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REVIEW 5 major objections 4 minor 38 references

Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces

T0 review · 5 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper proposes an event-based reconfiguration controller that switches a robot swarm between its task formation and a single-file tailgating configuration to pass narrow spaces, and proves the switched system converges asymptotically.

desk verdict A practical, code-backed two-mode reconfiguration controller for swarm navigation, but Theorem 1 overclaims and the Lyapunov proof doesn't cover the implemented controller. read the letter →

arxiv 2505.16087 v1 pith:KT6RB5MN submitted 2025-05-22 cs.RO

Duy-Nam Bui , Manh Duong Phung , Hung Pham Duy This is my paper · ORCID
classification cs.RO
keywords multi-robotsystemtime-varyingformationreconfigurationcontrolswarmroboticsevent-triggeredartificialpotentialfieldnarrowspacenavigationLyapunovstability
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 an event-based reconfiguration controller that lets a robot swarm change its formation while moving through narrow passages such as valleys, tunnels, and corridors. The controller has two modes: a formation mode that holds the desired task shape, and a tailgating mode in which robots line up single-file to pass through gaps too narrow for the full shape. Each robot decides which mode to use from its own local sensor readings, so the decision is distributed rather than centralized. The paper proves with Lyapunov functions that, under the proposed control law, the time-varying formation converges asymptotically to the desired task configuration. Simulation comparisons and software-in-the-loop tests are reported as supporting evidence.

What carries the argument

The load-bearing object is the event-triggering mode switch around control law (14), which sums behavior velocities and selects between the formation term $v^f_i$ and the tailgating term $v^t_i$ depending on the detected environment width. The formation term uses a scaled task configuration $\kappa\delta^*$, and the tailgating term makes each robot follow the nearest robot in front at a desired distance $d_{\mathrm{ref}}$. The event trigger computes the environment width $w_e$ from the left and right nearest obstacle points and compares it with the threshold $\lambda r$. Stability is carried by Lyapunov functions for each behavior, with the formation-mode proof relying on the Laplacian matrix of the sensing graph $G$ and the identity $H B = n B$ for the bias vector.

What would settle it

Run control law (14) with robots whose communication range is too short to keep the graph fully connected, and record whether the formation still converges to the task configuration; a divergence in that condition would falsify the claim that the TVF converges as stated, because the theorem's proof explicitly depends on the fully connected Laplacian.

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Extended reading notes

Core claim

The paper's central claim is that a single distributed control law can make a robot swarm reconfigure from a task formation into a safe single-file configuration and back, so that the formation survives narrow passages. The law combines five potential-field behaviors: formation, tailgating, migration, inter-agent avoidance, and obstacle avoidance. An event-triggering rule uses each robot's local range data to estimate the width of the environment and the width of the current formation; when the passage width drops below a threshold, the robot switches to tailgating, and the scaling factor $\kappa$ contracts the formation. Theorem 1 states that under control law (14) the time-varying formation described by (1) asymptotically converges to the desired task configuration. The proof builds separate Lyapunov functions for the formation, tailgating, and collision-avoidance behaviors and combines them mode by mode.

Load-bearing premise

The proof assumes the sensing and communication graph is fully connected, so every robot effectively sees every other robot; in a larger or more sparse swarm that assumption can fail and the Lyapunov argument no longer applies.

Editorial extensions

If this is right

  • A swarm using the ERC can pass through a corridor without a central planner, with each robot choosing formation or tailgating mode from its own sensor data.
  • The same control law covers nominal formation flight and the single-file safe configuration, so no separate switching controller is needed.
  • The formation contracts by the scaling factor $\kappa = (w_e - 2r)/w_f$ when the passage is wider than the safety margin but narrower than the task shape, so reconfiguration happens continuously rather than only at the trigger threshold.
  • If the convergence theorem holds, the swarm's shape is restored after the passage, so the task configuration is not lost during reconfiguration.

