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REVIEW 4 major objections 4 minor 26 references

This paper claims that a distributed controller lets robots with limited, heterogeneous sensing ranges acquire a rigid communication graph from an initially non-rigid one, while guaranteeing inter-agent collision avoidance and requiring no

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2026-08-02 07:19 UTC pith:7EDNJUOW

load-bearing objection Genuinely new construction for rigidity acquisition from a non-rigid graph under limited sensing — but Theorem 4's safety guarantee has an unproven multi-constraint feasibility step, and Assumption 4 quietly weakens the 'no global positions' claim. the 4 major comments →

arxiv 2607.10170 v2 pith:7EDNJUOW submitted 2026-07-11 cs.RO cs.SYeess.SY

From Non-Rigid to Rigid: Safe Acquisition of Rigid Communication Graphs under Limited Sensing

S. Saharsh , Vedhas Talnikar , Pushpak Jagtap This is my paper
classification cs.RO cs.SYeess.SY
keywords rigid graph acquisitioncommunication graph rigiditycontrol barrier functionsinter-agent safetylimited sensing rangeleader-follower consensussplay formationnon-rigid communication graphs
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper targets a gap in multi-robot formation control: most methods assume a rigid communication graph already exists, but in practice the graph starts non-rigid and must be built under limited sensing. The authors propose a leader–follower architecture built on an interleaved layered tree structure (iLDAG), plus a splay scheme that assigns every robot a unique position so it senses a second 'cross-family' link. They prove that adding one such link per follower turns the tree graph into a rigid maintenance graph, satisfying Laman's rigidity conditions. The distributed controller combines hierarchical second-order consensus with collision-cone control barrier functions, guaranteeing collision-free motion and finite-time rigidity acquisition. The claim is that this works for heterogeneous nonlinear robots, with followers using only relative measurements.

Core claim

The paper's central claim is that rigidity does not need to be assumed—it can be acquired. The authors construct a time-invariant spanning forest (iLDAG) and let each follower move toward a splay orbit so that it enters the sensing range of a designated second parent. By Lemma 1, adding this cross-family edge to each follower preserves Laman's rigidity conditions, so the maintenance graph becomes rigid with the minimal 2n−3 edges. The controller in Theorem 4 guarantees finite-time acquisition of these links while a collision-cone CBF safety filter keeps every pairwise distance above the safety radius, even when agents accelerate and have different sensing ranges.

What carries the argument

The central object is the maintenance graph, a selected subset of sensed edges that carries the rigidity guarantee. The key mechanism is the splay scheme: each parent's children are placed uniformly on a circle whose radius decays geometrically across layers, forcing cross-family links to fall within sensing range. The formal engine is Lemma 1 (edge addition preserves rigidity), Lemma 3 (sensing-range bound for acquiring a second parent), and Theorem 4 (a C3BF-QP controller that combines hierarchical consensus with collision-cone barrier functions).

Load-bearing premise

The whole safety and convergence proof leans on Assumption 4: every follower must receive from its parent recursively accumulated bounds on all upstream relative positions and velocities—if those bounds are wrong, delayed, or unavailable, the collision-avoidance and rigidity guarantees collapse.

