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How Multi-robot Foraging Scales with Number of Robots

How Multi-robot Foraging Scales with Number of Robots. Prasanna Velagapudi Paul Scerri, Katia Sycara Robotics Institute, Carnegie Mellon University Huadong Wang, Michael Lewis Dept. of Information Sciences, University of Pittsburgh. Outline. Motivation Problem Related Work Experiment

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How Multi-robot Foraging Scales with Number of Robots

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  1. How Multi-robot Foraging Scales with Number of Robots Prasanna Velagapudi Paul Scerri, Katia Sycara Robotics Institute, Carnegie Mellon University Huadong Wang, Michael Lewis Dept. of Information Sciences, University of Pittsburgh Speaking Qualifier - Nov. 24, 2009

  2. Outline • Motivation • Problem • Related Work • Experiment • Results • Discussion/Conclusion Speaking Qualifier - Nov. 24, 2009

  3. Motivation Disaster response Search and Rescue Perimeter Patrols Speaking Qualifier - Nov. 24, 2009

  4. Motivation • Goal: Increase operator’s span-of-control • Span-of-control: the number of subordinates (robots) a supervisor (operator) has • Human operators are necessary • Complex perception, meta-knowledge • Supply high-level, dynamic goals • Humans get overloaded Speaking Qualifier - Nov. 24, 2009

  5. Problem: Improving Span-of-Control • How can we increase human operators’ span-of-control? • How does task performance vary with number of robots? • Which tasks are most limiting to the operator? • Does alleviating a task improve performance? Speaking Qualifier - Nov. 24, 2009

  6. Understanding the Task Speaking Qualifier - Nov. 24, 2009

  7. Decomposing Foraging Foraging is composed of two largely independent but concurrent subtasks: Exploration Perceptual Search Speaking Qualifier - Nov. 24, 2009

  8. Focusing the Problem • Multi-robot foraging with waypoint control • Widely cited as likely field application • Each robot searches its own region • Minimal coordination to avoid overlaps or gaps • Waypoint control: • Lowest level of automation compatible with independent control of multiple robots Speaking Qualifier - Nov. 24, 2009

  9. Related Work Speaking Qualifier - Nov. 24, 2009

  10. Related Work: Span-of-control Limits • For foraging, operator span-of-control limits fall between 4-9 robots • Olsen & Wood (2004), Humphrey et al. (2007) • Dependent on environmental demands • Generally, operators can use more robots if robots have a higher level of autonomy Speaking Qualifier - Nov. 24, 2009

  11. Related Work: The Fan-out Plateau If a robot is added to a team, the change in performance is proportional to robot’s independence and operator’s available cognitive resources. • Olsen & Wood (2004) Performance plateau when operator saturates Performance Diminishing returns as # of robots increases # of Robots Speaking Qualifier - Nov. 24, 2009

  12. Fan-out Hypothesis • Task performance should follow Fan-out model Performance Plateau Performance Diminishing returns # of Robots Speaking Qualifier - Nov. 24, 2009

  13. Subtask Hypothesis • Task performance will reflect which subtask is contributing more to operator workload Exploration Subtask 1 Subtask 2 (limiting) Performance Full task Perceptual Search # of Robots Speaking Qualifier - Nov. 24, 2009

  14. Experiment • Task: Simulated search and rescue • Single operator controlling 4, 8, and 12 robots • Waypoint control (primary) • Direct teleoperation • Perform either full foraging task or just one subtask (exploration or perceptual search) Speaking Qualifier - Nov. 24, 2009

  15. USARSim • NIST-maintained open source simulator • High-fidelity physics • “Realistic” rendering • Camera • Laser scanner (LIDAR) • IMU/Odometry [http://www.sourceforge.net/projects/usarsim] Speaking Qualifier - Nov. 24, 2009

  16. Experiment Map P2AT Robots Human “Victims” (24 total) Speaking Qualifier - Nov. 24, 2009

  17. MrCSMulti-robot Control System Speaking Qualifier - Nov. 24, 2009

  18. MrCSMulti-robot Control System Status Window Map Overview Video/ Image Viewer Waypoint Navigation Teleoperation Speaking Qualifier - Nov. 24, 2009

