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REVIEW 4 major objections 5 minor 54 references

Distributed Coverage Control for Time-Varying Spatial Processes

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A distributed Gaussian-process controller lets a robot team estimate and cover a changing spatial field, adapting exploration versus coverage as uncertainty grows.

desk verdict A genuinely new combination with broad validation, but the time-decay kernel is order-dependent and nearly inert at the reported settings — conditioning acceptance on fixing the mechanism. read the letter →

arxiv 2502.07595 v1 pith:GCMVX6SR submitted 2025-02-11 cs.RO

Federico Pratissoli , Mattia Mantovani , Amanda Prorok , Lorenzo Sabattini This is my paper · ORCID
classification cs.RO
keywords multi-robotsystemscoveragecontrolGaussianprocessregressiontime-varyingspatialfieldsexploration-exploitationtrade-offdistributedestimationdatafilteringenvironmentalmonitoring
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes a distributed control scheme in which a team of robots simultaneously learns an unknown spatial field, such as a pollution or temperature distribution, and moves to cover it. The central claim is that a Gaussian-process estimate of the field, combined with a dynamically weighted exploration term based on predictive uncertainty, lets the team track fields that change over time without any global coordinator. Each robot uses only its own samples plus data shared by neighbors, filters those samples by a confidence threshold, and lets old data age through an exponential decay model, so its dataset and computation stay bounded. Simulations built on real indoor sensor data and physical robot experiments support the claim that the team approaches the coverage quality of an oracle that knows the true field while outperforming random exploration, uniform coverage, and a stationary-field GP baseline.

What carries the argument

The load-bearing object is the substitute density function of equation (15), which replaces the unknown true density inside the limited-Voronoi coverage law. It couples estimation and coverage by combining the GP posterior mean and standard deviation with the user-set temporal weight $W_t=\tanh(\alpha t)$. The exponential decay factors in equations (19)-(23) age the covariance so stale samples lose influence, and the threshold filter of equations (25)-(26) prunes samples that no longer reduce uncertainty. Together these pieces let the team shift from exploration to exploitation and back as the estimate's confidence changes over time.

What would settle it

Run the control law on a two-peak field whose peaks swap positions abruptly, with $\tau$ set to a slow value such as $10^5$ and $\epsilon=10^{-4}$; if the team's coverage cost stays well above the oracle level for much longer than the transient shown in Fig. 12, or if any robot's dataset grows monotonically because the cleaning criterion of equation (26) never triggers, the time-varying uncertainty mechanism is not doing the claimed work.

Watch

Extended reading notes

Core claim

The paper's central discovery is that the coverage objective can be driven by a substitute density function $\phi'_t(x)=e^{\beta(x)}-1$, where $\beta(x)=\sigma_{t-1}(x)+W_t\,\mu_{t-1}(x)$, with $\mu$ and $\sigma$ the Gaussian-process posterior mean and standard deviation. This makes robots treat regions of high uncertainty as worth visiting during exploration while eventually concentrating where the estimated field is high during exploitation. For time-varying fields, the GP covariance is modified by exponential decay factors so that older samples contribute less and uncertainty grows in regions not recently sampled; that growing uncertainty is what re-triggers exploration after the field changes. A threshold-based filter keeps only samples whose predicted standard deviation exceeds a confidence bound, controlling dataset size and computational cost. The result, if correct, is a fully distributed controller that rebalances exploration and coverage on its own as the monitored process evolves.

Load-bearing premise

The method assumes the spatial field is smooth enough for a squared-exponential Gaussian process and that its changes over time are captured by the user-selected exponential forgetting rate; if the field changes abruptly or on very different timescales, the uncertainty signal that drives exploration will be miscalibrated and the team may fail to track it.

