Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms

Alireza Abbaspour; Arman Sargolzaei; Carl D. Crane

REVIEW 3 major objections 6 minor 111 references

Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This review of cooperative UAV control organizes the field into three algorithm families—consensus, flocking, and guidance-law based—across search-and-rescue, surveillance, mapping, and military applications, and identifies…

desk verdict A well-intentioned survey whose core math tutorial is wrong in several places; not new enough or reliable enough to recommend as an entry point in its current form. read the letter →

arxiv 1908.02789 v1 pith:BQDVVBUY submitted 2019-08-07 eess.SY cs.SY

Arman Sargolzaei , Alireza Abbaspour , Carl D. Crane This is my paper · ORCID

classification eess.SYcs.SY

keywords cooperativeUAVcontrolswarmsconsensusflockingalgorithmsguidance-lawbasedformationsearchandrescuesurveillancemappingfaulttolerancesecurity

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

The paper tries to establish that the cooperative-UAV field is best surveyed through a three-part taxonomy: applications (search and rescue, surveillance, mapping, military), algorithm families (consensus, flocking, guidance-law based), and cross-cutting challenges (nonlinear dynamics, collision avoidance, velocity matching, cohesion, communication security, aerodynamic coupling, fault tolerance). If the taxonomy holds, it gives a newcomer a structured entry point to a scattered literature and shows where the theoretical foundations have matured. The review also argues that the expected benefits of swarms—efficiency, flexibility, accuracy, robustness, reliability—come with a new vulnerability layer, because external communication lets faults and cyber-attacks propagate across the network. A sympathetic reader would take the paper as a snapshot of the area circa 2019, intended more as an orientation map than as a new control method.

What carries the argument

The carrying structure is the survey's classification scheme itself: consensus, flocking, and guidance-law algorithms, each tied to a mathematical core. For consensus, the core is graph-theoretic agreement, built from an adjacency matrix and Laplacian with the eigenvalue condition $0 = \lambda_1 < \lambda_2 \leq \dots \leq \lambda_N$ for connected graphs, together with the continuous-time law $\dot{x}_i(t) = -\sum_j a_{ij}(t)(x_i(t)-x_j(t))$. For flocking, the core is Reynolds' three behavioral rules—separation, alignment, cohesion—encoded in a second-order control law with a gradient-based term, a consensus term, and a navigational feedback term. For guidance-law control, the core is pure-pursuit geometry, where the follower's velocity is held parallel to the line of sight to a virtual leader ($\vec{V}_f \times \vec{R} = 0$). These three cores do the work of connecting each application area to a concrete control formulation.

What would settle it

A reader can settle the tutorial claim by checking the example adjacency matrix in Eq. (1) against the five-node graph in Figure 8 and then applying the Laplacian definition in Eq. (4): the printed matrix is not square for five nodes, and the off-diagonal rule as written cannot yield the symmetric, zero-row-sum Laplacian the text relies on. That inconsistency would be enough to show the simplified exposition is not a reliable self-contained account.

Watch

Extended reading notes

Core claim

The central claim is that cooperative control of UAVs splits cleanly into consensus algorithms, flocking algorithms, and guidance-law-based formation control, and that the same set of applications—border patrol, search and rescue, surveillance, mapping, military operations—recurs across all three. Consensus methods let agents reach agreement on shared information through graph-theoretic updates; flocking methods enforce Reynolds' separation, alignment, and cohesion without a rigid formation shape; guidance-law methods make followers pursue a leader or virtual target using pure-pursuit or line-of-sight rules. The paper further claims that the main obstacle to real-world deployment is not any single algorithm but the combination of nonlinear UAV dynamics, collision avoidance, aerodynamic coupling, and communication-induced vulnerabilities such as denial-of-service and time-delay-switch attacks. On its own terms, the review establishes a map of the field and a set of design requirements that any cooperative controller must satisfy.

Load-bearing premise

The review's tutorial value rests on its simplified equations for consensus and flocking being a faithful representation of standard graph-theoretic control theory, since a reader who studies those equations should come away with the correct foundations.

Editorial extensions

If this is right

Multi-UAV teams can shorten missions and cover more ground than single UAVs, but only if the controller handles communication loss, cyber-attacks, and fault propagation as first-class design constraints.
Consensus control gives a principled way to make agents agree on shared states, and recent extensions reviewed here push it toward switching topologies, nonlinear agent dynamics, and actuator fault tolerance.
Flocking control can keep a swarm coherent without a rigid formation, which suits surveillance and search tasks that require flexible shape changes, while semi-flocking variants trade cohesion for broader area coverage.
Pure-pursuit guidance laws offer a simple, practical leader-follower formation method for fixed-wing aircraft, and the reviewed refinements combine them with dynamic inversion or PID loops to improve tracking.
Aerodynamic coupling between closely flying UAVs is both a stability risk and a potential fuel-saving opportunity, depending on whether the vortex effects are modeled or ignored.

