1 / 27

Experiments in Human-Robot Teams

Experiments in Human-Robot Teams Curtis W. Nielsen, Michael A. Goodrich, Jacob W. Crandall Brigham Young University Motivation Search and Rescue Robotics Still in its infancy Current methods have very high workload The Questions

emily
Download Presentation

Experiments in Human-Robot Teams

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Experiments in Human-Robot Teams Curtis W. Nielsen, Michael A. Goodrich, Jacob W. Crandall Brigham Young University

  2. Motivation • Search and Rescue Robotics • Still in its infancy • Current methods have very high workload

  3. The Questions How do human-robot interactions affect team performance and human workload? Where is the “Sweet Spot?”

  4. Procedure • Domain • Topological map-building • Interaction Schemes • Teleoperate • Point to Point • Region of Interest • Experiment

  5. Behavior-based Landmarks • Set of behaviors afforded to the robot • Affordance: “the perceived actionable properties between the world and an actor” (Gibson) • Actor = robot • Afforded behaviors: turn right, turn left, go forward • Afforded behaviors are found using filtered sonar measurements

  6. Building a Topological Map Classify a landmark Disambiguate landmarks Choose an action

  7. Characterizing the interaction schemes • Landmark classification • Landmark disambiguation • Choose an action • Advantages • Disadvantages

  8. Teleoperate (TOL) • Choose an action: Human • Landmark classification:Human • Landmark disambiguation:Human • Advantage: Human has very high control of the movement of the robot • Disadvantage: The human must devote a lot of attention to the robot

  9. Point To Point (PTP) • Choose an action: Human • Landmark classification:Robot • Landmark disambiguation:Human • Advantage: Relatively low workload • Disadvantage: Requires human control for each new action

  10. Region of Interest (ROI) • Choose an action: Human / Robot • Landmark classification:Robot • Landmark disambiguation:Robot • Advantage: Very little human workload • Disadvantage: Takes a long time to disambiguate landmarks

  11. The interface

  12. Joystick Control Landmark Disambiguation Landmark Classification Action Selection

  13. Point to Point Control Landmark Disambiguation Landmark Classification Action Selection

  14. Region of Interest Control Landmark Disambiguation Landmark Recognition Action Selection

  15. Measuring Performance The time it takes for the system to complete an accurate map of the environment. Time…

  16. Measuring Workload: Behavioral Entropy • Entropy of the joystick (Boer) • Velocity of the mouse. • Button clicks on the mouse and joystick • Change robots • Scaling issues

  17. Experiment: 10 subjects

  18. Region of Interest

  19. Point to Point

  20. Mixed with Joystick

  21. Workload(without joystick)

  22. Elapsed Time (without joystick)

  23. Workload (with Joystick)

  24. Elapsed Time (with Joystick)

  25. 2-PTP, TOL ROI, PTP, TOL 2-ROI, TOL 2-PTP, ROI 3-PTP 3-ROI 2-ROI, PTP Results Tradeoff Curve Without Teleop With Teleop

  26. Conclusions • Measured performance and workload for a system where a human controls 3 robots in a map-building task. • Analyzed the tradeoffs in terms of workload and performance of changing interaction schemes between robots. • Found a sweet spot where performance is relatively high and workload is relatively low. • Sweet spot can change as representation and autonomy level change.

  27. Questions for Future Work • Vary the number of robots? • Vary the number of users? • Vary environment complexity? • Dynamic autonomy? • Workload measurements (scaling issues)?

More Related