What would settle it

Run the controller with a single follower whose parent deliberately reports Bp/Bv values clipped to half their true magnitude; if any pair of robots violates the minimum safety distance during the transient, the safety proof's main premise fails. Also, start a team whose leader set does not form a rigid subgraph and observe whether the maintenance graph ever becomes rigid—the paper's construction assumes leader rigidity as a starting point.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • If the central claim holds, multi-robot systems can start from a non-rigid, minimally connected graph and still form a rigid formation, removing a common hidden assumption in formation control.
  • Rigidity is achieved with the minimum number of edges (2n−3), reducing communication and sensing overhead in large teams.
  • The safety filter extends collision-cone barrier functions to accelerating obstacles, not just constant-velocity ones, which broadens applicability to real robots.
  • Controller gains are selected from explicit stability conditions rather than trial and error, simplifying deployment.
  • Followers never need their own global position or velocity, making the method suitable for GPS-denied indoor or underground environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A hidden practical bottleneck is that the leader subgraph is assumed rigid and leaders are assumed to know their absolute positions; if the leaders themselves lose global sensing, the hierarchy's top layer needs a separate rigidity mechanism.
  • Assumption 4's recursively accumulated bounds Bp and Bv require reliable multi-hop communication of parent state; in lossy networks, underestimated bounds would undermine the safety proof—an empirical stress test would clarify how much slack the CBF formulation has.
  • The splay geometry essentially pre-plans each follower's target position; a natural extension would be to handle dynamic obstacles or formation reconfiguration after the graph becomes rigid, which the paper does not address.
  • The 2D rigidity argument relies on Laman's theorem; extending to 3D would require replacing it with generic rigidity in R^3 and adjusting the orbit geometry accordingly.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper studies distributed rigid communication-graph acquisition and collision avoidance for n planar heterogeneous nonlinear agents (1) with heterogeneous limited sensing ranges. It assumes a time-invariant leader-follower iLDAG spanning forest (Def. 2) and proposes a 'splay scheme' (Def. 6) under geometric orbit decay (Def. 7), a hierarchical second-order consensus controller (Thm. 3), and a C3BF-QP safety filter (Thms. 2 and 4). It claims: (i) every follower acquires a cross-family maintenance link (Lemmas 1 and 3), making the maintenance graph rigid in the sense of Laman's theorem; (ii) inter-agent safety is preserved despite accelerating nonlinear agents; (iii) followers need no global positions. The claims are supported by appendix proofs and by a 27-agent simulation and a 5-robot hardware experiment with 9 virtual agents.

Significance. If the technical claims were fully established, the paper would contribute a constructive, distributed approach to an under-studied problem: acquiring (rather than maintaining) rigid sensing/communication graphs under range limits and collision constraints, with nonlinear heterogeneous dynamics and only relative local measurements for followers. The iLDAG/splay construction is original and explicit, the edge-addition/rigidity argument via Lemma 1 is plausible, and the authors provide complete appendix derivations, parameter inequalities (Lemmas 2-3), and experimental validation. The paper also clearly identifies the gap relative to rigidity-maintenance literature. However, the main safety theorem is not presently established because the multi-constraint QP feasibility and rank-one projection consistency are not proved; this limits confidence in the central guarantee.

