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Use of an Autonomous Mobile Robot for Elderly Care

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

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Use of an Autonomous Mobile Robot for Elderly Care

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  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?

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