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arxiv: 2605.16673 · v1 · pith:QA2LY2U2new · submitted 2026-05-15 · 💻 cs.RO

Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments

Alkesh K. Srivastava , George P. Kontoudis , Donald Sofge , Michael Otte This is my paper

Pith reviewed 2026-05-20 17:01 UTC · model grok-4.3

classification 💻 cs.RO
keywords path-basedpathbeliefeventsensorbayesianhazardinformation
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The pith

A Bayesian Network models relationships between latent hazard locations and path-based observations to produce more principled belief updates and quicker convergence than previous methods in single- and multi-robot hazard detection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Path-based sensors give one yes-or-no answer for an entire route instead of pinpointing where something happened. Earlier techniques combined several such answers by averaging possible explanations. The new work builds a Bayesian Network that explicitly links hidden hazard positions to the sensor readings, including the chance of false alarms or missed detections. When a robot returns safely it updates the map as no hazard found; if it does not return the map increases the chance of hazard somewhere on that path. The network lets the system calculate information gain more directly so the next path chosen gathers more new knowledge about where hazards actually are. Tests in the abstract show faster map convergence for both one robot and teams of robots.

Core claim

We find that the new method leads to quicker convergence of the belief map than prior work in both single- and multi-robot cases.

Load-bearing premise

The Bayesian Network formulation accurately captures the probabilistic relationships between latent event locations and path-based measurements without requiring additional approximations beyond those stated for false positives and negatives.

Figures

Figures reproduced from arXiv: 2605.16673 by Alkesh K. Srivastava, Donald Sofge, George P. Kontoudis, Michael Otte.

Figure 1
Figure 1. Figure 1: Top-Left: Belief about hazard existence (grayscale) from three path-based sensor observations. Agents are destroyed along paths 1 and 2 (red), causing two sensor triggers (Θ = 1) that increase belief about hazards along those paths (dark gray). An agent survives path 3 (light green) causing a non-trigger (Θ = 0) decreasing belief about hazards (light gray). Bottom: Extension of this idea to multiple simult… view at source ↗
Figure 2
Figure 2. Figure 2: Bayesian networks used for inference based on path-based sensor triggering. (a) Bayesian network used when the path-based sensor is triggered (the robot is de￾stroyed, Θ = 1) along a path of length ℓ. The observation layer (red) indicates whether the path-based sensor was triggered. The inference layer (green) speculates the most likely triggering cell based on the prior belief map. The estimation layer (y… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of information entropy across search rounds for different methods: (a) BNITP vs. ITP [18] for 70% lethality; (b)-(d) BN-DEVPP vs. DEVPP [26] for 70% lethality and n = 3, 5, 7 respectively; (e) BNITP vs. ITP [18]; and (f)-(h) BN￾DEVPP vs. DEVPP [26] for 90% hazard lethality and n = 3, 5, 7 respectively [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Computation time per agent for dif￾ferent n. By definition n = 1 for BNITP and n > 1 for BN-DEVPP. Results for 70% (Left) and 90% (Right) lethal hazards. Box-plots show statistics over search rounds. Computation time per agent nn n n num agents deployed per round num agents deployed per round In the second experiment, we observe the number of agents lost in the haz￾ardous environment until the information … view at source ↗
read the original abstract

