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

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Drive Controller

Sensor Handler

Collision detector

Corridor Recognizer

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Sensor Handler

Driver Driver Driver

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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

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Sensor Handler

Collision detector(Fuzzy)

Corridor Recognize

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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