Editorial extensions

If this is right

  • A robot team could monitor dynamic environmental fields, such as pollution plumes, temperature, or salinity, without prior knowledge of the field and without a central computer.
  • The filtering strategy keeps per-robot datasets bounded, with the paper reporting up to 68.5% dataset reduction and 74.3% computation reduction compared with no filtering.
  • After a field change, the team automatically disperses to explore, updates its estimates, and re-converges to cover the new high-interest regions.
  • Per-robot estimates stay close to one another even under limited connectivity, with inter-robot estimate differences below about 10% in the reported simulations.
  • Coverage performance approaches that of an oracle with full knowledge of the field, and clearly beats random exploration, uniform coverage, and a state-of-the-art stationary-field GP coverage algorithm in the tested static case.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The controller can be read as a distributed, time-varying version of GP-UCB; adapting nonstationary bandit regret analysis could produce an exploration cost bound, but the paper does not attempt this.
  • Because the filter threshold is fixed, the bounded dataset size is only empirically demonstrated; an adaptive threshold keyed to the estimated uncertainty could turn this into a formal guarantee.
  • The exponential decay model is a modeling choice; the paper mentions a step-like decay for known event times, and comparing the two on the same data would reveal when the exponential assumption misleads the uncertainty signal.
  • A testable extension is to treat the decay rate $\tau$ as an online-estimated hyperparameter rather than a user preset, which could make the method responsive to fields whose rate of change is unknown.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a fully distributed multi-robot coverage controller for unknown, time-varying spatial fields. Each robot builds a Gaussian process estimate of the field from local samples and neighbor-shared data, forms a substitute density phi'_t = exp(sigma + W mu) - 1 with W = tanh(alpha t), and drives itself toward the centroid of its limited-range Voronoi cell under this density. Time variation is handled by an exponential forgetting mechanism applied to the GP covariance, and a sample filtering rule is used to keep datasets small. The method is validated through Python simulations with Intel Berkeley Lab data, Webots drone simulations, TurtleBot3 experiments, and comparisons with random exploration, plain coverage, an oracle coverage policy, and the approach of [33].

Significance. If the method is sound, it fills a genuine gap: prior GP-based multi-robot coverage work mostly assumes a static density, while known-time-varying-density coverage laws do not include online estimation. The paper's broad empirical validation is a clear strength: multiple simulation settings, real robot experiments, comparison against an oracle, explicit dataset-size and computation-time metrics, and a comparison with prior work. The main caveats are that the core acquisition function and time-decay mechanism are heuristic, with no formal convergence, tracking, or dataset-boundedness guarantees, and the paper itself acknowledges several of these limitations in Sections VIII and IX.A.

major comments (4)
  1. [§VII, Eqs. (19)–(20)] The time-decay construction is not invariant to the ordering of samples in a robot's dataset. D_t in Eq. (20) uses |i-j|, where i and j are positions in the dataset array, and d_t uses N+1-i. Algorithm 2 builds datasets by unions (lines 4–7) and does not prescribe any ordering rule, so the same multiset of timestamped measurements can produce different covariance matrices and different posterior variances sigma(x) on different robots. Since sigma(x) is the exploration signal in Eq. (15), the controller behavior can depend on arbitrary bookkeeping rather than on physical data content. The authors should either prove that a consistent chronological ordering is maintained or replace the array-index terms with timestamp-based quantities.
  2. [§VII, Eqs. (21)–(23)] The temporal kernel is not a well-defined function of the observation timestamps. Eq. (23) sets T_D_{i,j} = T_d_i * T_d_j for i != j, with T_d_i = e^{-(t-t_i)/tau} depending on the current absolute time t, not on the time difference between samples. For a fixed dataset, the training covariance therefore changes as t advances in a way that cannot be represented as Bayesian conditioning on fixed observations; the 'GP' in Eqs. (17)–(18) is not a consistent prior over the observed data. This may be a valid online heuristic, but the paper should state so explicitly and should provide at least a formal statement of what property the forgetting mechanism is intended to guarantee (e.g., bounded prediction error under a Lipschitz time-evolution model).
  3. [§IV–§VI, Eqs. (2)–(6), (15)] The coverage gradient in Eq. (4) is derived for a fixed, known density function, but the substitute density phi'_t in Eq. (15) is computed from a GP trained on robot positions and therefore depends on p_i through the data-collection process. The paper applies the standard centroid law without accounting for this dependence and provides no convergence or stability analysis for the coupled estimation–coverage system. The claims in Section X about 'near-oracle' performance and the characterization of the method in Abstract/Conclusion should be framed as empirical results, and the paper should state clearly what theoretical property, if any, is guaranteed.
  4. [§VIII, §XI] The paper explicitly acknowledges in Section VIII that no formal guarantee is provided that the dataset size will always decrease and that no theoretical bound is provided, and in Section IX.A that the methodology is sensitive to hyperparameter choices. Despite this, the Abstract and Section XI state that the method 'efficiently manages the data volume' and maintains 'a bounded training set'. These claims should be qualified to match the acknowledged heuristic nature of the filtering and forgetting mechanisms, or the authors should provide a formal bound under the stated assumptions.
minor comments (5)
  1. [§IX.D, Fig. 5] The text and figure caption refer to a 'light gay plot'; this should be 'light gray plot'.
  2. [References] Reference [21] appears corrupted, containing the placeholder text '1foldr Import 2019-10-08 Batch 6'; please replace it with the correct bibliographic entry.
  3. [§X, Figs. 11–12] The caption text says 'mean ad the standard deviation'; this should be 'mean and the standard deviation'.
  4. [§X] The paper describes the comparison method [33] as a 'centralized Voronoi approach,' but the cited title is 'Decentralized learning with limited communications...' and the surrounding text earlier describes it as decentralized; please clarify which property is intended.
  5. [§IX.A] The simulation setup reports the hyperparameter values but does not describe the schedule or method used to optimize GP hyperparameters online (e.g., how often Eq. (14) is maximized, what optimizer is used, or warm-starting details). Adding these details would improve reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the temporal-kernel sample-ordering issue is a correctness concern, not circular reasoning.