Reading between the lines

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

  • The controller's reliance on a fully connected graph means the published theorem is really a guarantee for small or densely communicating swarms; extending the Lyapunov argument to switching or disconnected topologies would be the natural next step and would make the stability claim match the paper's stated decentralization.
  • The same event-triggered shrinking-and-single-file mechanism could transfer to ground vehicles or heterogeneous robots, since the controller only needs local range data and a rule for picking a front leader; the paper does not test this.
  • The threshold $\lambda r$ and the desired tailgating distance $d_{\mathrm{ref}}$ together define an implicit safety-speed trade-off; sweeping them across passage widths could produce a design curve for choosing how early a swarm abandons its task shape, which the paper does not explore.
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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

5 major / 4 minor

Summary. The manuscript proposes an event-based reconfiguration controller (ERC) for a swarm of robots navigating narrow spaces. Each robot runs a local algorithm that selects between a formation mode and a tailgating mode on the basis of sensed corridor width, using artificial-potential-field behaviors for migration, formation maintenance, tailgating, inter-agent avoidance, and obstacle avoidance. The authors prove, in Theorem 1, that the time-varying formation asymptotically converges to the desired task configuration under control law (14). They report simulations in forest-like and cave-like environments, comparisons with APF and IAPF baselines, and software-in-the-loop tests with three Hummingbird UAVs. The central theoretical claim, however, is not established by the proof as written, and the paper's simulation and SIL results should be understood as empirical demonstrations rather than as consequences of the stated theorem.

Significance. If the theorem were correct, the paper would offer a distributed, event-triggered alternative to centralized reconfiguration planning, with a relatively simple potential-field controller and a public implementation. The empirical comparison is useful: the ERC achieves a higher success rate than the APF/IAPF baselines in the tested scenarios, and the code is publicly available. The weakness is that the main advertised guarantee, the asymptotic convergence to the task configuration, does not follow from the supplied Lyapunov analysis; the proof analyzes individual behaviors in isolation and does not handle the actual switched, acceleration-level, locally-sensed system. Because the theoretical contribution is load-bearing for the paper's framing, the manuscript needs substantial revision of the stability analysis or a correspondingly weakened theorem before it can be accepted.

major comments (5)
  1. [§3.3, Theorem 1] Theorem 1 claims that under control law (14) the TVF asymptotically converges to the desired task configuration. The proof's own tailgating analysis, Eqs. (26)-(28), concludes only that robot i aligns behind its leader at distance d_ref in a straight-line configuration. Thus the invariant set of tailgating mode is the safe line configuration, not the desired task shape; in a long corridor where robots remain in tailgating mode, Theorem 1 is false as stated.
  2. [§3.3, Eqs. (20)-(25) and (35)-(36)] The proof analyzes a first-order, velocity-level closed loop ˙P = −k_f H P + B with a constant κ and no switching, whereas the actual controller is executed at acceleration level (Eqs. (17)-(19)), includes the always-active migration and avoidance terms (9)-(13), and has piecewise-constant, robot-dependent κ from Algorithm 1. The separate negativities of ˙V_F and ˙V_T in (36) do not constitute a Lyapunov or LaSalle argument for the coupled switched system with mode transitions driven by local sensing. The theorem is therefore unsubstantiated for the controller actually proposed.
  3. [§3.3, Eq. (21)] The proof requires the sensing/communication graph G to be fully connected ('As G is fully connected') so that the Laplacian has the stated spectrum and HB=nB holds. This contradicts the decentralized description in Remark 2 and Section 2.1, where each robot uses only local sensors and peer communications; for a general graph the spectrum is not as claimed, and the convergence argument does not carry over to larger swarms or limited communication ranges.
  4. [§3.3, Eqs. (31)-(34)] The collision-avoidance Lyapunov computation is incomplete. The derivative ˙V_i in (31) contains (v_i − v_j), but the substitution into (33) uses only robot i's avoidance contribution and ignores the leader/tailgating, migration, and avoidance control of robot j and of all other active terms. Consequently the claimed negativity ˙V_i < 0 is not established for the full coupled system with control law (14).
  5. [§3.2 and §4.2, Eq. (6) and Fig. 8] The implementation uses a per-robot scaling factor κ, as Fig. 8 explicitly states, while Eq. (6) is written with a single κ multiplying the relative offsets. If each robot uses its own κ_i, the desired offsets are not those of a common scaled formation, and the formation-error term in (20) needs to be re-examined. The manuscript should clarify how a shared formation shape is encoded when the mode decision and κ are evaluated locally.
minor comments (4)
  1. [§4.3] In the comparison paragraph, 'AFP' appears to be a typo for 'APF'.
  2. [§2.2, Definition 2] The formation condition (3) is written as a sum of norms tending to zero; this is mathematically equivalent to requiring all robots to have the same offset p_i − δ_i, but the notation should be clarified so that readers do not mistake it for a statement forcing all δ_i equal.
  3. [§3.2, Algorithm 1] The pseudocode returns a desired velocity but the dynamics (1) require acceleration; the connection to Eq. (17) should be stated more explicitly in the algorithm itself, since the proof in Section 3.3 operates at the velocity level.
  4. [§4.4] The SIL test is reported with three UAVs only; a sentence explaining why this small scale is representative of the 5-robot simulation results would improve the validation discussion.

