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MotivationMotivation
• A visually impaired student on a powered wheelchair
• Increasing needs of Assistive Technology (intelligent wheelchair)
• Recent advancement of Robot Technology
• Prototype (small scale) of autonomous robot navigation
Problem StatementProblem Statement
• Corridor recognition (machine vision)• Collision avoidance (fuzzy logic control)
2. Robot system design (reusability, modularity)
Multi-platform component (Java, layered architecture)Easy increment of another agent with minimal developmental cost (multi-agent w/ BB)Quick development of a prototype system (ER-1: a commercial robot kit)
1. Robot navigation (hallway, unstructured)
ApproachApproach
• Behavior-based approach• Complete agents
2. Layered Architecture
Hardware Layer – C++ (ER1 SDK)Component Layer – JAVA
1. Incremental Design
Write once, Run anywhere
3. Platform Independence
Hardware Layer
Component Layer
AGENT
AGENT
AGENT
AGENT
HardwareHardware
• Chassis• Wheels• Motors• Power module• Battery
2. Sensors Camera (x1)Infrared Sensors (x9)
1. ER 1 Personal Robot System
3. Laptop Computer Windows XPUSB ports
Camera
Infrared
Front view
Side view
Rear view
SoftwareSoftware
• Blackboard as a medium
• Decentralization• Independent Agent• Distributed intelligence
2. Agents Sensor HandlerDrive ControllerFuzzy Collision DetectorCorridor Recognizer
1. Multi-Agent Architecture
Bla
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oar
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Drive Controller
Sensor Handler
Collision detector
Corridor Recognizer
En
viro
nm
ent
Sensor Handler
Driver Driver Driver
?
Camera IRs
Corridor Recognition Corridor Recognition AgentAgent• Gaussian smoothing filter• Sobel edge detector• Adaptive thresholding• Thinning operator
2. Feature Extraction and RecognitionHough transform
Histogram-based intensity analysis
1. Image Segmentation
Grayscale160x120 RGB Gaussian filter
ThresholdingThinning Sobel detector
Final Result
Corridor: YES
Wall: NO
Obstacle: NO
Collision Avoidance Collision Avoidance AgentAgent1. Input fuzzification2. Rule matching3. Defuzzification
2. AdvantagesDealing with uncertainty Fast and non-linear computationRobust and adaptiveEasy to modify
1. Fuzzy Logic Left sensor = 255
Left sensor input is large
IF left sensor input is large THEN right-turn angle is large.
Right-turn angle is large.
Turn-angle = -30˚
Linguistic Variable Inputs
Crisp Sensor Input
Fuzzy Inference
Linguistic Variable Outputs
Crisp Navigation Parameter Outputs
FUZZIFICATION
DEFUZZIFICATIONB
lack
bo
ard
Drive Controller
Sensor Handler
Collision detector(Fuzzy)
Corridor Recognize
r En
viro
nm
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Experiment ExamplesExperiment Examples
• Corridor Recognition
Only with Collision Avoidance
Obstacle Avoidance Behavior
Door Navigation Behavior
Results I : Robot Results I : Robot PerformancePerformance1. Corridor
RecognitionSuccessful identification of corridorsSuccess rate drops in identifying walls and obstacles
2. Fuzzy-based Collision DetectionRetardation caused by ambient
lightAdvisability of fuzzy rules
3. Control MechanismProblems found in knowledge synchronizationIn need of handling false claims
• Feasibility in applying a multi-agent system for robot control
• Platform independence realized by employing a layered architecture and Java technology
• Corridor recognition using Machine Vision techniques proven to be effective
• Safe navigation with fuzzy logic collision detection
• Problems found in navigation
SummarySummary
Future WorkFuture Work• Implementing a module for managing information on
the blackboard
• An agent for scheduling tasks resolving conflicts
• Vision-based landmark recognition
• An agent with a neuro-fuzzy controller for learning an environment so that no manual calibration is necessary
Y. Ono, H. Uchiyama, and W. Potter
Artificial Intelligence CenterThe University of Georgia
SEACM, April, 2004
A Mobile Robot For Corridor Navigation: A Mobile Robot For Corridor Navigation: Multi-Agent ApproachMulti-Agent Approach