Construction and Motion control of a mobile robot using Visual Roadmap
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Transcript of Construction and Motion control of a mobile robot using Visual Roadmap
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Construction and Motion control of a mobile
robot using Visual Roadmap
By: Harshad SawhneyGuide: Dr. Amitabha Mukerjee
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Objective
Source
Destination
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Introduction
• Inspiration From Human Brain.• The roadmap approach, captures connectivity
of robot’s free space.• 3-DOF mobile robot constructed.
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Construction Of Robot
• Major Components:
Microcontroller Arduino
Wireless module Xbee
Motor driver2 DC motors
Lithium-Ion battery
Image Source: robokits.co.in
Receives Data
UART communication
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Camera Input
Image Processing
Wireless data transfer
Microcontroller receives command
µC sends output
Robot Advances
Destination Reached
Flow Chart of Robot Navigation
NO
Stop
YES
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Image Pre-processing
• 10k images taken.• Background subtraction performed.• Parameters extracted - robot navigation.
Few images from dataset
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Initial Image Background subtraction
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Distance Metric Computation
• L2-norm expansion method.• Dist(X, Y) = sqrt(||X||2 + ||Y||2 - 2*||Y’X||)
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Graph generation
• k-nearest neighbours calculated.• Robot location as nodes.• k=6 taken.• k=10 ; robot jumps larger distance.
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Nearest nodes to some vertices
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Shortest path calculation1. Without Obstacles: – Dijkstra’s algorithm used.
Shortest Path Graph
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Shortest path calculation
1. With obstacles:– Obstacles image extracted.– Compared the image with the dataset.– Nodes and edges removed.– Reduced to no obstacles case.
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Obstacle Image
Image of environment with obstacles Obstacle image extraction
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Shortest path calculation
Shortest Path Graph with obstacles in the environment
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Robot Motion Control
• Feed the nodes.• Camera: Negative closed loop feedback
mechanism.• Reach till destination.• Real Time.
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Algorithm for controlling robot
• (x, y, Ɵ): Robot current parameters• (x’, y’, Ɵ’): Node parameters• Ɵ : Robot vector angle.• Ɵ1 : Position of robot and node vector angle.
• Step1: Ƒ = | Ɵ - Ɵ1 |• Rotate till | Ƒ | < ɛ• Step 2: Move till | (x-x’)2+ (y-y’)2|< ɛ1
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Algorithm for controlling robot
• Step 3: Align till | Ɵ - Ɵ’| < ɛ2
• Steps executed in increasing order of priority.• Thus, the camera provides negative feedback
closed loop system.
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Results
• Robot accurately navigates.• Videos demonstrating robot navigation.
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Challenges
• Distance metric computation: limits sampling density.
• Real time motion: possibly leading to collisions.
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Future Work
• Dynamic obstacle avoidance• Update graph first time; use relative changes
in image for future considerations.
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References[1] Amitabha Mukerjee, M Seetha Ramaiah, Sadbodh Sharma, Arindam Chakraborty, “The Baby at One Month: Visuo-motor discovery in the infant robot".[2] Joshua B. Tenenbaum, Vin de Silva, John C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction", 2000.[3] Jean-Claude Latombe, "Robot Motion Planning”, Edition en anglais. Springer, 1990.[4] Choset, Howie, Principles of robot motion: theory, algorithms, and implementations. MIT press, 2005.[5] Seth Hutchinson, Gregory D Hager, and Peter I Corke. A tutorial on visual servo control. Robotics and Automation, IEEE Transactions on, 12(5):651{670, 1996.