major comments (4)
  1. [§4.2, Theorem 4 (Eq. 14)] The safety guarantee of Theorem 4 is not established. Theorem 2 certifies only a single obstacle pair (i,j) with control u_i = g_i ξ_ij ξ_ij^T \hat u_i. In Theorem 4, one \hat q_i must satisfy all constraints j∈\tilde N_i(t). For each j the CBF constraint is L'_fij b + L'_hij b \hat q_i + κ(b) ≥ 0. Since L'_hij b \hat q_i = -A_j ξ_ij^T \hat q_i with A_j > 0, near the boundary this enforces ξ_ij^T \hat q_i ≤ c_j with c_j < 0. Two sensed agents approaching from opposite directions give opposite ξ_ij, making the feasible set empty. The proof in Appendix .1 ('feasible because Γ...≠0') only addresses one inequality, and no feasibility argument is given for the intersection of half-spaces. Moreover, the same theorem uses a single projection direction ξ_ij while constraints involve all j; for a neighbor m ≠ j, the lower-bound derivation used in Theorem 2 does not apply to u_i unless the project
  2. [§2.7, Assumption 4] The recursive accumulated bounds Bp and Bv are load-bearing: they enter every CBF lower bound in Theorem 2 and the disturbance bound γ_ij in Theorem 3. Assumption 4 says only that the parent 'communicates' Bp(i,pr(i)) = ||p_ipr(i)|| + Bp(pr(i),pr^{(2)}(i)) (and similarly Bv). For the root, Bp(i,l)=||p_l||+||p_il||, so followers receive a scalar depending on the leaders' absolute position norm. This is difficult to reconcile with the advertised claim that followers use only relative positions/velocities and have no global information. More importantly, no protocol is given for updating Bp/Bv, while the proofs need the inequalities ||p_i|| ≤ Bp(i,pr(i)) to hold at all times. Delayed, noisy, or lost packets would break the bound and invalidate both the CBF lower bound and the consensus disturbance estimate. This assumption should be stated as a dynamic invariant with a communication/measure
  3. [§4.1, Theorem 3 (Eq. 13)] Condition (13) is not a checkable design condition as stated. The variable γ* is used but never defined in the theorem statement; γ(t) is defined through γ_ij(t), which itself depends on the trajectory ||x_im(t)||, on Bp+Bv, and on the gains k_p,k_v. The proof's Remark 6 replaces it by an implicit equation R = C R_0 + C∆/(μ* - Cγ*), γ* = R'_0 + γ_1 R, making (13) an implicit region-of-attraction condition rather than an explicit inequality. Since Theorem 3 underpins convergence to the splay configuration and therefore the link-acquisition argument, the paper needs a constructive verification procedure or a clearly stated set of sufficient explicit inequalities.
  4. [§4.2, proof of Theorem 4] Even if QP feasibility were granted, the transition from asymptotic consensus (Theorem 3) to finite-time rigidity acquisition in Problem 1 is only sketched. The proof states that non-parent neighbors leave \tilde N_i(t) and the 'nominal controller is thus recovered in finite time,' but consensus is asymptotic and T(ε) in Theorem 3 only gives an ultimate bound. To guarantee acquisition of the cross-family link by a finite t', the paper must quantify an ε small enough that the sensing-range inequalities in Lemma 3 remain satisfied despite the tracking error. The statement 'This solves Problem 1' does not follow from the displayed arguments.
minor comments (4)
  1. [Figure 5 caption] The caption refers to 'Lemma 2, 3, 4', but there is no Lemma 4 in the manuscript.
  2. [Section 2.3, Definition 2] The phrase 'All the leaders form a rigid subgraph with at least 2n_l - 3 edges' followed by 'minimum edge count' is slightly confusing because the leader subgraph may have more than the minimum; the inequality should be stated explicitly as |E_leader| ≥ 2n_l - 3.
  3. [Section 5.1] The comparison with rigidity-maintenance methods is qualitative: a single target-enclosing scenario under one representative controller is shown to fail. This does not quantify performance against existing algorithms and should be labeled as an illustrative comparison, not a benchmark.
  4. [Assumption 3] Assumption 3 says the control input norm ||u_j|| can be measured or communicated, but Assumption 4 additionally requires Bp and Bv; the two assumptions should be consolidated or cross-referenced so that the full set of communicated quantities is explicit.

Circularity Check

0 steps flagged

No significant circularity: the rigidity acquisition chain is derived from Laman/Henneberg conditions and geometric inequalities, and the C3BF self-citation is independent prior support rather than a self-referential premise.

full rationale

The rigidity result is not circular. Definition 2 and Lemmas 1–3 construct the maintenance graph from the iLDAG structure and verify rigidity via Theorem 1, which is Laman's theorem cited from external literature. Lemma 1 is an edge-addition argument: a new follower with one tree parent plus one cross-family link adds two edges, so the edge count 2n−3 and the Laman sparsity condition are checked incrementally. Lemma 3 derives the sensing-range conditions such as λ_i ≥ η_{s−2}(2 sin(π/k)+α) directly from triangle inequalities on the splay orbits; these are sufficient geometric conditions, not fitted values. The splay parameters η0, α, k, and λ_i in the simulation are chosen to satisfy those derived inequalities, not tuned to match the target rigidity conclusion. Safety is built on the C3BF from [14], which is a self-citation with overlapping authorship (Jagtap), and it is load-bearing for the pairwise forward-invariance argument. However, [14] is a prior published CBF construction with its own assumptions and development, used as external support rather than as an unverified premise that already contains this paper's claimed results. The recursive bounds Bp and Bv in Assumption 4 are inputs/assumptions used to bound unknown states; they are not outputs renamed as predictions. The main substantive concern is Theorem 4's proof: the text asserts that forward invariance "follows directly from Theorem 2," but Theorem 2 certifies a single pair, while the QP in (14) has constraints for all j ∈ Ñ_i(t), and joint feasibility is not shown. That is a proof gap / correctness risk, not a circular equivalence, so it does not raise the circularity score.