full rationale

The proposed pipeline is not circular. The coverage controller (6) is the standard limited-Voronoi gradient law, the GP posterior (11)-(14) is standard regression, and the exploration-exploitation density (15) plus the time-decay kernels (17)-(23) are presented and used as a heuristic control law rather than as a derivation whose conclusion is contained in its assumptions. The performance evaluation in Figs. 11-13 uses external yardsticks (oracle coverage with the true field, plain coverage, random exploration, and [33]) and computes H using the true spatial process, so the favorable comparison is not forced by construction. The only self-citation, [9], supplies limited-range Voronoi machinery, but the same construction is standard and anchored to external references [8] and [39], so it is not load-bearing for the paper's central time-varying estimation-coverage claim. The paper also states limitations honestly, including 'formal guarantees are not provided that the dataset size will always decrease' and 'developed and tested solely in convex environments.' A genuine correctness risk exists: D_t in Eq. (20) depends on dataset-array indices |i-j|, and T_D in Eq. (23) depends on current time t, so the uncertainty signal can vary with sample ordering and merging in the distributed implementation; however, this is an internal-consistency and robustness issue, not a circular derivation.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The central claim depends entirely on the GP model and the user-chosen time-decay and filtering hyperparameters; there are no new physical entities. The smoothness assumption, the exponential-forgetting model, the UCB-style acquisition function, and the threshold filter are all unproved design choices, so they are listed as axioms. The most evidence-sensitive parameters are alpha, tau, epsilon, e_a, and e_r, which are set per scenario rather than derived.