A "path-based sensor" produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single "1" if an event of interest is detected at any point along the path and a "0" otherwise. Notably, a "1" provides no direct information about where along the path the event(s) may have occurred. Previous work has demonstrated that observations from multiple path-based sensors can be fused to create a Bayesian belief map over the spatial locations of the underlying event or phenomenon. Moreover, path planning can employ Shannon information theory to accelerate the rate of convergence of the belief map. In this paper, we present a new method to update the belief map based on a path-based sensor observation, and then plan paths to increase information gain. In contrast to prior work that approximates the posterior by averaging over the alternative event histories, we introduce a Bayesian Network (BN) formulation that models the probabilistic relationships between the latent variables and path-based sensor measurements, enabling a more principled Bayesian belief update. We consider static hazard detection in a communication-denied environment as a representative problem setting. The event of a robot returning from its path corresponds to a path-based hazard sensor reading of "0" (hazard not detected), while a robot failing to return corresponds to a reading of "1" (hazard detected). We consider false positives and false negatives. We find that the new method leads to quicker convergence of the belief map than prior work in both single- and multi-robot cases.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a Bayesian Network (BN) formulation to update a spatial belief map over latent event locations from path-based sensor observations in communication-denied environments. Using static hazard detection as the running example, a robot's return (reading 0) or failure to return (reading 1) provides a binary observation with modeled false-positive and false-negative rates; the BN is presented as a more principled alternative to prior posterior approximations that average over event histories. The work also incorporates Shannon information gain for path planning and claims that the BN update produces faster belief-map convergence than previous methods in both single- and multi-robot settings.

Significance. If the claimed improvement in convergence rate is substantiated with quantitative metrics, the BN approach could provide a cleaner Bayesian treatment of ambiguous path-based measurements and thereby strengthen information-theoretic planning for multi-robot teams operating without reliable communication. The formulation appears to avoid additional ad-hoc approximations beyond the stated sensor error model, which is a positive structural feature.

major comments (2)
  1. [Abstract / Results] Abstract and Results: The central claim that the BN method 'leads to quicker convergence of the belief map than prior work in both single- and multi-robot cases' is presented without quantitative support (e.g., convergence curves, KL-divergence or entropy reduction rates, error bars, or tabulated comparisons). Because this assertion is the primary empirical contribution, the lack of visible metrics undermines evaluation of the method's practical advantage.
  2. [§3] §3 (BN Formulation): The statement that the BN 'models the probabilistic relationships between the latent variables and path-based sensor measurements' enabling a 'more principled Bayesian belief update' requires an explicit derivation or factor-graph diagram showing how the joint distribution is factorized and how the update differs from the averaging-over-histories baseline; without this, it is unclear whether the BN simply re-expresses the same marginalization or introduces a genuine computational or accuracy benefit.
minor comments (2)
  1. Define all acronyms at first use (BN, KL, etc.) and ensure consistent notation for the belief map and information-gain objective across sections.
  2. [Multi-robot planning subsection] Clarify how the multi-robot case coordinates path selection under communication denial; the current description leaves open whether robots share belief maps or operate with local copies.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a new Bayesian Network formulation to update belief maps from path-based sensor observations (including false positives/negatives) and uses it for information-gain path planning. This is explicitly contrasted with prior approximation methods that average over event histories. The central claims rest on the BN modeling of latent variables and measurements plus empirical simulation results showing faster convergence; no equations or steps reduce by construction to fitted inputs, self-definitions, or load-bearing self-citations. The derivation is self-contained with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not detail specific free parameters or axioms; false positive and false negative rates are mentioned as considered but not quantified or derived.

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Works this paper leans on

31 extracted references · 31 canonical work pages

  1. [1]

    In: IEEE/RSJ International Conference on Intelligent Robots and Systems

    Bourgault, F., Makarenko, A.A., Williams, S.B., Grocholsky, B., Durrant-Whyte, H.F.: Information based adaptive robotic exploration. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 1, pp. 540–545 (2002)

    work page 2002

  2. [2]

    Autonomous Robots37(4), 383–400 (2014)

    Charrow, B., Kumar, V., Michael, N.: Approximate representations for multi-robot control policies that maximize mutual information. Autonomous Robots37(4), 383–400 (2014)

    work page 2014

  3. [3]

    In: IEEE International Confer- ence on Decision and Control

    Flint, M., Fernandez-Gaucherand, E., Polycarpou, M.: Cooperative control for UAV’s searching risky environments for targets. In: IEEE International Confer- ence on Decision and Control. vol. 4, pp. 3567–3572 (2003)

    work page 2003

  4. [4]

    Machine learning29(2), 131–163 (1997)