Axiom & Free-Parameter Ledger

5 free parameters · 6 axioms · 3 invented entities

The paper is honest about its structural prerequisites: the iLDAG spanning forest, recursive Bp/Bv communication, and several Lipschitz/heterogeneity bounds are assumed rather than derived. The geometric design parameters (η_0, α, k, sensing ranges) are chosen by hand to satisfy derived inequalities, not learned from data. No entity is invented as a physical mechanism; the invented entities are graph/architecture constructs and communication aggregations.

free parameters (5)
  • orbit radius η_0 = 17 (simulation), 25 (examples)
    Base radius of the splay scheme; chosen by hand to satisfy Lemma 2 and sensing constraints. It sets the formation scale.
  • radial decay rate α = 0.436 (simulation), 0.4 (Example 3)
    Layer-by-layer orbit radius decay. Chosen to satisfy the inequalities in Lemma 2; it directly affects safety margins and sensing requirements.
  • branching factor k = 3 (simulation), 6 (Example 4)
    Number of children per parent that fixes iLDAG capacity and sensing/safety trade-offs. Chosen by hand from the derived bounds.
  • consensus gains k_p, k_v = 4.0, 6.0 (simulation)
    Chosen to satisfy the eigenvalue condition (12) of Theorem 3. The paper does not give a constructive selection rule, so this is tuning.
  • sensing ranges λ_i per layer = λ=40 (layer 2), λ=18 (layer 3), λ=50 (cross-tree)
    Per-layer sensor ranges set to satisfy Lemma 3 sensing inequalities (9). They are assigned by the designer, not measured or optimized.
axioms (6)
  • domain assumption There exists a time-invariant spanning forest G_F with the iLDAG structure that is preserved under the time-varying graph (Assumption 3, Section 2.3).
    The entire architecture requires a pre-designed iLDAG spanning forest with known layers, branching factors, and leader rigid subgraph. This is a strong structural prerequisite.
  • domain assumption Assumption 4: each follower receives recursively accumulated position/velocity bounds Bp/Bv from its parent chain.
    Used in every C3BF constraint (Theorem 2 and 4) to handle accelerating obstacles; requires multi-hop communication of state bounds and weakens the 'only relative local sensing' claim.
  • domain assumption Assumption 1: existence of g_i(δ_i) making h_vi g_i symmetric positive definite with a uniform lower eigenvalue bound Γ_i.
    Needed for CBF feasibility and control allocation; excludes agents whose input matrix cannot be symmetrized.
  • domain assumption Assumption 2: uniform bounds Δ̄, ε̄ on model mismatch across heterogeneous agents.
    Used to bound the disturbance in the consensus proof (Theorem 3) and the CBF lower bound; requires global knowledge of worst-case heterogeneity.
  • standard math Laman's theorem (Theorem 1) as the rigidity criterion.
    Standard rigidity characterization in R^2; assumed as background.
  • standard math The C3BF validity result of [14] (Theorem 2 proof) is imported as a black box.
    The paper relies on [14, Theorem 1] for forward invariance, including its regularity conditions (∂b/∂x ≠ 0 on boundary).
invented entities (3)
  • iLDAG (Interleaved Layered Directed Acyclic Graph) spanning forest no independent evidence
    purpose: A graph-theoretic scaffold that determines which agents sense whom and how cross-family links are acquired; it organizes the hierarchical splay formation.
    This is a designer-chosen architecture, not an independently measurable entity. It has no falsifiable handle outside the paper.
  • Maintenance graph G_hat and maintenance links E_hat no independent evidence
    purpose: Selected subset of sensed edges on which rigidity is certified (Definition 3); used to satisfy Laman's conditions.
    An algorithmic selection rule, not an independently measurable physical entity.
  • Recursive accumulated bounds Bp, Bv no independent evidence
    purpose: State bounds propagated down the tree to make CBF constraints for accelerating neighbors tractable under only local measurements.
    Introduced as a communication convention in Assumption 4; no independent physical evidence.