free parameters (7)
  • alpha (alpha) = 0.1 in most simulations; user-selected
    Controls the exploration schedule W_t = tanh(alpha*t) in Eq. (16); the paper provides no criterion for choosing it and claims incorrect choices still work, which is not substantiated.
  • e_a (sample inclusion error tolerance) = 0.03 to 0.04 in simulations
    Threshold in Eq. (25) determines whether a new sample is added to the GP training set; user-selected, directly affects estimation accuracy and dataset size.
  • e_r (sample removal error tolerance) = 0.04 to 0.05 in simulations
    Threshold in Eq. (26) determines when an old sample is removed; user-selected and coupled to the time-decay mechanism.
  • epsilon (epsilon) for time-decay covariance = 1e-4 in time-varying cases; 1e-100 in time-invariant cases
    Governs the D_t matrix in Eq. (20) that downweights old sample correlations; user-selected per scenario.
  • tau (tau) for exponential decay = 1e5 in time-varying cases; 1e100 in time-invariant cases
    Time constant in Eq. (22); with tau=1e5 over a 300-step experiment the decay factor remains above 0.997, so the reported time-varying runs barely exercise the forgetting mechanism.
  • GP kernel hyperparameters lambda, sigma_f, sigma_nu = estimated online via marginal likelihood maximization
    Eq. (14): hyperparameters are fitted to the collected data each time the GP is retrained; values are data-dependent, not fixed.
  • Time-decay function shape = exponential in all reported experiments; step-like optional
    The paper asserts exponential decay is typically appropriate (Section VII and IX-A); the choice is a modeling assumption with its own parameters.
assumptions (7)
  • domain assumption The spatial process is smooth and can be represented by a GP with a squared exponential kernel.
    Stated as Assumption 3 in Section IV and used in Eq. (10); the entire estimation procedure rests on this smoothness assumption.
  • standard math Limited-range Voronoi coverage control converges to the weighted centroid configuration for a known density.
    Borrowed from [9] and used in Eqs. (4)-(6); the proposed controller inherits this convergence property for the known-density case, not for the learned substitute density.
  • domain assumption Measurement noise is i.i.d. zero-mean Gaussian with variance sigma_nu^2.
    Eq. (8); standard for GPs, but if noise is non-Gaussian or heteroscedastic, the uncertainty estimates are mis-calibrated.
  • ad hoc to paper An exponential time-decay model with user-chosen tau and epsilon correctly captures how the usefulness of samples decreases over time.
    Eqs. (19)-(22); no evidence that this model matches the target processes, and the paper states that even for nonexponential processes it remains effective with a potentially slower response.
  • ad hoc to paper The substitute density phi'_t = exp(sigma + W mu) - 1 with W = tanh(alpha t) yields an appropriate exploration-exploitation balance for coverage control.
    Eqs. (15)-(16); a design choice inspired by UCB, with no regret, convergence, or tracking analysis. The reported experiments tune alpha.
  • ad hoc to paper The threshold criteria (25)-(26) based on sigma >= e/z_N * mu_max preserve estimation accuracy while bounding dataset size.
    Section VIII; the paper acknowledges no theoretical bound on dataset size and offers only simulation evidence.
  • domain assumption The environment is a convex polytope without obstacles, and communication and sensing are limited but always available locally.
    Problem statement in Section IV and limitation in Section XI; non-convex environments and obstacles are left to future work.

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Cite this review

Pith. "Pith review of Distributed Coverage Control for Time-Varying Spatial Processes." pith.science (2026). https://pith.science/paper/GCMVX6SR

@misc{pith2026250207595,
  author       = {Pith},
  title        = {Pith review of: Distributed Coverage Control for Time-Varying Spatial Processes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GCMVX6SR}},
  note         = {Machine review of arXiv:2502.07595}
}
read the original abstract

Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi-robot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian Processes (GPs) to model the spatial field and balance the trade-off between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where the exploration-exploitation trade-off is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.

Figures

Figures reproduced from arXiv: 2502.07595 by the authors.

Figure 1
Figure 1. Comparison between a time invariant and time variant approach with a 1-D signal estimation. We presume the 6 samples are tidily taken from left [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. The figure illustrates the control architecture implemented on each robot. Each robot implements the coverage control strategy by utilizing the locations [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. The figure presents three subfigures, each corresponding to a different [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The figure presents five subfigures, each corresponding to a different [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: This figure shows the RMSE of robot estimates during a simulation where robots explore an environment with a dynamic spatial process. At time step 60 (black dashed line), the process shifts, causing a temporary RMSE increase as outdated data is filtered out. Over time,…
Figure 6
Figure 6. Figure 6: The figure shows two different subfigures, one for each time step, of [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: This plot compares the effect of the proposed filter strategy versus [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 9
Figure 9. Figure 9: The illustration depicts a team of drones assigned to assess smoke [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 11
Figure 11. Figure 11: The figure shows the performance comparison between the proposed [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: The figure shows the performance comparison between the proposed [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]

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Reference graph

Works this paper leans on

54 extracted references · 51 canonical work pages

  1. [33]