Reading between the lines

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

Applying the same three-family taxonomy to literature published after 2019 would probably require a fourth family for learning-based and data-driven cooperative controllers, which this snapshot does not cover.
The paper's framing of communication as a vulnerability layer implies that security and fault tolerance should be co-designed with the coordination algorithm, not added as a separate module after the fact.
A reader could use the survey's application-to-algorithm pairing as a template for evaluating new UAV missions: pick the mission type, identify its dominant challenge (time, coverage, deception, or mapping), then choose the algorithm family that addresses that constraint.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a serviceable undergraduate-level survey of multi-UAV cooperative control, and nothing more. The math primer that is supposed to make it useful is wrong in several places, so I would not point a student at it until the equations are fixed.

What it does well: it organizes the literature into three application areas and three algorithm families, and it is honest about what earlier surveys cover. For someone who knows nothing about the area, the survey gives a reasonable map of the field as of 2019. The bibliography is broad and the summaries of individual papers are mostly fair.

Soft spots: the stress-test concern is accurate. Section 4.1 is the tutorial heart of the paper and it is not reliable. Equation (1) gives a 4x5 adjacency matrix for a five-node graph; Eq. (4) is missing the diagonal case and garbles the off-diagonal definition; Eq. (5) repeats lambda_2. A reader cannot reconstruct the consensus dynamics from the printed text. These are not cosmetic typos. For a review whose stated value is accessibility, they cut the legs off the tutorial claim.

Also, the survey is not new. It follows the categories of earlier reviews [6,9,11] and adds little organizational novelty. The challenges section leans on the authors' own prior work (refs 54-59); that is not circular, but it gives the section a slightly self-promotional flavor. The prose needs editing; there are typos throughout, including in the abstract.

I do not think the math errors are a sign of dishonesty; they look like careless writing. But for a paper that claims to simplify the mathematics for readers, careless is disqualifying.

Bottom line: this is a paper for a newcomer who wants a quick bibliography, not for a researcher. It is not a research contribution in itself. If the authors correct Section 4.1 and tighten the prose, it could be a useful classroom reference. As it stands, I would not send it to a serious peer-review venue.

Referee Report

3 major / 6 minor

Summary. The manuscript is a survey of cooperative control for multi-UAV systems. It organizes the field into applications (search and rescue, surveillance, localization and mapping, military applications), challenges (communication and security, aerodynamic coupling, fault tolerance), and algorithm families (consensus, flocking, and guidance-law-based formation control). For each algorithm family the paper provides a short mathematical background and summarizes selected recent contributions, often with comments on advantages and disadvantages of the reviewed methods. The stated contribution is to provide an organized, accessible entry point to the multi-UAV cooperative-control literature, with the relevant mathematics simplified for readers.

Significance. If the presentation were technically clean, the survey would be useful as a compact orientation for graduate students and engineers entering multi-UAV control: it covers a broad set of applications, includes a substantial reference list, and gives an organized comparison of algorithm families. Its contribution is synthetic rather than empirical or theoretical, and it makes no new algorithmic claims. The tutorial material is the main content that can be independently checked, and that is where the current version fails: the graph-theoretic consensus primer in Section 4.1 contains concrete errors that would mislead precisely the readers the survey targets. I credit the authors for structuring a wide literature, for including practical guidance-law formulations, and for acknowledging limitations, but the tutorial promise needs repair before the survey is reliable.

major comments (3)

[Section 4.1, Eq. (1)] The displayed adjacency matrix has four rows and five columns, while the text says it corresponds to the five-node undirected graph in Figure 8. As printed, the matrix cannot represent an undirected graph on nodes A-E: the row for node E is missing, and the fourth row has a nonzero diagonal entry. This invalidates the graph-theory example that the consensus tutorial is built on.
[Section 4.1, Eq. (4)] The Laplacian definition is garbled. The diagonal case is effectively missing, since the condition j in N_i, i=j is not the standard definition l_ii = sum_{j != i} a_ij, and the off-diagonal case -a_ij is paired with an incomplete and incorrect condition. Because Eq. (3) defines the consensus dynamics in terms of this Laplacian, a reader cannot reconstruct a valid consensus model from the printed definitions.
[Section 4.1, Eq. (5)] The eigenvalue ordering repeats lambda_2 and therefore does not state the standard ordering 0 = lambda_1 < lambda_2 <= ... <= lambda_N. The sentence just above also describes the nonzero eigenvalues as lying on the right side of the imaginary plane; for the symmetric Laplacian of a connected graph the accurate statement is that these eigenvalues are real and positive. Since the spectral argument underpins the consensus convergence claim, this confusion is load-bearing rather than merely typographical.

minor comments (6)