  19. MrCSMulti-robot Control System Speaking Qualifier - Nov. 24, 2009

  20. Conditions Exploration Perceptual Search Full Task • Issue waypoints • Locate victims • Issue waypoints • Locate victims Speaking Qualifier - Nov. 24, 2009

  21. Experimental Procedure • Between groups, repeated measure design Each Subject Speaking Qualifier - Nov. 24, 2009

  22. Methodology • Independent Variables: • Conditions of Task • Numbers of Robots • Dependent Variables: • Workload (NASA-TLX) • Victims found • Area Explored • Switches in focus among robots • Number of assigned missions • Average path length • Robots neglected Speaking Qualifier - Nov. 24, 2009

  23. Participants • 44 paid participants from U. of Pitt. community • 25 male, 20 female • Ages 20-33 (average age 25.18) • No prior experience with robot control • Most were frequent computer users Speaking Qualifier - Nov. 24, 2009

  24. Conditions Exploration Perceptual Search Full Task • Issue waypoints • Locate victims • Issue waypoints • Locate victims Speaking Qualifier - Nov. 24, 2009

  25. Task Performance: Full Task vs. Perceptual Search • Compare number of victims found in full task and perceptual search conditions • Expected: Fan-out plateau and performance gap Perceptual search Performance Full task # of Robots Speaking Qualifier - Nov. 24, 2009

  26. Task Performance: Full Task vs. Perceptual Search Diminishing return/plateau F1,28= 27.4 p < .0001 Operator overload Speaking Qualifier - Nov. 24, 2009

  27. Task Performance:Full Task vs. Exploration • Compare total area explored in full task and exploration condition • Expected: Fan-out plateau and performance gap Exploration Performance Full task # of Robots Speaking Qualifier - Nov. 24, 2009

  28. Task Performance:Full Task vs. Exploration Diminishing return/plateau F1,28= 21.17 p < .002 Operator overload Similar performance Speaking Qualifier - Nov. 24, 2009

  29. Exploration Control Statistics # Paths Issued Average Path Length Longer paths Fewer paths # of Paths Length of Path (m) Speaking Qualifier - Nov. 24, 2009

  30. Neglected Robots • Neglected robots are never issued waypoints • Further evidence of reaching operator span-of-control limits χ22=10.75 p = 0.005 # Neglected Robots Speaking Qualifier - Nov. 24, 2009

  31. Number of Switches Between Robots • Before operators issue waypoints or mark victims, they must switch to the desired robot • Switches are cognitively expensive to operators Exploration Perceptual Search Speaking Qualifier - Nov. 24, 2009

  32. Number of Switches Between Robots Close correspondence Significant gap Number of Switches Between Robots F2,54 = 12.6 p < .0001 Speaking Qualifier - Nov. 24, 2009

  33. Operator Pause Statistics • Robots become paused when commanded by operator or when finished with all waypoints • Perception pauses • Operator stops robot in middle of mission to locate victim • Navigation pauses: • Robot is paused by itself or by operator so that a new set of waypoints can be issued • Pausesmay indicate operator neglect Speaking Qualifier - Nov. 24, 2009

  34. Distribution of Pauses More frequent pauses Full Task Exploration Perceptual Search Pause (sec) Counts 4 8 12 4 8 12 12 4 8 More long-duration pauses (robot neglect) # of Robots Speaking Qualifier - Nov. 24, 2009

  35. Aggregate Pause Duration Aggregate Pause Duration per Robot (sec) Speaking Qualifier - Nov. 24, 2009

  36. Workload Survey • Survey (NASA-TLX) administered after each session (4, 8, 12 robots) • Aggregate score indicates participants’ subjective assessment of cognitive workload • Expected: Full task Subtask 2 (limiting) Subtask 1 Workload # of Robots Speaking Qualifier - Nov. 24, 2009

  37. Workload Survey Significant gap F1,27 = 21.17 p < .0001 Speaking Qualifier - Nov. 24, 2009

  38. Discussion Fan-out Hypothesis: Task performance should follow Fan-out model • Performance in subtasks consistent with fan-out • Performance in full task does not follow fan-out • Inflection point in performance at 8 robots • Operators experiencing cognitive overload Speaking Qualifier - Nov. 24, 2009

