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Potential Scaling Effects for Asynchronous Video in Multirobot Search

Potential Scaling Effects for Asynchronous Video in Multirobot Search. Prasanna Velagapudi 1 , Huadong Wang 2 , Paul Scerri 1 , Michael Lewis 2 and Katia Sycara 1 1 Carnegie Mellon University, USA 2 University of Pittsburgh, USA. Urban Search and Rescue (USAR).

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Potential Scaling Effects for Asynchronous Video in Multirobot Search

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  1. Potential Scaling Effects for Asynchronous Video in Multirobot Search Prasanna Velagapudi1, Huadong Wang2, Paul Scerri1, Michael Lewis2 and Katia Sycara1 1Carnegie Mellon University, USA2University of Pittsburgh, USA

  2. Urban Search and Rescue (USAR) • Location and rescue of people in a structural collapse • Urban disasters • Landslides • Earthquakes • Terrorism Credit: NIST

  3. USAR Robots • Robots can help • Unstable voids • Mapping/clearing • Want them to be: • Small • Cheap • Plentiful Credit: NIST

  4. Urban Search and Rescue (USAR) • Now: One operator  one robot • Directly teleoperated • Victim detection through synchronous video • Future: One operator  many robots • Manufacturing robots is easy • Training operators is hard • Need to scale navigation and search

  5. Synchronous Video • Most common form of camera teleoperation • High bandwidth • Low latency • Applications • Surveillance • Bomb disposal • Inspection Credit: iRobot

  6. Synchronous Video • Does not scale with team size

  7. Synchronous Video • Does not scale with team size

  8. Synchronous Video • Does not scale with team size

  9. Asynchronous Imagery • Inspired by planetary robotic solutions • Limited bandwidth • High latency • Multiple photographs from single location • Maximizes coverage • Can be mapped to virtual pan-tilt-zoom camera

  10. Hypothesis • Asynchronicity may improve performance • Helps guarantee coverage • Can review imagery on demand • Asynchronicity may reduce mental workload • Only navigation must be done in real-time • Search becomes self-paced

  11. USARSim • Based on UnrealEngine2 • High-fidelity physics • “Realistic” rendering • Camera • Laser scanner (LIDAR) [http://www.sourceforge.net/projects/usarsim]

  12. MrCSMulti-robot Control System

  13. MrCSMulti-robot Control System Status Window Map Overview Video/ Image Viewer Waypoint Navigation Teleoperation

  14. Pilot Study • Objective: • Find victims  Mark victims on map • Control 4 robots • Waypoint control (primary) • Direct teleoperation • Explore the map • Map generated online w/ Occupancy Grid SLAM • Simulated laser scanners

  15. Experimental Conditions Arena 2 10 Victims Arena 1

  16. Streaming Mode Panorama Mode Panoramas stored for later viewing Streaming live video Experimental Conditions

  17. Experimental Conditions(Streaming Mode)

  18. Experimental Conditions(Panorama Mode)

  19. Subjects • 21 paid participants • 9 male, 12 female • No prior experience with robot control • Frequent computer users: 71% • Played computers games > 1hr/week: 28%

  20. Method • Written instructions • 20 min. training session • Both streaming and panoramas enabled • Encouraged to find and mark at least one victim • 20 min. testing session (Arena 1) • 20 min. testing session (Arena 2)

  21. Metrics • Switching times • Number of victims • Thresholded accuracy

  22. Panorama 6 Streaming 5 4 3 2 1 0 Within 0.75m Within 1m Within 1.5m Within 2m Accuracy Threshold Victims Found Average # of victims found

  23. 7 Panorama First 6 < 2m < 1.5m 5 4 < 2m 3 < 1.5m Streaming First 2 1 0 First Session Second Session Trial Order Interaction Average # of victims found

  24. 12 10 8 6 4 2 0 0 20 40 60 80 100 120 Number of Switches Switching Time (Streaming Mode) p=0.064 Average # of reported victims

  25. 12 10 8 6 4 2 0 0 20 40 60 80 100 120 Number of Switches Switching Time (Panorama Mode) Average # of reported victims

  26. Summary • Streaming is better than panoramic • Perhaps not by as much as expected • Conditions favorable to streaming video • Asynchronous performance has potential • May avoid forced pace switching • May scale with team size

  27. Synchronous Scaling • Objective: • Find victims  Mark victims on map • Control 4, 8, 12 robots • Waypoint control (primary) • Direct teleoperation • Explore the map • Map generated online w/ Occupancy Grid SLAM • Simulated laser scanners

  28. Experimental Conditions 8 4 12

  29. Experimental Conditions(Streaming Mode)

  30. Subjects • 15 paid participants • 8 male, 7 female • No prior experience with robot control • Most were frequent computer users

  31. Method • Written instructions • 20 min. training session • Encouraged to find and mark at least one victim • 20 min. testing session (4 robots) • 20 min. testing session (8 robots) • 20 min. testing session (12 robots)

  32. Metrics • Explored regions • Number of victims • Neglect tolerance • Switching times • Number of missions • NASA-TLX workload

  33. Explored Region Area explored

  34. Victims Found Number of Victims

  35. Victims Found per Robot Number of Victims

  36. Neglected Robots Totally Number of Robots Initial Move

  37. Switch Times Number of Switches

  38. Mission Numbers Number of Missions

  39. NASA-TLX Workload Workload

  40. Fan-out (Neglect Tolerance) (Interaction Time)

  41. Summary • Bounded number of directly controllable robots between 8 and 12 • Diminishing returns as robots are added • Performance drops above 8 robots • Fan-out parallels the number of robots operator controls • Operators using satisficing strategy

  42. Asynchronous Scaling (Proposed) • Objective: • Find victims  Mark victims on map • Control 4, 8, 12 robots • Waypoint control (primary) • Direct teleoperation • Explore the map • Map generated online w/ Occupancy Grid SLAM • Simulated laser scanners

  43. Experimental Conditions 8 4 12

  44. Experimental Conditions(Panorama Mode)

  45. Method • Written instructions • 20 min. training session • Both streaming and panoramas enabled • Encouraged to find and mark at least one victim • 20 min. testing session (4 robots) • 20 min. testing session (8 robots) • 20 min. testing session (12 robots)

  46. Metrics • Explored regions • Number of victims • Neglect tolerance • Switching times • Number of missions • NASA-TLX workload

  47. Expected Contributions • Determine when asynchronicity is useful • Advantages for larger team sizes • Simultaneous search is not viable • Establish performance baselines for asynchronous search

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