Circularity Check

0 steps flagged · score 2.0 of 10

No construction-level circularity: the Lyapunov proof and external benchmarks are independent; Theorem 1 overclaims but does not reduce to its own inputs.

full rationale

The central claim, Theorem 1, is that control law (14) makes the TVF asymptotically converge to the desired task configuration. The proof is a direct Lyapunov/LaSalle argument conducted separately for the formation, tailgating, and collision-avoidance behaviors. No equation is defined in terms of the theorem's conclusion, and no fitted parameter is renamed as a prediction. The desired formation appears as the equilibrium of the formation dynamics because the control term (6) was deliberately designed with the δ offsets, but designing a controller so that its target is an equilibrium is standard construction, not circularity. The theorem's proof does contain gaps: it assumes a fully connected graph before Eq. (21), it explicitly omits the migration term ('except the migration behavior v_m^i due to its constant impact'), and the mode-wise negativities in (36) do not by themselves prove convergence of the coupled switched system. The tailgating analysis also establishes only a straight-line invariant set, not the task configuration. These are overclaiming or proof-completeness issues, not circular reduction. The self-citations in the paper (e.g., [14], [16], [19], [36]) are background, related-work, or implementation references; they do not supply a uniqueness theorem or carry the stability argument. The comparisons against APF and IAPF and the RotorS-based software-in-the-loop tests provide external, non-circular evidence. Overall, the derivation chain is not circular; the appropriate concern is correctness of the stability claim, not circularity.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities; all behaviors are standard APF terms. The central theoretical claim rests on the fully connected graph assumption, the local width estimator, and the unstated rule for adjusting the migration direction inside narrow passages.

free parameters (7)
  • kf (formation gain)
    Hand-chosen control gain scaling the formation attraction in Eq. (6); no value or tuning procedure reported.
  • kt (tailgating gain)
    Hand-chosen gain in Eq. (8); no value or tuning procedure reported.
  • ki and ko (avoidance gains)
    Hand-chosen gains in Eqs. (10)-(11); no values reported.
  • vref (preferred migration speed)
    Reference speed in Eq. (9); no value reported in the evaluation setup.
  • dref (tailgating distance) = 1 m
    Desired leader-follower distance in Eq. (8), set to 1 m in Section 4.1.
  • lambda (mode-switch threshold)
    Threshold in Eq. (16) and Algorithm 1; only constrained as lambda > 2, no explicit value.
  • ra (alert radius) = 0.9 m (3r)
    Activation range for repulsive behaviors; chosen as 3r in Section 4.1.
assumptions (4)
  • domain assumption The sensing/communication graph G is fully connected.
    Invoked in Section 3.3 (Eq. 21) for the Laplacian spectrum; not guaranteed by the local-sensing description.
  • domain assumption The environment width can be inferred from the two closest obstacle points on each side of the robot.
    Used in Eq. (15) and Algorithm 1; can be inaccurate in non-convex or cluttered passages.
  • domain assumption The migration direction uref can be steered along the environment boundary while in a narrow space, and reverts to the goal direction afterward.
    Stated in Section 3.1 without an explicit rule for how the boundary-aligned direction is computed.
  • domain assumption Robots are point masses with double-integrator dynamics and negligible communication delay.
    Model in Eq. (1) and Section 2.1; ignores actuator dynamics and delays.

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Pith. "Pith review of Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces." pith.science (2026). https://pith.science/paper/KT6RB5MN

@misc{pith2026250516087,
  author       = {Pith},
  title        = {Pith review of: Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KT6RB5MN}},
  note         = {Machine review of arXiv:2505.16087}
}
read the original abstract

This study proposes an event-based reconfiguration control to navigate a robot swarm through challenging environments with narrow passages such as valleys, tunnels, and corridors. The robot swarm is modeled as an undirected graph, where each node represents a robot capable of collecting real-time data on the environment and the states of other robots in the formation. This data serves as the input for the controller to provide dynamic adjustments between the desired and straight-line configurations. The controller incorporates a set of behaviors, designed using artificial potential fields, to meet the requirements of goal-oriented motion, formation maintenance, tailgating, and collision avoidance. The stability of the formation control is guaranteed via the Lyapunov theorem. Simulation and comparison results show that the proposed controller not only successfully navigates the robot swarm through narrow spaces but also outperforms other established methods in key metrics including the success rate, heading order, speed, travel time, and energy efficiency. Software-in-the-loop tests have also been conducted to validate the controller's applicability in practical scenarios. The source code of the controller is available at https://github.com/duynamrcv/erc.