  39. Discussion Subtask Hypothesis: Task performance will reflect which subtask is contributing more to operator workload • Exploration and full task performancesimilar • Removing perceptual search subtask removes little workload • Perceptual search performs better than full task • Significantly less workload than other tasks with 12 robots Speaking Qualifier - Nov. 24, 2009

  40. Conclusions • How does task performance vary with number of robots? Fulltask: operators overload, performance drops Subtasks: follows fan-out model • Which tasks are most limiting to the operator? The exploration subtask • Does alleviating a task improve performance? Removing exploration helps, removing perceptual search does not Speaking Qualifier - Nov. 24, 2009

  41. Conclusions • How can we increase human operators’ span-of-control? • Offload (automate) Exploration subtask • When operators are overloaded with full task, performance drops dramatically • Operators devote most cognitive effort to exploration, performance not much better than full task Speaking Qualifier - Nov. 24, 2009

  42. Future Work • Effects of exploration autonomy • How is span-of-control affected by higher-level autonomy, sliding autonomy? • Effects of perception/exploration task difficulty • Do scaling effects hold in more realistic environments? • Scaling to larger team sizes (16, 24+) • Do subtasks also have steep performance drop-offs? • Multi-operator interaction • Can subtasks be distributed across operators? Speaking Qualifier - Nov. 24, 2009

  43. Questions? Speaking Qualifier - Nov. 24, 2009

  44. Speaking Qualifier - Nov. 24, 2009

  45. Related Work - Supplemental Speaking Qualifier - Nov. 24, 2009

  46. Methodology – Perceptual search • If an autonomous path planner is used: • Covers a larger area than possible with a human operator (never pauses upon arrival at a waypoint) • If human generated trajectories are taken from the full task: • Pauses for waypoint completion • Pauses at locations where victims were found • Solution: Use trajectories from the exploration condition • Contain pauses associated with waypoint arrival • Do not contain pauses for identifying victims. • Operators must be able to pause robots when they discover victims Speaking Qualifier - Nov. 24, 2009

  47. Learning curves 8 Robots 4 Robots 100% 100% 35 20 35 50 20 12 Robots 100% 20 35 65 50

  48. Missions Assigned Operator overload F1,28=6.34 p < .018 Missions assigned Speaking Qualifier - Nov. 24, 2009

  49. Average Path Length Average Path Length (m) Speaking Qualifier - Nov. 24, 2009

  50. USARSim Validation Studies • Synthetic video • Carpin, S., Stoyanov, T., Nevatia, Y., Lewis, M. and Wang, J. (2006a). Quantitative assessments of USARSim accuracy". Proceedings of PerMIS 2006 • Hokuyo laser range finder • Carpin, S., Wang, J., Lewis, M., Birk, A., and Jacoff, A. (2005). High fidelity tools for rescue robotics: Results and perspectives, Robocup 2005 Symposium. • Platform physics & behavior • Carpin, S., Lewis, M., Wang, J., Balakirsky, S. and Scrapper, C. (2006b). Bridging the gap between simulation and reality in urban search and rescue. Robocup 2006: Robot Soccer World Cup X, Springer, Lecture Notes in Artificial Intelligence • Lewis, M., Hughes, S., Wang, J., Koes, M. and Carpin, S., Validating USARsim for use in HRI research, Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, Orlando, FL, 457-461, 2005. • Pepper, C., Balakirsky, S. and Scrapper, C., Robot Simulation Physics Validation, Proceedings of PerMIS’07, 2007. • Taylor, B., Balakirsky, S., Messina, E. and Quinn, R., Design and Validation of a Whegs Robot in USARSim, Proceedings of PerMIS’07. • Zaratti, M., Fratarcangeli, M., and Iocchi, L., A 3D Simulator of Multiple Legged Robots based on USARSim. Robocup 2006: Robot Soccer World Cup X, Springer, LNAI, 2006. [http://www.sourceforge.net/projects/usarsim] Speaking Qualifier - Nov. 24, 2009

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