[Section 4.1] The notation switches between N (number of graph nodes) and n (summation limits in Eqs. (2), (6), and (7)); please use one symbol consistently throughout the mathematical exposition.
[Section 4.2, Eq. (10)] The definition of n_ij lacks parentheses around the numerator and contains 'q_j q_i' in the denominator; it should be n_ij = (q_j - q_i) / sqrt(1 + epsilon ||q_j - q_i||^2).
[Section 4.3, Eq. (13)] The statement that the navigation constant N is 'usually chosen between 0.3 to 0.5' should be verified against the cited guidance-law references; standard treatments of proportional navigation typically quote N = 3-5, so this appears to be a typo.
[Section 2.2] The name 'Bread et al.' should be 'Beard et al.'; the corresponding reference [28] is Beard et al.
[Abstract and Section 5] The abstract repeats the sentence about challenges, and Section 5 says 'In this chapter' although the manuscript is a journal article; these should be cleaned up.
[Bibliography] Several references, for example [91], lack complete volume and page information; since the survey's value depends on guiding readers to the literature, full citation details should be provided.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the review's organization and survey claims are supported by external literature; the §4.1 equation errors are correctness defects, not self-referential reductions.

full rationale

This paper is a literature review, not a derivation paper. Its central claim is organizational: it categorizes cooperative UAV applications, challenges, and algorithms and surveys recent studies. There is no fitted parameter later renamed as a prediction, no quantity defined in terms of the claimed output, and no uniqueness theorem invoked to force a choice. The authors' self-citations ([54]-[59]) appear in Section 3 to support the statement that cooperative UAVs are vulnerable to denial-of-service and time-delay-switch attacks; these are examples from the authors' own prior publications, but the review's classification of challenges does not depend on those citations in a way that would make the survey circular. The mathematical exposition in Section 4.1 contains apparent errors — Eq. (1) is a 4x5 matrix for the five-node graph of Figure 8, Eq. (4) omits the diagonal Laplacian case and garbles the off-diagonal condition, and Eq. (5) repeats λ2. These are correctness and quality defects in the tutorial content, not circular reductions: no result in the survey is equivalent by construction to an input assumption. Therefore the circularity score is 0.

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

No new parameters, entities, or specialized axioms are introduced; the paper is a literature review. The only assumptions are standard mathematical tools and the accuracy of the cited literature.

assumptions (4)

standard math Standard graph-theoretic model of communication networks (undirected graphs with adjacency and Laplacian matrices)
Used in Section 4.1 to present consensus algorithms; equations (1)-(6) rely on this.
standard math Consensus convergence conditions for connected graphs (eigenvalue properties of Laplacian)
Invoked in Section 4.1 to state when consensus is achieved.
standard math Reynolds flocking rules (separation, alignment, cohesion) and second-order agent dynamics
Used in Section 4.2 to describe flocking algorithms, equations (9)-(10).
domain assumption Assumption that the surveyed papers' reported results are accurate
The review relies on the accuracy of the cited literature's claims, particularly the experimental results in [2, 4, 15, 101] etc.

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Cite this review

Pith. "Pith review of Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms." pith.science (2026). https://pith.science/paper/BQDVVBUY

@misc{pith2026190802789,
  author       = {Pith},
  title        = {Pith review of: Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BQDVVBUY}},
  note         = {Machine review of arXiv:1908.02789}
}

read the original abstract

A system of cooperative unmanned aerial vehicles (UAVs) is a group of agents interacting with each other and the surrounding environment to achieve a specific task. In contrast with a single UAV, UAV swarms are expected to benefit efficiency, flexibility, accuracy, robustness, and reliability. However, the provision of external communications potentially exposes them to an additional layer of faults, failures, uncertainties, and cyber-attacks and can contribute to the propagation of error from one component to other components in a network. Also, other challenges such as complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion should be addressed adequately. The main applications of cooperative UAVs are border patrol; search and rescue; surveillance; mapping; military. Challenges to be addressed in decision and control in cooperative systems may include the complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion. In this paper, emerging topics in the field of cooperative UAVs control and their associated practical approaches are reviewed.

Figures

Figures reproduced from arXiv: 1908.02789 by the authors.

**Figure 2.** Cooperative surveillance concept for patrolling urban areas. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Cooperative three-dimensional mapping using quadrotor UAVs. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Target detection using multiple cooperative UAVs in a reconnaissance mission [44]. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Cooperative radar jamming using multiple UAVs [45]. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Cooperative missile attacking concept to a target. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Cooperative algorithms are categorized and explained in three main algorithms. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Directed and undirected graph structure. [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

**Figure 9.** Figure 9: Multi-consensus control of three subgroups by Han et al. [82] [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

**Figure 10.** Figure 10: Geometry of the PP guidance algorithm [105] [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]

Discussion (0). Continue with ORCID to comment.

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Reviewed August 14, 2026 · model on record in the stance chip above.

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