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Reference graph

Works this paper leans on

38 extracted references · 38 canonical work pages

  1. [16]

    Self-reconfigurable V-shape formation of multi- ple UA Vs in narrow space environments,

    D. N. Bui, M. D. Phung, and H. Pham Duy, “Self-reconfigurable V-shape formation of multi- ple UA Vs in narrow space environments,” in 2024 IEEE/SICE International Symposium on System Integration (SII), pp. 1006–1011, 2024

    work page 2024

  2. [1]

    An overview of recent progress in the study of distributed multi- agent coordination,

    Y. Cao, W. Yu, W. Ren, and G. Chen, “An overview of recent progress in the study of distributed multi- agent coordination,” IEEE Transactions on Indus- trial Informatics , vol. 9, no. 1, pp. 427–438, 2013

    work page 2013

  3. [2]

    Multi-robot coordi- nation analysis, taxonomy, challenges and future scope,

    J. K. Verma and V. Ranga, “Multi-robot coordi- nation analysis, taxonomy, challenges and future scope,” Journal of Intelligent & Robotic Systems , vol. 102, Apr. 2021

    work page 2021

  4. [3]

    Nmpc for- mation control for heterogeneous multi-air vehicles with obstacle avoidance and connectivity mainte- nance,

    F. Ghaderi, A. Toloei, and R. Ghasemi, “Nmpc for- mation control for heterogeneous multi-air vehicles with obstacle avoidance and connectivity mainte- nance,” Intelligent Service Robotics , Nov. 2024

    work page 2024

  5. [4]

    Formation control: a review and a new consideration,

    Y. Q. Chen and Z. Wang, “Formation control: a review and a new consideration,” in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems , pp. 3181–3186, 2005

    work page 2005

  6. [5]

    A survey of multi-agent formation control,

    K.-K. Oh, M.-C. Park, and H.-S. Ahn, “A survey of multi-agent formation control,” Automatica, vol. 53, pp. 424–440, Mar. 2015

    work page 2015

  7. [6]

    Time- varying formation control for unmanned aerial vehi- cles: Theories and applications,

    X. Dong, B. Yu, Z. Shi, and Y. Zhong, “Time- varying formation control for unmanned aerial vehi- cles: Theories and applications,” IEEE Transactions on Control Systems Technology, vol. 23, p. 340–348, Jan. 2015

    work page 2015

  8. [7]

    Time-varying formation con- trol for general linear multi-agent systems with switching directed topologies,

    X. Dong and G. Hu, “Time-varying formation con- trol for general linear multi-agent systems with switching directed topologies,” Automatica, vol. 73, p. 47–55, Nov. 2016

    work page 2016

Show all 38 references
  1. [8]

    Behavior-based formation control for multirobot teams,

    T. Balch and R. Arkin, “Behavior-based formation control for multirobot teams,” IEEE Transactions on Robotics and Automation, vol. 14, no. 6, pp. 926– 939, 1998

    1998

  2. [9]

    Optimized flocking of autonomous drones in confined environments,

    G. V´ as´ arhelyi, C. Vir´ agh, G. Somorjai, T. Nepusz, A. E. Eiben, and T. Vicsek, “Optimized flocking of autonomous drones in confined environments,” Science Robotics, vol. 3, July 2018

    2018

  3. [10]

    Flocks, herds and schools: A dis- tributed behavioral model,

    C. W. Reynolds, “Flocks, herds and schools: A dis- tributed behavioral model,” in Proceedings of the 14th annual conference on Computer graphics and interactive techniques, ACM, Aug. 1987

    1987

  4. [11]

    Flocking for multi-robot systems via the null- space-based behavioral control,

    G. Antonelli, F. Arrichiello, and S. Chiaverini, “Flocking for multi-robot systems via the null- space-based behavioral control,” Swarm Intelli- gence, vol. 4, p. 37–56, Oct. 2009

    2009

  5. [12]

    Reynolds flocking in reality with fixed-wing robots: Commu- nication range vs. maximum turning rate,

    S. Hauert, S. Leven, M. Varga, F. Ruini, A. Can- gelosi, J.-C. Zufferey, and D. Floreano, “Reynolds flocking in reality with fixed-wing robots: Commu- nication range vs. maximum turning rate,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems , pp. 50...

