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by
Mohammad
Najib
Bin Abdul
Rahim
, 8649
MA
Y 2010
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1.0 Company Background
2.0 Project Background
3.0 Literature Review
4.0 Methodology5.0 Result and Discussion (up-to-date)
6.0 Future Works
7.0 Additional Work & Activities
8.0 Lessons Learned
9.0 Conclusion & Summary
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LE2I, Laboratoire Electronique, Informatique et Image
oAssoc: CNRS,
IUT Le Creusot (host), UB
o3 Research Teams:
Informatique, Signal et
Imagerie medicale, Image
& Vision
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2.1 Problem Statement
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The purpose of this project is to develop an autonomous navigation system for a
mobile robot to assist mobility of the physically disabled in a known environment such as
in a building.
For those with physical disabilities, personal mobility is a challenge for them
especially when moving around in a new environment (i.e building). However, the need is
inevitable in places like hospital and government agencies.
By having a mobile robot equipped with autonomous navigation system, it
provides mobility assistance for the disabled to reach specific destination in a building
from the computation of path planning and motion control algorithms. The embedded
artificial intelligence enables the mobile robot to function as a movingGPS to usher
direction to required destination.
2.1 Problem Statement
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2.3 Scope
Develop Graphical User Interface (GUI) software
Implement global path planning algorithms
Implement motion control algorithm
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1) Stephen J. Tobias and A. Antonio Arroyo, 2000. Autonomous Pathfinding,
University of Florida, USA, 2000 Florida Conference on Recent Advances in Robotics
This paper discusses pathfinding approach using sensors for the robot to learn and
adapt to the environment at a fraction of computing power
Advantages: Low-cost sensors, requires no global
positioning information (except at start & end)
Disadvantages: Takes time, accumulated errors, not
portable information (parameters in algorithm)
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Mohd Yamani Idna Idris, Norzaily Mohamed Nor, Zaidi Razak, Md Nor Ridzuan Daud,
ParkingSystem Using Chain Code & A Star Algorithm, Malaysia, Universiti Malaya.
This paper takes the reader to another application of image processing, chain code
& A star implementations
System Overview: Image recognition detects empty
space comparing histogram of floor and object,
character on the floor is recognized using Chain Code,
then, A star generates the shortest route
Results: Preliminary research has shown the
implementation of image processing & robotics
navigation
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-Progress until 10 May 2010-
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General Features:
Service environment: Known environment (i.e In a building)
General operation: Upload map -> Set start point -> Set destination -> Run
Programming Language: Visual C++
Input devices:Collision protection sensor
9 infrared distance sensors
Analogue inductive sensor
2 optical sensors
Colour webcam with USB interface
3 wheel encoders
5.1 Product Design Specification (PDS)
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Robotino
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5.2 System Breakdown
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5.2.1 Graphical User Interface (GUI)
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Allows robot to move from Point A to Point B
Compute the shortest route
Development phase:
5.2.2 Path Planning Algorithm
Basic algorithm:
Adaptive algorithm:
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(a) Wavefront algorithm:
1. Check node-by-node
2. Start from top left3. Ignore walls & count up with
respect to the minimum
bordering node
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check node A at [0][0]
now look north, south, east, and west of this node (boundary nodes)
if(boundary node is a wall) ignore this node, go to next node B
else if(boundary node is robot location && has a number in it) path
found!
else if(boundary node has a goal) mark node A with the number 3
else if(boundary node is marked with a number) find the boundary
node with the smallest number mark node A with
(smallest number + 1)
if(no path found) go to next node B at [0][1] (sort through entire
matrix in order)
if(no path still found after full scan) go to node A at [0][0] (start over,
but do not clear map) (sort through entire matrix in
order) repeat until path found
if(no path still found && matrix is full) this means there is no solutionclear entire matrix of obstacles and start over this
accounts for moving objects! adaptivity!
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(b) A* (A-star) algorithm:F = G + H
G = the movement cost (A to current point)
H = the estimated movement cost (current
point to B)
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Simple Test:
http://d/MECHANICAL%20ENGINEERING/Internship/Internship%20Visit/Presentation/AstarRobotino.exe8/10/2019 UTP-LE2I_Mobile Robot Navigation System
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6.1 A* (continuation)
Integration with Robotino API
Implementation and testing
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6.2 Motion Control AlgorithmPID Control for Driving Straight
Each motor individual motor has its own PID controller, however due to
its differential drive configuration, synchronization of the motor speeds is
required.
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6.3 RecommendationTrajectory smoothing
Localization
Motion control feedback:
ousing gyro sensor & compass
odummy wheels attached with encoders
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(a) Additional Work:IR Tracking Program
Multi-threading Program
(b) Other Activities:I.U.T Open Day
Attended Computer Vision class
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(a) Technical:Programming Skill: Visual C++ (MFCMicrosoft Foundation Class)
Robotics: Path planning, motion control
Multi-disciplinary skill set: Computer Science + Mechanical
(b) Non-technical:Personal development: goal-oriented, self-motivation
Research environment
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Work-In-Progress:
o 1 solution implemented
o 1 solution to be implemented
o Refinement required
Objective - check
Gantt Chart - check
9.1 Conclusion
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Introduction: Company & Project
Work-In-Progress:
o Literature Review
o Methodology
o Results & Discussion
Future Works
Additional Work & Activities
Lessons Learned
Conclusion
9.2 Summary
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INTERNSHIP GANTT CHART
Action Plan January February March April May June
Definition of the Problem
Literature Reviews
Research for the Solution
Solution Development
Testing & Refinement