pith-pipeline@v1.3.0-alltime-deepseek · 20437 in / 8805 out tokens · 72924 ms · 2026-08-02T07:19:22.742692+00:00 · methodology

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Cite this review

Pith. "Pith review of From Non-Rigid to Rigid: Safe Acquisition of Rigid Communication Graphs under Limited Sensing." pith.science (2026). https://pith.science/paper/7EDNJUOW

@misc{pith2026260710170,
  author       = {Pith},
  title        = {Pith review of: From Non-Rigid to Rigid: Safe Acquisition of Rigid Communication Graphs under Limited Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7EDNJUOW}},
  note         = {Machine review of arXiv:2607.10170}
}
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read the original abstract

Communication graph rigidity is a fundamental requirement in many multi robot formation control approaches. However, ensuring and maintaining a rigid communication topology becomes challenging in practice due to limited sensing ranges and dynamic operating conditions. This paper provides a method for achieving an inter robot collision free, rigid time varying communication graph, where communication links are established or broken according to limited sensing ranges, without assuming an initial rigid graph. In addition, the proposed approach guarantees the realization of a rigid graph for heterogeneous nonlinear multi robot systems. A computationally lean, distributed quadratic optimization-based controller is developed for a leader follower architecture, acquiring rigidity based on hierarchical second-order consensus among robots. Follower agents do not require global absolute positions of any agent, including their own. The proposed method is validated through both simulations and hardware experiments in a motion-capture environment, demonstrating reliable performance under the limited sensing capabilities of individual robots.

Figures

Figures reproduced from arXiv: 2607.10170 by Pushpak Jagtap, S. Saharsh, Vedhas Talnikar.

Figure 1
Figure 1. Figure 1: Illustration of iBFI in iLDAG with nl = 2, k = 3, d = 3. Leaders (in red) form a subset V1. Subset V2 agents are shown in blue and subset V3 in green, with color intensity increasing with sibling rank σpr(i)(i): the darkest node corresponds to σ = 1, and the lightest to σ = 3. Definition 2 (Interleaved LDAG). Given an nl-rooted directed forest G F := (V, E F , AF ), we define the Interleaved Layered Direct… view at source ↗
Figure 2
Figure 2. Figure 2: Logical flow of the proposed solution. Moving forward, for any agent pair (i, j) governed by the dynamics (1), agent i, we define agent j’s (treated as obstacle) relative position as pij := pj − pi , relative velocity as vij := vj − vi , and the half-angle of the collision cone as cos ϕij = √∥pij ∥2−(rij ) 2 ∥pij ∥ . The C3BF candidate for agent i, against agent j as the obstacle, is defined as: b(pij , vi… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of edge addition-based rigidity construction for the maintenance graph. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of hardware experiment setup with 5 physical robots and 9 virtual gazebo robots. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: MATLAB Simulation results validating the proposed framework. Video: [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Illustrations of splay formation tracking performance Layer-wise (a) mean position and (b) mean velocity [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of rigidity-matrix based methods for target-enclosing scenario with a non-rigid initial graph. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

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Reference graph

Works this paper leans on

26 extracted references · 1 linked inside Pith

  1. [1]

    Control and communication challenges in networked real-time systems

    John Baillieul and Panos J Antsaklis. Control and communication challenges in networked real-time systems. Proceedings of the IEEE, 95(1):9–28, 2007

    2007

  2. [2]

    Princeton University Press, 2009

    Francesco Bullo, Jorge Cortés, and Sonia Martinez.Distributed control of robotic networks: a mathematical approach to motion coordination algorithms. Princeton University Press, 2009

    2009

  3. [3]

    A survey of multi-agent systems on distributed formation control.Unmanned Systems, 12(05):913–926, 2024

    Yefeng Liu, Jingjing Liu, Zengpeng He, Zhenhong Li, Qichun Zhang, and Zhengtao Ding. A survey of multi-agent systems on distributed formation control.Unmanned Systems, 12(05):913–926, 2024