    Decentralized learning with limited communications for multi-robot coverage of unknown 16 spatial fields,

    K. Nakamura, M. Santos, and N. E. Leonard, “Decentralized learning with limited communications for multi-robot coverage of unknown 16 spatial fields,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 9980–9986

    work page 2022

  2. [1]

    Distributed search and rescue with robot and sensor teams,

    G. Kantor, S. Singh, R. Peterson, D. Rus, A. Das, V . Kumar, G. Pereira, and J. Spletzer, “Distributed search and rescue with robot and sensor teams,” in Field and Service Robotics . Springer, 2003, pp. 529–538

    work page 2003

  3. [2]

    Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots,

    A. Barrientos, J. Colorado, J. d. Cerro, A. Martinez, C. Rossi, D. Sanz, and J. Valente, “Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots,” Journal of Field Robotics , vol. 28, no. 5, pp. 667–689, 2011

    work page 2011

  4. [3]

    Development of a high-resolution aerial remote-sensing system for precision agriculture,

    N. Bagheri, “Development of a high-resolution aerial remote-sensing system for precision agriculture,” International journal of remote sens- ing, vol. 38, no. 8-10, pp. 2053–2065, 2017

    work page 2017

  5. [4]

    Towards environmental monitoring with mobile robots,

    M. Trincavelli, M. Reggente, S. Coradeschi, A. Loutfi, H. Ishida, and A. J. Lilienthal, “Towards environmental monitoring with mobile robots,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2008, pp. 2210–2215

    work page 2008

  6. [5]

    Robots for environmental monitoring: Significant advancements and applications,

    M. Dunbabin and L. Marques, “Robots for environmental monitoring: Significant advancements and applications,” IEEE Robotics & Automa- tion Magazine, vol. 19, no. 1, pp. 24–39, 2012

    2012

  7. [6]

    Active drifters: Towards a practical multi-robot system for ocean monitoring,

    A. Molchanov, A. Breitenmoser, and G. S. Sukhatme, “Active drifters: Towards a practical multi-robot system for ocean monitoring,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 545–552

    work page 2015

  8. [7]

    Distributed multirobot exploration and mapping,

    D. Fox, J. Ko, K. Konolige, B. Limketkai, D. Schulz, and B. Stewart, “Distributed multirobot exploration and mapping,” Proceedings of the IEEE, vol. 94, no. 7, pp. 1325–1339, 2006

    work page 2006

Show all 54 references
  1. [8]

    Coverage control for mobile sensing networks,

    J. Cortes, S. Martinez, T. Karatas, and F. Bullo, “Coverage control for mobile sensing networks,” IEEE Transactions on robotics and Automation, vol. 20, no. 2, pp. 243–255, 2004

    2004

  2. [9]

    On coverage control for limited range multi-robot systems,

    F. Pratissoli, B. Capelli, and L. Sabattini, “On coverage control for limited range multi-robot systems,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 9957–9963

    2022

  3. [10]

    Coverage control for multi-robot teams with heterogeneous sensing capabilities using limited communications,

    M. Santos and M. Egerstedt, “Coverage control for multi-robot teams with heterogeneous sensing capabilities using limited communications,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 5313–5319

    2018

  4. [11]

    Multirobot control using time-varying density functions,

    S. G. Lee, Y . Diaz-Mercado, and M. Egerstedt, “Multirobot control using time-varying density functions,” IEEE Transactions on robotics, vol. 31, no. 2, pp. 489–493, 2015

    2015

  5. [12]

    Distributed cooperative coverage control of sensor networks,

    W. Li and C. G. Cassandras, “Distributed cooperative coverage control of sensor networks,” in Proceedings of the 44th IEEE Conference on Decision and Control (CDC) . IEEE, 2005, pp. 2542–2547

    2005

  6. [13]

    Informative planning and online learning with sparse gaussian processes,

    K.-C. Ma, L. Liu, and G. S. Sukhatme, “Informative planning and online learning with sparse gaussian processes,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 4292–4298

    2017

  7. [14]

    Multi-robot active information gathering with periodic communication,

    M. Lauri, E. Heinänen, and S. Frintrop, “Multi-robot active information gathering with periodic communication,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 851–856

    2017

  8. [15]

    Robotic active information gathering for spatial field reconstruction with rapidly-exploring random trees and online learning of gaussian processes,

    A. Viseras, D. Shutin, and L. Merino, “Robotic active information gathering for spatial field reconstruction with rapidly-exploring random trees and online learning of gaussian processes,” Sensors, vol. 19, no. 5, p. 1016, 2019

    2019

  9. [16]

    Rapidly- exploring adaptive sampling tree*: A sample-based path-planning algo- rithm for unmanned marine vehicles information gathering in variable ocean environments,

    C. Xiong, H. Zhou, D. Lu, Z. Zeng, L. Lian, and C. Yu, “Rapidly- exploring adaptive sampling tree*: A sample-based path-planning algo- rithm for unmanned marine vehicles information gathering in variable ocean environments,” Sensors, vol. 20, no. 9, p. 2515, 2020

    2020

  10. [17]

    Path planning for underwater information gathering based on genetic algorithms and data stochastic models,

    M. Bresciani, F. Ruscio, S. Tani, G. Peralta, A. Timperi, E. Guerrero- Font, F. Bonin-Font, A. Caiti, and R. Costanzi, “Path planning for underwater information gathering based on genetic algorithms and data stochastic models,” Journal of Marine Science and Engineering , vol. ...

    2021

  11. [18]

    Adaptive visual information gathering for autonomous exploration of underwater environments,

    E. Guerrero, F. Bonin-Font, and G. Oliver, “Adaptive visual information gathering for autonomous exploration of underwater environments,” IEEE Access, vol. 9, pp. 136 487–136 506, 2021

    2021

  12. [19]

    Path planning of mul- tiple autonomous marine vehicles for adaptive sampling using voronoi- based ant colony optimization,

    C. Xiong, D. Chen, D. Lu, Z. Zeng, and L. Lian, “Path planning of mul- tiple autonomous marine vehicles for adaptive sampling using voronoi- based ant colony optimization,” Robotics and Autonomous Systems , vol. 115, pp. 90–103, 2019

    2019

  13. [20]

    Asynchronous adaptive sampling and reduced-order modeling of dynamic processes by robot teams via intermittently connected networks,

    H. Rovina, T. Salam, Y . Kantaros, and M. A. Hsieh, “Asynchronous adaptive sampling and reduced-order modeling of dynamic processes by robot teams via intermittently connected networks,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE,...

    2020

  14. [21]

    Efficient computation of gaussian process re- gression for large spatial data sets by patching local gaussian processes,

    C. Park and J. Z. Huang, “Efficient computation of gaussian process re- gression for large spatial data sets by patching local gaussian processes,” 1foldr Import 2019-10-08 Batch 6 , 2016

    2019

  15. [22]

    Deep gaussian processes: A survey,

    K. Jakkala, “Deep gaussian processes: A survey,” arXiv preprint arXiv:2106.12135, 2021

    2021 arXiv

  16. [23]

    Mobile sensor network navigation using gaussian processes with truncated observations,

    Y . Xu, J. Choi, and S. Oh, “Mobile sensor network navigation using gaussian processes with truncated observations,” IEEE Transactions on Robotics, vol. 27, no. 6, pp. 1118–1131, 2011

    2011

  17. [24]

    Mobile sensor networks and control: Adaptive sampling of spatiotemporal processes,

    D. A. Paley and A. Wolek, “Mobile sensor networks and control: Adaptive sampling of spatiotemporal processes,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 3, pp. 91–114, 2020

    2020

  18. [25]

    Adaptive sampling and online learning in multi- robot sensor coverage with mixture of gaussian processes,

    W. Luo and K. Sycara, “Adaptive sampling and online learning in multi- robot sensor coverage with mixture of gaussian processes,” in2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 6359–6364

    2018

  19. [26]

    Distributed environmental modeling and adaptive sampling for multi-robot sensor coverage,

    W. Luo, C. Nam, G. Kantor, and K. Sycara, “Distributed environmental modeling and adaptive sampling for multi-robot sensor coverage,” in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) , 2019, pp. 1488–1496

