Potential scaling effects for asynchronous video in multirobot search
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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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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


Urban Search and Rescue (USAR)

  • Location and rescue of people in a structural collapse

  • Urban disasters

    • Landslides

    • Earthquakes

    • Terrorism

Credit: NIST


USAR Robots

  • Robots can help

    • Unstable voids

    • Mapping/clearing

  • Want them to be:

    • Small

    • Cheap

    • Plentiful

Credit: NIST


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


Synchronous Video

  • Most common form of camera teleoperation

    • High bandwidth

    • Low latency

  • Applications

    • Surveillance

    • Bomb disposal

    • Inspection

Credit: iRobot


Synchronous Video

  • Does not scale with team size


Synchronous Video

  • Does not scale with team size


Synchronous Video

  • Does not scale with team size


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


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


USARSim

  • Based on UnrealEngine2

  • High-fidelity physics

  • “Realistic” rendering

    • Camera

    • Laser scanner (LIDAR)

[http://www.sourceforge.net/projects/usarsim]


MrCSMulti-robot Control System


MrCSMulti-robot Control System

Status Window

Map Overview

Video/ Image Viewer

Waypoint Navigation

Teleoperation


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


Experimental Conditions

Arena 2

10 Victims

Arena 1


Streaming Mode

Panorama Mode

Panoramas stored for later viewing

Streaming live video

Experimental Conditions


Experimental Conditions(Streaming Mode)


Experimental Conditions(Panorama Mode)


Subjects

  • 21 paid participants

    • 9 male, 12 female

    • No prior experience with robot control

    • Frequent computer users: 71%

    • Played computers games > 1hr/week: 28%


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)


Metrics

  • Switching times

  • Number of victims

    • Thresholded accuracy


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


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


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


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


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


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


Experimental Conditions

8

4

12


Experimental Conditions(Streaming Mode)


Subjects

  • 15 paid participants

    • 8 male, 7 female

    • No prior experience with robot control

    • Most were frequent computer users


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)


Metrics

  • Explored regions

  • Number of victims

  • Neglect tolerance

  • Switching times

  • Number of missions

  • NASA-TLX workload


Explored Region

Area explored


Victims Found

Number of Victims


Victims Found per Robot

Number of Victims


Neglected Robots

Totally

Number of Robots

Initial Move


Switch Times

Number of Switches


Mission Numbers

Number of Missions


NASA-TLX Workload

Workload


Fan-out

(Neglect Tolerance)

(Interaction Time)


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


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


Experimental Conditions

8

4

12


Experimental Conditions(Panorama Mode)


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)


Metrics

  • Explored regions

  • Number of victims

  • Neglect tolerance

  • Switching times

  • Number of missions

  • NASA-TLX workload


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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