Use of an Autonomous Mobile Robot for Elderly Care

1. Use of an Autonomous Mobile Robot for Elderly Care • Artie Shen • Computer Science • Rice University • Karsten Berns, Syed Atif Mehdi. 2010 Advanced Technologies for Ehancing Qauality of Life • M. Proetzsch, T. Luksch, and K. Berns, “Development of complex robotic systems using the behavior-based control architecture iB2C,” Robotics and Autonomous Systems, vol. 58, no. 1, pp. 46–67, 2010.

2. Motivation • A steady increase of elderly population in most modern societies. • Possible Solution : install surveillance devices. • New Solution : Use Mobile Robots!

3. Agenda • Introduction to the problem • Overview of the methodology • Mapping and Localization • Navigation • Searching Human • Experiments • Summary

4. Purpose Statement Major Duty: Periodically search for the human inhabitant; Report to the human caregiver if the inhabitant is at risk;

5. Overview Autonomous Robot for Transport and Service (ARTOS) • Laser Range Finder, Ultrasonic Sensors, Tactile Sensors, Pan-tilt-zoom Camera • Radio Frequency Identification Reader, MCAKL Based Control System

6. Overview • What makes it hard: • Navigation in the complex indoor environment with moving obstacles • Making decision about when and how to search for the human inhabitant • How to solve these problems: • Grid Mapping, A* algorithm, Elastic Band Approach • Markov Decision Process

7. Overview

8. Mapping • Grid Map Approach • Implant the entire indoor space with Radio Frequency Identification tags • 4,000 passive RFID tags with unique coordinate. 5 inch * 5 inch • During navigation, a cell is set to 1 (occupied) if at least one sensor reports occupied; -1 (free) if all sensors report this cell is free; 0 (unknown) otherwise.

9. Localization • Position and Orientation • Position is calculated as the mean value of the RFID tags in range • Orientation is estimated based on detecting several tags while the robot is moving.

10. Navigation • How to move from start point s to terminal t? • Use A* algorithm. For each cell in the path, choose its neighbor n with : • g(c) is the shortest know path from s to c • h(c) is the heuristic estimated cost from c to t. Euclidean distance function is used as the h(c) here. High costs are assigned to the neighbor cells of obstacles.

11. Navigation • Shortest Path = Quickest Path ? Getting too close to the obstacles will result in unnecessary reduction in speed, and the robot might take longer time to get to the goal

12. Navigation • Elastic Band Approach: optimize the path incrementally Sigmoid Function Reference: S. Quinlan and O. Khatib, “Elastic bands: Connecting path planning and control,” in Proceedings of IEEE Int. Conference on Robotics and Automation, Atlanta, 1993, pp. 802–807.

13. Searching Human • How can the robot find the position of the human inhabitant? • Markov Decision Process: (S, A, S’, R(S,A), T(S,A,S’)) • R(S,A) = probability of finding the person at state S with action A = P(S’) • T(S, A, S’) = P(S’) / Estimated Navigation Cost • Rewarding Function: • Global Policy:

14. Searching Human • P(S’): Sample the presentence of the human being in apartment at different places at different times!

15. Searching Human Reference: M. Proetzsch, T. Luksch, and K. Berns, “Development of complex robotic systems using the behavior-based control architecture iB2C,” Robotics and Autonomous Systems, vol. 58, no. 1, pp. 46–67, 2010.

16. Searching Human Reference: M. Proetzsch, T. Luksch, and K. Berns, “Development of complex robotic systems using the behavior-based control architecture iB2C,” Robotics and Autonomous Systems, vol. 58, no. 1, pp. 46–67, 2010.

17. Experiment

18. Experiment The robot never stops!

19. Conclusion

20. Questions?