    2024

  4. [4]

    Operations on rigid formations of autonomous agents.Communications in Information and Systems, 3(3):223–258, 2003

    Tolga Eren, Brian Anderson, A Stephen Morse, Walter Whiteley, and Peter N Belhumeur. Operations on rigid formations of autonomous agents.Communications in Information and Systems, 3(3):223–258, 2003. 17 arXivTemplateA PREPRINT

    2003

  5. [5]

    Rigid graph control architectures for autonomous formations.IEEE Control systems magazine, 28(6):48–63, 2018

    Brian DO Anderson, Changbin Yu, Baris Fidan, and Julien M Hendrickx. Rigid graph control architectures for autonomous formations.IEEE Control systems magazine, 28(6):48–63, 2018

    2018

  6. [6]

    Yale University, 2004

    Tolga Eren.Rigid formations of autonomous agents. Yale University, 2004

    2004

  7. [7]

    Localizability and distributed protocols for bearing-based network localization in arbitrary dimensions.Automatica, 69:334–341, 2016

    Shiyu Zhao and Daniel Zelazo. Localizability and distributed protocols for bearing-based network localization in arbitrary dimensions.Automatica, 69:334–341, 2016

    2016

  8. [8]

    Planar bearing-only formation control of heterogeneous multi- agent systems

    Akshay Pampatwar and Dwaipayan Mukherjee. Planar bearing-only formation control of heterogeneous multi- agent systems. InSeventh Indian Control Conference, pages 171–176, 2021

    2021

  9. [9]

    Formation control of double integrators over directed graphs using bearings and bearing rates

    Susmitha T Rayabagi, Debasattam Pal, and Dwaipayan Mukherjee. Formation control of double integrators over directed graphs using bearings and bearing rates. InIEEE 63rd Conference on Decision and Control, pages 6560–6565, 2024

    2024

  10. [10]

    Decentralized formation control, collision avoidance and global connectivity maintenance using non-smooth barrier functions.Robotics and Autonomous Systems, page 105272, 2025

    Pranjal Bhatia, Sayan Basu Roy, PB Sujit, Luis Mejias Alvarez, and Aaron McFadyen. Decentralized formation control, collision avoidance and global connectivity maintenance using non-smooth barrier functions.Robotics and Autonomous Systems, page 105272, 2025

    2025

  11. [11]

    Distributed formation control of quadrotors under limited sensor field of view

    Duarte Dias, Pedro Urbano Lima, and Alcherio Martinoli. Distributed formation control of quadrotors under limited sensor field of view. InInternational Conference on Autonomous Agents & Multiagent Systems, pages 1087–1095, 2016

    2016

  12. [12]

    Safety barrier certificates for collisions-free multirobot systems

    Li Wang, Aaron D Ames, and Magnus Egerstedt. Safety barrier certificates for collisions-free multirobot systems. IEEE Transactions on Robotics, 33(3):661–674, 2017

    2017

  13. [13]

    Distributed control barrier function-based control scheme for multi-agent systems under a collective constraint

    Xiao Tan and Dimos V Dimarogonas. Distributed control barrier function-based control scheme for multi-agent systems under a collective constraint. In25th International Symposium on Mathematical Theory of Networks and Systems Bayreuth, Germany, pages 571–574, 2022

    2022

  14. [14]

    A collision cone approach for control barrier functions.ArXiv preprint arXiv:2403.07043, 2024

    Manan Tayal, Bhavya Giri Goswami, Karthik Rajgopal, Rajpal Singh, Tejas Rao, Jishnu Keshavan, Pushpak Jagtap, and Shishir Kolathaya. A collision cone approach for control barrier functions.ArXiv preprint arXiv:2403.07043, 2024

    Pith/arXiv arXiv 2024

  15. [15]

    Bearing-only formation control with limited visual sensing: Two agent case.IFAC-PapersOnLine, 51(23):28–33, 2018