    2019

  20. [27]

    Decentralized multi-agent exploration with online- learning of gaussian processes,

    A. Viseras, T. Wiedemann, C. Manss, L. Magel, J. Mueller, D. Shutin, and L. Merino, “Decentralized multi-agent exploration with online- learning of gaussian processes,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 4222–4229

    2016

  21. [28]

    Multi-robot active sensing and environmental model learning with distributed gaussian process,

    D. Jang, J. Yoo, C. Y . Son, D. Kim, and H. J. Kim, “Multi-robot active sensing and environmental model learning with distributed gaussian process,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5905– 5912, 2020

    2020

  22. [29]

    Multi-robot gaussian process estimation and coverage: Deterministic sequencing algorithm and regret analysis,

    L. Wei, A. McDonald, and V . Srivastava, “Multi-robot gaussian process estimation and coverage: Deterministic sequencing algorithm and regret analysis,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 9080–9085

    2021

  23. [30]

    Robust adaptive coverage for robotic sensor networks,

    M. Schwager, M. P. Vitus, D. Rus, and C. J. Tomlin, “Robust adaptive coverage for robotic sensor networks,” in Robotics Research. Springer, 2017, pp. 437–454

    2017

  24. [31]

    Multi- robot learning and coverage of unknown spatial fields,

    M. Santos, U. Madhushani, A. Benevento, and N. E. Leonard, “Multi- robot learning and coverage of unknown spatial fields,” in 2021 Inter- national Symposium on Multi-Robot and Multi-Agent Systems (MRS) . IEEE, 2021, pp. 137–145

    2021

  25. [32]

    Multi-robot coordination for estimation and coverage of unknown spatial fields,

    A. Benevento, M. Santos, G. Notarstefano, K. Paynabar, M. Bloch, and M. Egerstedt, “Multi-robot coordination for estimation and coverage of unknown spatial fields,” in 2020 ieee international conference on robotics and automation (ICRA) . IEEE, 2020, pp. 7740–7746

    2020

  26. [34]

    Generalized coverage control for time-varying density functions,

    J. Kennedy, A. Chapman, and P. M. Dower, “Generalized coverage control for time-varying density functions,” in 2019 18th European Control Conference (ECC). IEEE, 2019, pp. 71–76

    2019

  27. [35]

    Decentralized minimum-energy coverage control for time-varying density functions,

    M. Santos, S. Mayya, G. Notomista, and M. Egerstedt, “Decentralized minimum-energy coverage control for time-varying density functions,” in 2019 international symposium on multi-robot and multi-agent systems (MRS). IEEE, 2019, pp. 155–161

    2019

  28. [36]

    Coverage control with information decay in dynamic environments,

    N. Hübel, S. Hirche, A. Gusrialdi, T. Hatanaka, M. Fujita, and O. Sawodny, “Coverage control with information decay in dynamic environments,” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 4180– 4185, 2008

    2008

  29. [37]

    An improved kf-rbf based esti- mation algorithm for coverage control with unknown density function,

    L. Zuo, M. Yan, Y . Guo, and W. Ma, “An improved kf-rbf based esti- mation algorithm for coverage control with unknown density function,” Complexity, vol. 2019, 2019

    2019

  30. [38]

    Dynamic coverage meets regret: Unifying two control performance measures for mobile agents in spatiotemporally varying environments,

    B. Haydon, K. D. Mishra, P. Keyantuo, D. Panagou, F. Chow, S. Moura, and C. Vermillion, “Dynamic coverage meets regret: Unifying two control performance measures for mobile agents in spatiotemporally varying environments,” in 2021 60th IEEE Conference on Decision and Control (...