    2011

  6. [13]

    Fuzzy-based distributed behavioral control with wall-following strategy for swarm navigation in arbitrary-shaped environments,

    T. Nhu, P. D. Hung, V. A. Ho, and T. D. Ngo, “Fuzzy-based distributed behavioral control with wall-following strategy for swarm navigation in arbitrary-shaped environments,” IEEE Access , vol. 9, pp. 139176–139185, 2021

    2021

  7. [14]

    Deployment of UA Vs for optimal multihop Ad-hoc networks using particle swarm optimization and behavior-based control,

    N. Duong Thi Thuy, D. Nam Bui, M. Duong Phung, and H. Pham Duy, “Deployment of UA Vs for optimal multihop Ad-hoc networks using particle swarm optimization and behavior-based control,” in 2022 11th International Conference on Control, Automation and Information Sciences (ICCAI...

    2022

  8. [15]

    Flocking for multi-agent dynamic 11 systems: algorithms and theory,

    R. Olfati-Saber, “Flocking for multi-agent dynamic 11 systems: algorithms and theory,” IEEE Transac- tions on Automatic Control , vol. 51, no. 3, pp. 401– 420, 2006

    2006

  9. [17]

    Obsta- cle avoidance of resilient uav swarm formation with active sensing system in the dense environment,

    P. Peng, W. Dong, G. Chen, and X. Zhu, “Obsta- cle avoidance of resilient uav swarm formation with active sensing system in the dense environment,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 10529– 10535, 2022

    2022

  10. [18]

    Ego-swarm: A fully autonomous and decentral- ized quadrotor swarm system in cluttered environ- ments,

    X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao, “Ego-swarm: A fully autonomous and decentral- ized quadrotor swarm system in cluttered environ- ments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , pp. 4101–4107, 2021

    2021

  11. [19]

    Hierarchi- cal distributed control for global network integrity preservation in multirobot systems,

    P. D. Hung, T. Q. Vinh, and T. D. Ngo, “Hierarchi- cal distributed control for global network integrity preservation in multirobot systems,”IEEE Transac- tions on Cybernetics , vol. 50, no. 3, pp. 1278–1291, 2020

    2020

  12. [20]

    Formation control of unmanned micro aerial vehicles for straitened environments,

    M. Saska, D. Hert, T. Baca, V. Kratky, and T. Nascimento, “Formation control of unmanned micro aerial vehicles for straitened environments,” Autonomous Robots , vol. 44, pp. 991–1008, Mar. 2020

    2020

  13. [21]

    Multi-robot formation control and object transport in dynamic environments via constrained optimization,

    J. Alonso-Mora, S. Baker, and D. Rus, “Multi-robot formation control and object transport in dynamic environments via constrained optimization,” The International Journal of Robotics Research , vol. 36, p. 1000–1021, Aug. 2017

    2017

  14. [22]

    Planning robot formations with fast marching square including uncertainty condi- tions,

    J. V. G´ omez, A. Lumbier, S. Garrido, and L. Moreno, “Planning robot formations with fast marching square including uncertainty condi- tions,” Robotics and Autonomous Systems , vol. 61, p. 137–152, Feb. 2013

    2013

  15. [23]

    Multi-robot virtual structure switching and formation changing strategy in an unknown occluded environment,

    D. Roy, A. Chowdhury, M. Maitra, and S. Bhat- tacharya, “Multi-robot virtual structure switching and formation changing strategy in an unknown occluded environment,” in 2018 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), IEEE, Oct. 2018

    2018

  16. [24]

    Distributed model predictive formation control with discretization-free path planning for transport- ing a load,

    H. Ebel, E. Sharafian Ardakani, and P. Eberhard, “Distributed model predictive formation control with discretization-free path planning for transport- ing a load,” Robotics and Autonomous Systems , vol. 96, p. 211–223, Oct. 2017

    2017

  17. [25]

    Multi-agent flock- ing with obstacle avoidance and safety distance preservation: a fuzzy potential-based approach,

