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Multi-Agent Team Access Monitoring: Environments that Benefit from Target Information Sharing

Andrew Dudash, Scott James, Ryan Rubel

TL;DR

This work extends the minimum-node-cut framework to monitor access to multiple non-contiguous target regions by comparing an iterative per-target approach with a holistic, information-sharing approach. Both methods solve a $minimum\text{-}node\text{-}cut$ on a traversability graph using the $preflow\text{-}push$ algorithm, with the holistic method connecting targets to a common sink to compute a single cut. Through extensive simulations on open and closed grid environments with varying obstacle densities, target counts, and sizes, the authors show that holistic monitoring can reduce robot requirements notably in medium-density scenarios while remaining a valid solution strategy. The findings have practical implications for dynamic checkpointing, surveillance, and hazard containment by leveraging environment structure to share information across targets.

Abstract

Robotic access monitoring of multiple target areas has applications including checkpoint enforcement, surveillance and containment of fire and flood hazards. Monitoring access for a single target region has been successfully modeled as a minimum-cut problem. We generalize this model to support multiple target areas using two approaches: iterating on individual targets and examining the collections of targets holistically. Through simulation we measure the performance of each approach on different scenarios.

Multi-Agent Team Access Monitoring: Environments that Benefit from Target Information Sharing

TL;DR

This work extends the minimum-node-cut framework to monitor access to multiple non-contiguous target regions by comparing an iterative per-target approach with a holistic, information-sharing approach. Both methods solve a on a traversability graph using the algorithm, with the holistic method connecting targets to a common sink to compute a single cut. Through extensive simulations on open and closed grid environments with varying obstacle densities, target counts, and sizes, the authors show that holistic monitoring can reduce robot requirements notably in medium-density scenarios while remaining a valid solution strategy. The findings have practical implications for dynamic checkpointing, surveillance, and hazard containment by leveraging environment structure to share information across targets.

Abstract

Robotic access monitoring of multiple target areas has applications including checkpoint enforcement, surveillance and containment of fire and flood hazards. Monitoring access for a single target region has been successfully modeled as a minimum-cut problem. We generalize this model to support multiple target areas using two approaches: iterating on individual targets and examining the collections of targets holistically. Through simulation we measure the performance of each approach on different scenarios.
Paper Structure (9 sections, 1 theorem, 11 figures, 1 table)

This paper contains 9 sections, 1 theorem, 11 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODOLOGY
  4. Algorithm Description
  5. Algorithm Analysis
  6. Simulator Design
  7. EXPERIMENTS
  8. RESULTS
  9. CONCLUSION

Key Result

Theorem 1

Let there be a graph dual $G$ of the given environment. Let there be $n$ target regions $X_{int}^{i}$ and let $\delta X$ be the border region subgraph. Let there be an extra node $S$ that all $X_{int}^{i}$ are adjacent to. Let there be a partition separating $\delta X$ from $X_{int}^{}$. If the part

Figures (11)

  • Figure 1: Sharing target region information can improve solution quality for some environments.
  • Figure 2: Each node in \ref{['fig:dual1']} corresponds to a discretized space of \ref{['fig:dual2']}. All border regions are contracted to a common node. This is the holistic approach and the graph is non-planar; the three target nodes are all adjacent to one common target node.
  • Figure 3: Experiment 2 compared the performance of the individual and holistic approach on open environments with varied obstacle counts.
  • Figure 4: For all open environments tested, the holistic approach allocates fewer robots than the individual approach. Medium density environments show the greatest improvement.
  • Figure 5: In open environments with high obstacle count, the holistic approach shows a computational advantage compared to the parallelized, individual approach. Conversely, the iterative approach shows a computational advantage for low obstacle count.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof