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Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus

Zachary R. Madin, Jonathan Lawry, Edmund R. Hunt

TL;DR

This work examines the performance of unmodified idleness-based patrolling algorithms when given the additional objective of reaching an environmental perception consensus via local pairwise communication and a quorum threshold and finds that generally, MRP algorithms that promote physical mixing of robots, as measured by a higher connectivity of their emergent communication network, reach consensus more quickly.

Abstract

An important real-world application of multi-robot systems is multi-robot patrolling (MRP), where robots must carry out the activity of going through an area at regular intervals. Motivations for MRP include the detection of anomalies that may represent security threats. While MRP algorithms show some maturity in development, a key potential advantage has been unexamined: the ability to exploit collective perception of detected anomalies to prioritize the location ordering of security checks. This is because noisy individual-level detection of an anomaly may be compensated for by group-level consensus formation regarding whether an anomaly is likely to be truly present. Here, we examine the performance of unmodified idleness-based patrolling algorithms when given the additional objective of reaching an environmental perception consensus via local pairwise communication and a quorum threshold. We find that generally, MRP algorithms that promote physical mixing of robots, as measured by a higher connectivity of their emergent communication network, reach consensus more quickly. However, when there is noise present in anomaly detection, a more moderate (constrained) level of connectivity is preferable because it reduces the spread of false positive detections, as measured by a group-level F-score. These findings can inform user choice of MRP algorithm and future algorithm development.

Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus

TL;DR

This work examines the performance of unmodified idleness-based patrolling algorithms when given the additional objective of reaching an environmental perception consensus via local pairwise communication and a quorum threshold and finds that generally, MRP algorithms that promote physical mixing of robots, as measured by a higher connectivity of their emergent communication network, reach consensus more quickly.

Abstract

An important real-world application of multi-robot systems is multi-robot patrolling (MRP), where robots must carry out the activity of going through an area at regular intervals. Motivations for MRP include the detection of anomalies that may represent security threats. While MRP algorithms show some maturity in development, a key potential advantage has been unexamined: the ability to exploit collective perception of detected anomalies to prioritize the location ordering of security checks. This is because noisy individual-level detection of an anomaly may be compensated for by group-level consensus formation regarding whether an anomaly is likely to be truly present. Here, we examine the performance of unmodified idleness-based patrolling algorithms when given the additional objective of reaching an environmental perception consensus via local pairwise communication and a quorum threshold. We find that generally, MRP algorithms that promote physical mixing of robots, as measured by a higher connectivity of their emergent communication network, reach consensus more quickly. However, when there is noise present in anomaly detection, a more moderate (constrained) level of connectivity is preferable because it reduces the spread of false positive detections, as measured by a group-level F-score. These findings can inform user choice of MRP algorithm and future algorithm development.
Paper Structure (12 sections, 2 equations, 5 figures, 4 tables)

This paper contains 12 sections, 2 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. BACKGROUND
  3. Multi-Robot Patrolling Algorithms
  4. Collective Decision-Making and Perception
  5. Communication Graph Connectivity
  6. Experimental Set Up
  7. Simulator
  8. Opinion Dynamics
  9. Error and F-Score
  10. Experiment Parameters
  11. Simulation Results and Discussion
  12. Conclusion and Future Work

Figures (5)

  • Figure 1: Trajectory plot of a system of eight robots executing the SEBS patrol algorithm for over an hour on the 'Cumberland' map , with each unique color representing a single robot and its patrol route.
  • Figure 2: Example social network of eight robots executing SEBS algorithm on 'Cumberland' map for 1 hour. Nodes are individual robots, with edges representing the number of communications between respective robots. Darker shade indicates more frequent communication between robots.
  • Figure 3: Algebraic Connectivity results for each algorithm tested
  • Figure 4: Time to reach 85% consensus on accurate view of world state against algebraic connectivity for each algorithm and noise level.
  • Figure 5: For 20 simulation runs, Top: Proportion of runs that reached consensus ($> 85\%$ or at least 7 of 8 robots) on particular node containing true positive (real security threat); Bottom: Average of count false positive consensus on other nodes.