    2021

  31. [39]

    Spatial tessellations,

    A. Okabe, “Spatial tessellations,” International Encyclopedia of Geogra- phy: People, the Earth, Environment and Technology: People, the Earth, Environment and Technology, pp. 1–11, 2016

    2016

  32. [40]

    Distributed coverage control with sensory feedback for networked robots

    M. Schwager, J. McLurkin, and D. Rus, “Distributed coverage control with sensory feedback for networked robots.” in robotics: science and systems, 2006, pp. 49–56

    2006

  33. [41]

    Obstacle avoidance of non- holonomic unicycle robots based on fluid mechanical modeling,

    R. Soukieh, I. Shames, and B. Fidan, “Obstacle avoidance of non- holonomic unicycle robots based on fluid mechanical modeling,” in Proceedings of the European Control Conference (ECC) , 2009

    2009

  34. [42]

    Semiautonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles,

    D. Lee, A. Franchi, H. Son, C. Ha, H. Bulthoff, and P. Robuffo Giordano, “Semiautonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles,” IEEE/ASME Transactions on Mechatronics , vol. 18, no. 4, pp. 1334–1345, Aug 2013

    2013

  35. [43]

    C. E. Rasmussen, C. K. Williams et al., Gaussian processes for machine learning. Springer, 2006, vol. 1

    2006

  36. [44]

    A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions,

    E. Schulz, M. Speekenbrink, and A. Krause, “A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions,” Journal of Mathematical Psychology , vol. 85, pp. 1–16, 2018

    2018

  37. [45]

    Gaussian processes for learning and control: A tutorial with examples,

    M. Liu, G. Chowdhary, B. C. Da Silva, S.-Y . Liu, and J. P. How, “Gaussian processes for learning and control: A tutorial with examples,” IEEE Control Systems Magazine , vol. 38, no. 5, pp. 53–86, 2018

    2018

  38. [46]

    Finite-time analysis of the multiarmed bandit problem,

    P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,” Machine learning, vol. 47, no. 2, pp. 235– 256, 2002

    2002

  39. [47]

    Time-varying gaussian process bandit optimization,

    I. Bogunovic, J. Scarlett, and V . Cevher, “Time-varying gaussian process bandit optimization,” in Artificial Intelligence and Statistics . PMLR, 2016, pp. 314–323

    2016

  40. [48]

    Kernel recursive least-squares tracker for time-varying regression,

    S. Van Vaerenbergh, M. Lázaro-Gredilla, and I. Santamaría, “Kernel recursive least-squares tracker for time-varying regression,” IEEE Trans- actions on Neural Networks and Learning Systems , vol. 23, no. 8, pp. 1313–1326, 2012

    2012

  41. [49]

    Confidence and tolerance intervals for the normal distribu- tion,

    F. Proschan, “Confidence and tolerance intervals for the normal distribu- tion,” Journal of the American Statistical Association , vol. 48, no. 263, pp. 550–564, 1953

    1953

  42. [50]

    Confidence interval estimation of a normal percentile,

    S. Chakraborti and J. Li, “Confidence interval estimation of a normal percentile,” The American Statistician, vol. 61, no. 4, pp. 331–336, 2007

    2007

  43. [51]

    Intel lab data,

    P. Bodik, W. Hong, C. Guestrin, S. Madden, M. Paskin, and R. Thibaux, “Intel lab data,” https://db.csail.mit.edu/labdata/labdata.html

  44. [52]

    Cyberbotics ltd. webots™: professional mobile robot simu- lation,

    O. Michel, “Cyberbotics ltd. webots™: professional mobile robot simu- lation,” International Journal of Advanced Robotic Systems, vol. 1, no. 1, p. 5, 2004

    2004

  45. [53]

    Multi-robot informa- tive and adaptive planning for persistent environmental monitoring,

    K.-C. Ma, Z. Ma, L. Liu, and G. S. Sukhatme, “Multi-robot informa- tive and adaptive planning for persistent environmental monitoring,” in Distributed Autonomous Robotic Systems: The 13th International Symposium (DARS). Springer, 2018, pp. 285–298. Federico Pratissoli (Graduat...

    2018

  46. [2012]

    He has been an Associate Professor with the Depart- ment of Sciences and Methods for Engineering, Uni- versity of Modena and Reggio Emilia, since 2018

    In 2010, he was a Visiting Researcher with the University of Maryland, College Park, MD, USA. He has been an Associate Professor with the Depart- ment of Sciences and Methods for Engineering, Uni- versity of Modena and Reggio Emilia, since 2018. His research interests include ...

    2010

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.