1. EE887 Special Topics in Robotics Paper Review Initial Results in the Development Guidance System of a Guidance System for a Powered Wheelchair 2000. 6. 7. BSCL Lee Hyong Euk

2. General Idea of this paper The autonomous navigation and the control of wheelchair What are the issues in the wheelchair application?  User Interface  Obstacle avoidance  Battery usage  Seating Comport  User demographics  … The navigation(guidance) of the wheelchair, Particularly the estimation and control of the system Focus :

3. Requirement for Wheelchair Application 1.System must be more accurate 2.System must be robust and repeatable 3.Smooth ride to ensure user comport 4.System should be simple and inexpensive 5.Remember that the passenger is a human being

4. Requirement for Wheelchair Application 1.System must be more accurate 2.System must be robust and repeatable 3.Smooth ride to ensure user comport 4.System should be simple and inexpensive 5.Remember that the passenger is a human being  This requirements need ‘ exact position estimation ’

5. Experimental Wheelchair System 512 by 480 pixel CCD Black and white 1000 count-per revolution Optical encoders The camera view is not blocked by the user ’ s lower extremities. Everest & Jennings Powered Wheelchair 26 inches

6. Approach(1) Odometry information(wheel rotation) External vision-based observation of surrounding envrionment Combining and Applying Extended Kalman Filter algorithm Optimal estimate of the wheelchair ’ s pose Observation or measurement noise are modeled by Gaussian Distributed white process A set of diff. Equation which relate wheel motion to the position and orientation of the wheel chair are numerically integrated to produce the so-called “ dead-reckoned ”

7. Approach(2) Automatic Guidance of the wheelchair  “ Teach-repeat ” Procedure The role of two video camera  Detect the cues in the surroundings

8. Approach(2) Automatic Guidance of the wheelchair  “ Teach-repeat ” Procedure The role of two video camera  Detect the cues in the surroundings : Reference hints for pos. estimation (ex. Desk, wall, or any fixed one in the workspace) 16 cues were used for this system (some objects in the experimental workspace) ‘ cue ’ is a priori information

9. Approach(3)

10. Experiments Test environment : home, office, classroom, laboratory, … Load : 200-lb human passenger and other equipments(PC, … ) Floor surface : smooth poured concrete, tile, various carpet type  Room layout with nominal path Ref. Path was taught in the Mechanical Systems and Robotics Lab. At the University of Notredam

11. Experiment Results(1) Tracking Ref. Path Result Total Time : 175s Avr. Speed : 0.5 ft/s 16 cues

12. Experiment Results(2) Tracking Ref. Path Result with obstacle avoidance Manual Control for obstacle avoidance

13. Experiment Results(3) Speed Control : error between actual and estimated position Avr. X error (in) Avr. Y error (in) Max. X error (in) Max. Y error (in) Nominal speed1.0061.1672.8842.393 Low speed0.1750.1460.3690.393 For 10 consecutive run case

14. Discussion 1.The extended Kalman filter accurately estimate the system ’ s pose(position & orientation) 2.The limitation of evaluating accuracy depend on the position of the ‘ cue ’ s.  a total of four cues were available for the last 8 ft of the path. 3. The obstacle avoidance strategy must be developed 4. User interface and other considerable factor is remained jobs.