    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine learning29(2), 131–163 (1997)

    work page 1997

  5. [5]

    In: IEEE Interna- tional Conference on Robotics and Automation

    Furukawa, T., Bourgault, F., Lavis, B., Durrant-Whyte, H.: Recursive Bayesian search-and-tracking using coordinated UAVs for lost targets. In: IEEE Interna- tional Conference on Robotics and Automation. pp. 2521–2526 (2006)

    work page 2006

  6. [6]

    Current Robotics Reports pp

    Gielis, J., Shankar, A., Prorok, A.: A critical review of communications in multi- robot systems. Current Robotics Reports pp. 1–13 (2022)

    work page 2022

  7. [7]

    Grocholsky, B.: Information-theoretic control of multiple sensor platforms. Ph.D. thesis, University of Sydney. School of Aerospace, Mechanical and Mechatronic Engineering (2002)

    work page 2002

  8. [8]

    IEEE Transactions on Knowledge and Data Engineering28(12), 3395–3408 (2016)

    Hao, W., Yeung, D.Y.: Towards Bayesian deep learning: A framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering28(12), 3395–3408 (2016)

    work page 2016

  9. [9]

    In: IEEE/RSJ International Conference on Intelligent Robots and Systems

    Jorgensen, S., Chen, R.H., Milam, M.B., Pavone, M.: The matroid team surviving orienteers problem: Constrained routing of heterogeneous teams with risky traver- sal. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 5622–5629 (2017)

    work page 2017

  10. [10]

    The International Journal of Robotics Research31(10), 1134–1154 (2012)

    Julian, B.J., Angermann, M., Schwager, M., Rus, D.: Distributed robotic sen- sor networks: An information-theoretic approach. The International Journal of Robotics Research31(10), 1134–1154 (2012)

    work page 2012

  11. [11]

    MIT press (2009)

    Koller, D., Friedman, N.: Probabilistic graphical models: Principles and techniques. MIT press (2009)

    work page 2009

  12. [12]

    IEEE Robotics and Automation Letters1(2), 1164–1171 (2016)

    Lyu, Y.H., Chen, Y., Balkcom, D.:k-survivability: Diversity and survival of ex- pendable robots. IEEE Robotics and Automation Letters1(2), 1164–1171 (2016)

    work page 2016

  13. [13]

    In: IEEE/RSJ International Conference on Intelligent Robots and Systems

    McGuire, L., Otte, M., Sofge, D.: Valuing attrition in a fleet of robots used as path- based sensors for gathering information in a communications restricted environ- ment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 7633–7640 (2024)

    work page 2024

  14. [14]

    In: International Conference on Autonomous Agents and Multiagent Systems

    Mendelsohn, A., Sofge, D., Otte, M.: Enhancing search and rescue capabilities in hazardous communication-denied environments through path-based sensors with backtracking. In: International Conference on Autonomous Agents and Multiagent Systems. pp. 2387–2389 (2024), (extended abstract)

    work page 2024

  15. [15]

    Entropy17(6), 3989–4027 (2015)

    Mohammad-Djafari, A.: Entropy, information theory, information geometry and Bayesian inference in data, signal and image processing and inverse problems. Entropy17(6), 3989–4027 (2015)

    work page 2015

  16. [16]

    IEEE Signal Processing Magazine35(1), 101–111 (2018) 18 A

    Nie, S., Zheng, M., Ji, Q.: The deep regression Bayesian network and its appli- cations: Probabilistic deep learning for computer vision. IEEE Signal Processing Magazine35(1), 101–111 (2018) 18 A. K. Srivastava, G. P. Kontoudis, D. Sofge, and M. Otte

    work page 2018

  17. [17]

    Entropy21(11), 1081 (2019)

    Oladyshkin, S., Nowak, W.: The connection between Bayesian inference and in- formation theory for model selection, information gain and experimental design. Entropy21(11), 1081 (2019)

    work page 2019

  18. [18]

