO Frank Marino, Nick Wang, Jacky Yu, Hao Wu and Debarati Basu Department of Computer Science University.
ABSTRACT Currently, drivers must utilize a third-party, such as a radio or broadband device, to learn about local traffic conditions. However, this information.
Recharging Process 1.Robot detects low battery 2.Robot requests a bay from the charging station over wireless 3.Charging station accepts or denies the.
Joe Cohen Presentation Overview  Project definition and requirements  Solution process and explanation  Methodology.
Team 6 DOODLE DRIVE Presenter: Jun Pan. PROJECT OVERVIEW  Android application as controller  Robot vehicle with microcontroller  Path will be drawn.
Jason Holmes Matt Wickesberg Michael Piercy Matt Guenette Team 12 – Super Tank February 15, 2012.
August 2003 At A Glance The IRC is a platform independent, extensible, and adaptive framework that provides robust, interactive, and distributed control.
DO NOT FEED THE ROBOT. The Autonomous Interactive Multimedia Droid (GuideBot) Bradley University Department of Electrical and Computer Engineering EE-452.
The George Washington University Department of ECE ECE Intro: Electrical & Computer Engineering Dr. S. Ahmadi Class 4/Lab3.
The George Washington University Electrical & Computer Engineering Department ECE 002 Dr. S. Ahmadi Class3/Lab 2.
Nir Mendel, Yuval Pick & Ilya Roginsky Advisor: Prof. Ronen Brafman
Developing a Low-Cost Robot Colony Association for the Advancement of Artificial Intelligence November 10, 2007 James Kong Felix Duvallet Austin Buchan.
+ Routing Concepts 1 st semester Objectives  Describe the primary functions and features of a router.  Explain how routers use information.
See3PO - A Visually Capable Path Finding Robot See3PO Frank Marino, Nick Wang, Jacky Yu, Hao Wu and Debarati Basu Department of Computer Science University.
Maze Twinbots Group 28 Uyen Nguyen – EE Ly Nguyen – EE Luke Ireland - EE.
1 Programming of FPGA in LiCAS ADC for Continuous Data Readout Week 6 Report Wednesday 6 th August 2008 Jack Hickish.
Final Design Review By: Alireza Veiseh Anh-Thu Thai Luai Abou-Emara Peter Tsang.
Auto-Park for Social Robots By Team I. Meet the Team Alessandro Pinto ▫ UTRC, Sponsor Dorothy Kirlew ▫ Scrum Master, Software Mohak Bhardwaj ▫ Vision.
The Corridor Challenge
VEX IQ Curriculum It’s Your Future Lesson 11 Lesson Materials:
VEX IQ Curriculum Smart Machines Lesson 09 Lesson Materials:
COGNITIVE APPROACH TO ROBOT SPATIAL MAPPING
SCADA for Remote Industrial Plant
Smart Car Robot Prepared by Supervised by Mai Asem Abushamma
Navigation Life in the Atacama 2005 Science & Technology Workshop January 6-7, 2005 Dominic Jonak Carnegie Mellon.
Electrical Engineer Responsibilities
Multiple Robot navigation and Mapping for Combat environment
Sasha Popov November 16, 2018 iRobot Create.
Presented by Angel Nunez IDML Spring 2008 Dr. Arroyo Dr. Schwartz
ECE 477 Design Review Group 10  Spring 2005 I, Robotic Waitress
Wireless Autonomous Trolley
9/28/18 – Earthquake and Tsunami hit Indonesia
Wireless Autonomous Trolley
Presentation transcript:

CEG 4392 : Maze Solving Robot Presented by: Dominic Bergeron George Daoud Bruno Daoust Erick Duschesneau Bruno Daoust Erick Duschesneau Martin Hurtubise Mathieu Mallet

Presentation Overview

► Product Overview ► Features ► Specifications ► YARE Demo ► Where to buy

Product Overview

► YARE is the name of our maze solving robot. The four letter name is an acronym for “Yare Automaton for Revealing Exits”. ► YARE provides the greatest degree of autonomous behaviour and functionality achieved by our engineering team to navigate through a maze and locate nearest exit.

Product Overview ► In autonomous mode, YARE follows the right wall of the maze and finds its way through the nearest accessible exit. ► Using a PC, the client software and the wireless network interface, YARE acknowledges manual commands and is able to go through the maze using the shortest possible path.

Features Robot

Features: Robot ► Three-way wall detection ► Distance compensation ► Collision detection/avoidance ► Data acquisition and analysis system ► Wireless communication ► Autonomous navigation mode ► Slave navigation mode

Three-way wall detection ► YARE detects nearby walls using infrared (IR) sensors. ► YARE contains three sets of IR sensors positioned at the front, left and right of the robot. ► Each of these sensors is used to measure the distance between the robot and the closest wall.

