2. Y. Ono, H. Uchiyama, and W. Potter Artificial Intelligence Center The University of Georgia SEACM, April, 2004 A Mobile Robot For Corridor Navigation: Multi-Agent Approach

3. What the paper is about … ? Suggesting a framework for a robot control program independent of the system platform Providing the implementation of a multi-agent system with blackboard architecture Attacking problems in robot navigation using various techniques Goal: to build a controller of autonomous vehicle

4. Presentation Outline: Motivation and Problem Statement Approach Design and Implementation Experimental Results Summary Future Work

5. 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

6. Problem 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)

7. Related Work Vision-based map acquisition (Matsumoto et al. 1999) Road detection (Broggi & Bertè 1995; McDonald et al. 2001) 2. Collision Avoidance Fuzzy logic based (Fayad & Webb 1999; Kimiaghalam et al. 2001) Vision-based (Cho & Nam 2000) 1. Corridor Recognition Multi-agent system (Solar et al. 2000; Sierra et al. 2001) Blackboard architecture (Liscano et al. 1995) 3. Multi-Agent Systems for Robot Control

8. Approach 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

9. Hardware Chassis Wheels Motors Power module Battery 2. Sensors Camera (x1) Infrared Sensors (x9) 1. ER 1 Personal Robot System 3. Laptop Computer Windows XP USB ports Camera Infrared Front view Side view Rear view

10. Software Blackboard as a medium Decentralization Independent Agent Distributed intelligence 2. Agents Sensor Handler Drive Controller Fuzzy Collision Detector Corridor Recognizer 1. Multi-Agent Architecture Blackboard Drive Controller Sensor Handler Collision detector Corridor Recognizer Environment Sensor Handler Driver ? Camera IRs

11. Corridor Recognition Agent Gaussian smoothing filter Sobel edge detector Adaptive thresholding Thinning operator 2. Feature Extraction and Recognition Hough transform Histogram-based intensity analysis 1. Image Segmentation Grayscale160x120 RGBGaussian filter ThresholdingThinning Sobel detector Final Result Corridor: YES Wall: NO Obstacle: NO

12. Note I: Image Processing Smoothing Filter Edge Detector Input Image MEANMEDIANGAUSSIAN LAPLACIANPREWITTROBERTSSOBEL

13. Note II: Hough Transform Parameterized plane ⇒ Image plane

14. Corridor Recognition Agent (cont ’ ) Good prediction on corridors and walls Need a priori knowledge for more effective analysis The robot is facing an object in the path The robot (camera) is facing a narrow corridor The robot is facing a wall

15. Collision Avoidance Agent 1. Input fuzzification 2. Rule matching 3. Defuzzification 2. Advantages Dealing with uncertainty Fast and non-linear computation Robust and adaptive Easy 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 DEFUZZIFICATION Blackboard Drive Controller Sensor Handler Collision detector (Fuzzy) Corridor Recognizer Environment

16. Collision Avoidance Agent (cont ’ ) IF [Left Front] sensor is MEDIUM [Left Rear] sensor is FAR THEN [Turn Angle] is NL 3. Membership functions Input: Sensor value Output:Distance Velocity Turn angle 4. Fuzzy rules 0 -30º30º PR PC PL NR NC NL positive-left (PL) positive-center (PC) positive-right (PR) negative-left (NL) negative-center (NC) negative-right (NR)

17. Note IV: Defuzzification Y-axis: membership degree (0-1) X-axis: crisp input vale Matched rule 1 Matched rule 2 1 0 1 0

18. Experimental Setup Indoor Corridor Situation Dim lighting condition A narrow corridor Objects on the robot ’ s path

19. Experiment Examples Corridor Recognition Only with Collision Avoidance Obstacle Avoidance Behavior Door Navigation Behavior

20. 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

21. Results II : System Evaluation 1. Safety & Stability Memory management Fail-safe routine 2. Modularity & Usability Independent agents Java based GUI

22. 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 Summary

23. 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

24. Thanks AI Center UGA