    IEEE Transactions on Automation Science and Engineering18(3), 946–967 (2021)

    Otte, M., Sofge, D.: Path-based sensors: Paths as sensors, Bayesian updates, and Shannon information gathering. IEEE Transactions on Automation Science and Engineering18(3), 946–967 (2021)

    work page 2021

  19. [19]

    Morgan kaufmann (1988)

    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible in- ference. Morgan kaufmann (1988)

    work page 1988

  20. [20]

    Naval Research Logistics (NRL)57(5), 422–440 (2010)

    Sato, H., Royset, J.O.: Path optimization for the resource-constrained searcher. Naval Research Logistics (NRL)57(5), 422–440 (2010)

    work page 2010

  21. [21]

    In: Robotics Research, pp

    Schwager, M., Dames, P., Rus, D., Kumar, V.: A multi-robot control policy for information gathering in the presence of unknown hazards. In: Robotics Research, pp. 455–472. Springer (2017)

    work page 2017

  22. [22]

    The Bell System Tech- nical Journal27(3), 379–423 (1948)

    Shannon, C.E.: A mathematical theory of communication. The Bell System Tech- nical Journal27(3), 379–423 (1948)

    work page 1948

  23. [23]

    Srivastava, A.K., Kontoudis, G.P., Sofge, D., Otte, M.: Path-based sensors: Will the knowledge of correlation in random variables accelerate information gathering? (2023), https://arxiv.org/abs/2305.06929

    work page arXiv 2023

  24. [24]

    Srivastava, A.K., Kontoudis, G.P., Sofge, D., Otte, M.: Path-based sensors: Will the knowledge of correlation in random variables accelerate information gather- ing? In: IEEE International Conference on Robotics and Automation, Workshop on Communication Challenges in Multi-Robot Systems: Perception, Coordination, and Learning (2023)

    work page 2023

  25. [25]

    arXiv preprint arXiv:2508.04537 (2025)

    Srivastava, A.K., Suresh, A., Nieto-Granda, C.: Behaviorally adaptive multi-robot hazard localization in failure-prone, communication-denied environments. arXiv preprint arXiv:2508.04537 (2025)

    work page arXiv 2025

  26. [26]

    In: International Symposium on Distributed Autonmous Robotic Systems (2022)

    Srivastava, A.K., Kontoudis, G.P., Sofge, D., Otte, M.: Distributed multi-robot in- formation gathering using path-based sensors in entropy-weighted Voronoi regions. In: International Symposium on Distributed Autonmous Robotic Systems (2022)

    work page 2022

  27. [27]

    In: International Symposium on Image and Signal Processing and Analysis

    Uney, M., Cetin, M.: Graphical model-based approaches to target tracking in sen- sor networks: An overview of some recent work and challenges. In: International Symposium on Image and Signal Processing and Analysis. pp. 492–497 (2007)

    work page 2007

  28. [28]

    In: IEEE Signal Processing, Communication and Applications Conference

    Uney, M., Cetin, M.: Target localization in acoustic sensor networks using factor graphs. In: IEEE Signal Processing, Communication and Applications Conference. pp. 1–4 (2008)

    work page 2008

  29. [29]

    In: American Control Conference

    Yang, Y., Minai, A.A., Polycarpou, M.M.: Decentralized cooperative search by networked UAVs in an uncertain environment. In: American Control Conference. vol. 6, pp. 5558–5563 (2004)

    work page 2004

  30. [30]

    Journal of Dynamic Systems, Measurement, and Control129(5), 716–728 (2007)

    Yang, Y., Polycarpou, M.M., Minai, A.A.: Multi-UAV cooperative search using an opportunistic learning method. Journal of Dynamic Systems, Measurement, and Control129(5), 716–728 (2007)

    work page 2007

  31. [31]

    Bioinformatics20(18), 3594–3603 (2004)

    Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to Bayesian network inference for generating causal networks from observational bio- logical data. Bioinformatics20(18), 3594–3603 (2004)

    work page 2004