Distance compensation ► The right IR sensor is used to monitor distance between the robot and the right wall. ► To prevent the robot from deviating from its current path, a distance compensation algorithm was implemented to keep the robot as parallel as possible to the right wall and at a constant distance.

Collision detection/avoidance ► The front sensor is used to monitor the distance between the robot and an incoming obstacle (or front wall). ► It can also be used to avoid front collisions. ► If avoidance is not possible because the obstacle is not detected, the frontal collision will trigger the bumper switch and the robot will stop and go into slave mode, awaiting commands.

Data acquisition and analysis system ► Data is acquired internally (time, wheel speed, wheel direction) and externally (IR sensors, bumper switch). ► Only the data acquired from external sensors is analysed by the robot. ► This data is crucial when the robot is in autonomous navigation mode.

Wireless communication ► The data acquired is transformed into several 10-bit RS232 serial packets and sent to the base station. ► To maintain synchronization, the packet is synchronized at a serial-compatible bit rate. ► In addition to the serial start and stop bits, an extra information bit is present in each packet to efficiently identify the start byte. ► The data and its checksum are sent three times.

Autonomous navigation mode ► In this mode, YARE analyses data from the right and front IR and follows the right wall while avoiding collisions. ► If a possible path is discovered at the right side of the robot, YARE will turn 90 degrees right and follow that path. ► If a front wall is detected and no right path is discovered, YARE will turn 90 degrees left and follow that path.

Slave navigation mode ► In slave mode, YARE awaits for instructions:  “follow right wall”  “clear”  “turn 90 degrees to the right”  “find right wall”  “turn 90 degrees to the left”  “follow left wall”  “find left wall”  “find front wall”  “switch to autonomous mode”

Features Client software

Features: Client software ► Cross-platform compatible ► Robot progress displayed in real-time ► Ability to send manual commands to robot ► Ability to load/save maze data ► Ability to transform IR data samples into a straight wall ► Implementation of A* shortest path algorithm ► Communicates path to the robot

Cross-platform compatible ► The JAVA language was chosen to create YARE’s client software. ► Cross-platform compatibility is achieved using JAVA to provide flexibility and meet user demands.

Robot progress displayed in real-time ► The interface allows real-time display of the robot’s progress. ► Wall information captured from IR sensors and the robot’s current path are displayed on screen. ► The GUI interface allows the user to choose which information (sensor and/or path) will be displayed.

Ability to send manual commands to robot ► Through the GUI interface, the user can conveniently send manual commands to YARE, when he awaits in slave mode. ► The robot can be manually re-set to autonomous mode through the same interface.

Ability to load/save maze data ► Maze information captured during a session can be saved into a file. ► The maze data file can be re-loaded at any time and allow YARE to receive shortest path instructions for any recorded maze.

Ability to transform IR data samples into a straight wall ► To analyse the maze data, the client software transforms IR data samples into straight line by means of an interpolation. ► These straight lines are used in the A* shortest path algorithm.

Implementation of A* shortest path algorithm ► The A* algorithm can be explained as follows:  The start point is the robot’s starting point. This point becomes the first element of the closed list.  Then, all reachable points from the last element of the closed list (in the first case the starting point) are found and added to the open list.  The point with the minimum F value is found from the open list and added to the closed list.  The process is repeated until the end point is reached.  Some backtracking is done to find the optimal path.

Implementation of A* shortest path algorithm ► The A* algorithm can be explained as follows:  The start point is the robot’s starting point. This point becomes the first element of the closed list.  Then, all reachable points from the last element of the closed list (in the first case the starting point) are found and added to the open list.  The point with the minimum F value is found from the open list and added to the closed list.  The process is repeated until the end point is reached.  Some backtracking is done to find the optimal path.

Implementation of A* shortest path algorithm ► The A* algorithm can be explained as follows:  The start point is the robot’s starting point. This point becomes the first element of the closed list.  Then, all reachable points from the last element of the closed list (in the first case the starting point) are found and added to the open list.  The point with the minimum F value is found from the open list and added to the closed list.  The process is repeated until the end point is reached.  Some backtracking is done to find the optimal path.

Communicates path to the robot ► Manual commands and shortest path instructions are sent directly to YARE wirelessly. ► The shortest path found using our A* algorithm implementation is first transformed into a series of manual instructions.

Communicates path to the robot ► These commands are assembled and sent into several 10-bit RS232 serial packets to the robot. ► Synchronization is set at a serial-compatible bit-rate and each packet contains an information bit needed to identify the start byte. ► Since these instructions are most crucial, the data and its checksum is sent five times.

Specifications

YARE Demo

For those interested, a live demonstration of YARE’s capabilities will be shown at the University of Ottawa. ► When?December 1 st 2003, 7:10pm. ► Where? SITE 2061

Where to buy

► This product is currently not for sale or rent. ► Contact the University of Ottawa, School of Information Technology and Engineering for more information.