Download Corridor navigation of a mobile robot using a camera and sensors

A Mobile Robot For Corridor Navigation:
Multi-Agent Approach
Y. Ono, H. Uchiyama, and W. Potter
Artificial Intelligence Center
The University of Georgia
SEACM, April, 2004
Presentation Outline:
Motivation and Problem Statement
Approach
Design and Implementation
Experimental Results
Summary
Future Work
Motivation
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 Statement
1. Robot navigation (hallway, unstructured)
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)
Approach
1. Incremental Design
Behavior-based approach
Complete agents
2. Layered Architecture
Hardware Layer – C++ (ER1 SDK)
Component Layer – JAVA
3. Platform Independence
Write once, Run anywhere
Hardware
Layer
Component
Layer
AGENT
AGENT
AGENT
AGENT
Hardware
1. ER 1 Personal Robot System
Chassis
Wheels
Motors
Power module
Battery
Camera
Front view
2. Sensors
Camera (x1)
Infrared Sensors (x9)
Infrared
Side view
3. Laptop Computer
Windows XP
USB ports
Rear view
Software
2. Agents
Sensor Handler
Drive Controller
Fuzzy Collision Detector
Corridor Recognizer
Environment
Blackboard as a medium
Decentralization
Independent Agent
Distributed intelligence
Blackboard
1. Multi-Agent Architecture
Sensor
Handler
Collision
detector
Corridor
Recognizer
Drive
Controller
Camera
IRs
?
Driver
Driver
Sensor Handler
Driver
Corridor Recognition Agent
1. Image Segmentation
Gaussian smoothing filter
Sobel edge detector
Adaptive thresholding
Thinning operator
160x120 RGB
Grayscale
Gaussian filter
Thinning
Thresholding
Sobel detector
2. Feature Extraction and Recognition
Hough transform
Histogram-based intensity analysis
Final Result
Corridor: YES
Wall:
NO
Obstacle: NO
Collision Avoidance Agent
Input fuzzification
Rule matching
Defuzzification
2. Advantages
Dealing with uncertainty
Fast and non-linear computation
Robust and adaptive
Easy to modify
Left sensor = 255
FUZZIFICATION
Sensor
Linguistic
Left sensor input is large
Handler
Variable Inputs
Environment
1.
2.
3.
Crisp Sensor Input
Blackboard
1. Fuzzy Logic
Collision
detector
IF left sensor input is large
(Fuzzy)
Fuzzy Inference
THEN right-turn angle is
large.
Corridor
Recognizer
Linguistic
Right-turn angle is
Variable Outputs Drivelarge.
Controller
DEFUZZIFICATION
Crisp Navigation
Parameter Outputs
Turn-angle = -30˚
Experiment Examples
Corridor Recognition
Only with Collision Avoidance
Obstacle Avoidance Behavior
Door Navigation Behavior
Results I : Robot Performance
1. Corridor Recognition
Successful identification of corridors
Success rate drops in identifying walls and obstacles
2. Fuzzy-based Collision Detection
Retardation caused by ambient light
Advisability of fuzzy rules
3. Control Mechanism
Problems found in knowledge synchronization
In need of handling false claims
Summary
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
Future 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
Thanks
AI Center
UGA