    Daniel Frank, Daniel Zelazo, and Frank Allgöwer. Bearing-only formation control with limited visual sensing: Two agent case.IFAC-PapersOnLine, 51(23):28–33, 2018

    2018

  16. [16]

    Decentralized rigidity maintenance control with range measurements for multi-robot systems.The International Journal of Robotics Research, 34(1):105–128, 2015

    Daniel Zelazo, Antonio Franchi, Heinrich H Bülthoff, and Paolo Robuffo Giordano. Decentralized rigidity maintenance control with range measurements for multi-robot systems.The International Journal of Robotics Research, 34(1):105–128, 2015

    2015

  17. [17]

    Rigidity maintenance control for multi-robot systems.Robotics: Science and Systems VIII (Sydney 2012), 60, 2012

    Daniel Zelazo, Antonio Franchi, Frank Allgöwer, Heinrich H Bülthoff, and P Robuffo Giordano. Rigidity maintenance control for multi-robot systems.Robotics: Science and Systems VIII (Sydney 2012), 60, 2012

    2012

  18. [18]

    Shubham Sawarkar, P Sangeerth, S Saharsh, and Pushpak Jagtap. Sliding mode control for safe trajectory tracking with moving obstacles avoidance: Experimental validation on planar robots.in Proceedings of 18th International Workshop on Variable Structure Systems, 2026

    2026

  19. [19]

    On distance-preserving and domination elimination orderings.SIAM Journal on Discrete Mathematics, 11(3):414–436, 1998

    Victor Chepoi. On distance-preserving and domination elimination orderings.SIAM Journal on Discrete Mathematics, 11(3):414–436, 1998

    1998

  20. [20]

    Network connectivity maintenance via nonsmooth control barrier functions

    Pio Ong, Beatrice Capelli, Lorenzo Sabattini, and Jorge Cortés. Network connectivity maintenance via nonsmooth control barrier functions. In60th IEEE Conference on Decision and Control, pages 4786–4791, 2021

    2021

  21. [21]

    Decentralized estimation and control of graph connectivity for mobile sensor networks.Automatica, 46(2): 390–396, 2010

    Peng Yang, Randy A Freeman, Geoffrey J Gordon, Kevin M Lynch, Siddhartha S Srinivasa, and Rahul Sukthankar. Decentralized estimation and control of graph connectivity for mobile sensor networks.Automatica, 46(2): 390–396, 2010

    2010

  22. [22]

    Scalable second-order consensus of hierarchical groups.Systems & Control Letters, 197:106032, 2025

    Jiamin Wang, Jian Liu, Feng Xiao, Ning Xi, Lipo Mo, and Yuanshi Zheng. Scalable second-order consensus of hierarchical groups.Systems & Control Letters, 197:106032, 2025

    2025

  23. [23]

    Bearing-only formation tracking control of multiagent systems

    Shiyu Zhao, Zhenhong Li, and Zhengtao Ding. Bearing-only formation tracking control of multiagent systems. IEEE Transactions on Automatic Control, 64(11):4541–4554, 2019

    2019

  24. [24]

    Connectivity maintenance: Global and optimized approach through control barrier functions

    Beatrice Capelli and Lorenzo Sabattini. Connectivity maintenance: Global and optimized approach through control barrier functions. InIEEE International Conference on Robotics and Automation, pages 5590–5596, 2020

    2020

  25. [25]

    Target enclosing control for nonholonomic multi-agent systems with connectivity maintenance and collision avoidance.Unmanned Systems, pages 1–16, 2026

    Boyin Zheng, Yahui Hao, and Lu Liu. Target enclosing control for nonholonomic multi-agent systems with connectivity maintenance and collision avoidance.Unmanned Systems, pages 1–16, 2026

    2026

  26. [26]

    Some necessary and sufficient conditions for second-order consensus in multi-agent dynamical systems.Automatica, 46(6):1089–1095, 2010

    Wenwu Yu, Guanrong Chen, and Ming Cao. Some necessary and sufficient conditions for second-order consensus in multi-agent dynamical systems.Automatica, 46(6):1089–1095, 2010. 18

    2010