    A. Ebrahimi and M. Farrokhi, “Multi-agent flock- ing with obstacle avoidance and safety distance preservation: a fuzzy potential-based approach,” Intelligent Service Robotics, vol. 17, p. 181–195, Jan. 2024

    2024

  18. [26]

    A formation maintenance and reconstruction method of UA V swarm based on distributed control,

    X. Fu, J. Pan, H. Wang, and X. Gao, “A formation maintenance and reconstruction method of UA V swarm based on distributed control,” Aerospace Sci- ence and Technology , vol. 104, p. 105981, Sept. 2020

    2020

  19. [27]

    Reconfigurable multi-UA V formation using angle- encoded PSO,

    V. Hoang, M. Phung, T. Dinh, Q. Zhu, and Q. Ha, “Reconfigurable multi-UA V formation using angle- encoded PSO,” in 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1670–1675, 2019

    2019

  20. [28]

    Robust adap- tive formation control of quadcopters based on a leader–follower approach,

    N. Xuan-Mung and S. K. Hong, “Robust adap- tive formation control of quadcopters based on a leader–follower approach,” International Jour- nal of Advanced Robotic Systems , vol. 16, no. 4, p. 1729881419862733, 2019

    2019

  21. [29]

    Bandwidth allocation and power control optimiza- tion for multi-uavs enabled 6g network,

    M. A. Alnakhli, E. M. Mohamed, and M. M. Fouda, “Bandwidth allocation and power control optimiza- tion for multi-uavs enabled 6g network,” IEEE Access, vol. 12, pp. 67405–67415, 2024

    2024

  22. [30]

    Formation control of mobile agents based on inter-agent distance dynam- ics,

    K.-K. Oh and H.-S. Ahn, “Formation control of mobile agents based on inter-agent distance dynam- ics,” Automatica, vol. 47, p. 2306–2312, Oct. 2011

    2011

  23. [31]

    Distributed multi-robot formation control in dynamic environ- ments,

    J. Alonso-Mora, E. Montijano, T. N¨ ageli, O. Hilliges, M. Schwager, and D. Rus, “Distributed multi-robot formation control in dynamic environ- ments,” Autonomous Robots, vol. 43, p. 1079–1100, July 2018

    2018

  24. [32]

    Learning- based multi-robot formation control with obsta- cle avoidance,

    C. Bai, P. Yan, W. Pan, and J. Guo, “Learning- based multi-robot formation control with obsta- cle avoidance,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11811– 11822, 2022

    2022

  25. [33]

    Multi uav cluster control method based on virtual core in improved artificial potential field,

    E. Wu, Y. Sun, J. Huang, C. Zhang, and Z. Li, “Multi uav cluster control method based on virtual core in improved artificial potential field,” IEEE Access, vol. 8, pp. 131647–131661, 2020

    2020

  26. [34]

    Invari- ance principles and observability in switched sys- tems with an application in consensus,

    T.-C. Lee, Y. Tan, Y. Su, and I. Mareels, “Invari- ance principles and observability in switched sys- tems with an application in consensus,” IEEE Transactions on Automatic Control, vol. 66, no. 11, pp. 5128–5143, 2021

    2021

  27. [35]

    Furrer, M

    F. Furrer, M. Burri, M. Achtelik, and R. Siegwart, Robot Operating System (ROS): The Complete Ref- erence (Volume 1), ch. RotorS—A Modular Gazebo MA V Simulator Framework, pp. 595–625. Cham: Springer International Publishing, 2016

    2016

  28. [36]

    Lyapunov-based nonlinear model predictive con- trol for attitude trajectory tracking of unmanned aerial vehicles,

    D. N. Bui, T. T. V. Nguyen, and M. D. Phung, “Lyapunov-based nonlinear model predictive con- trol for attitude trajectory tracking of unmanned aerial vehicles,” International Journal of Aeronau- tical and Space Sciences , vol. 24, pp. 502–513, Oct. 2022

    2022

  29. [37]

    On the scal- ability of vision-based drone swarms in the presence of occlusions,

    F. Schilling, E. Soria, and D. Floreano, “On the scal- ability of vision-based drone swarms in the presence of occlusions,” IEEE Access , vol. 10, pp. 28133– 28146, 2022

    2022

  30. [38]

    Novel type of phase transition in a system of self-driven particles,

    T. Vicsek, A. Czir´ ok, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical Review Letters, vol. 75, pp. 1226–1229, Aug. 1995. 12

    1995

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