CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

Fei Lin; Fei-Yue Wang; Jun Huang; Patrik P. S\"uli; Qinghua Ni; Rui Qin; Tengchao Zhang; Xiao Wang; Yonglin Tian

REVIEW 3 major objections 5 minor 24 references

CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read CoordField claims that a coordination field—a time-varying potential plus per-drone vortex repulsion—can allocate heterogeneous UAV tasks in urban scenarios with better coverage and balance than classical planners.

desk verdict The integrated architecture is sensible, but the control equation's gradient force is gauge-absorbed by pressure in a standard N-S reading, leaving the central mechanism inert as written. read the letter →

arxiv 2505.00091 v4 pith:Z5VXOQZS submitted 2025-04-30 cs.RO cs.AI

Tengchao Zhang , Yonglin Tian , Fei Lin , Jun Huang , Patrik P. Süli , Qinghua Ni , Rui Qin , Xiao Wang

show 1 more author

Fei-Yue Wang

This is my paper · ORCID

classification cs.ROcs.AI

keywords UAVswarmtaskallocationcoordinationfieldartificialpotentiallargelanguagemodelsagenticsystemdecentralizedurbanairmobility

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 proposes CoordField, an agentic system in which a large language model converts natural-language instructions into structured task tuples, and a coordination field converts those tuples into motion commands for a heterogeneous UAV swarm. The central claim is that this field-driven, decentralized allocation outperforms both standalone language models and classical optimization algorithms on task coverage, load balance, response time, and utilization in a 2D urban simulation. A sympathetic reading is that the system establishes a practical route from human language to swarm coordination without a central optimizer. If correct, CoordField would let drone swarms accept high-level commands such as “inspect the crowd and vehicles” and adapt to newly appearing tasks in real time.

What carries the argument

The central object is the coordination field, a two-part construction. The first part is a time-varying potential $\phi(x,y,t)$ built as a sum of weighted Gaussians, one per active task, with obstacles removed from the domain; its gradient supplies the attraction force $F_{\mathrm{task}}=k\nabla\phi$. The second part is a flow field modeled on the Navier–Stokes equations, $\frac{dv}{dt}=-\frac{1}{\rho}\nabla p+\nu\nabla^2 v+F_{\mathrm{task}}$, whose viscosity term smooths motion and prevents clumping. Around each UAV a local vortex adds a tangential repulsion $v_{\theta,i}(r)=\frac{\Gamma_i}{2\pi r}(1-\exp(-(r/r_0)^2))$, with circulation $\Gamma_i$ proportional to the UAV's capability and the local potential. Each UAV samples the combined vector $v_{\mathrm{new}}=v+\sum_i v_{\theta,i}$ at its position, so allocation emerges from local field following; the vortex term is the paper's proposed mechanism for keeping load balanced across hotspots.

What would settle it

Run CoordField in the same 1000×1000 grid with two identical high-priority tasks placed symmetrically on either side of a single UAV and record the trajectory: if the UAV settles at the midpoint or oscillates instead of committing to one task, the field has a local equilibrium that breaks allocation. A second check is to sweep the viscosity $\nu$ and vortex radius $r_0$ over a range while holding the task distribution fixed; if coverage efficiency or load balance moves by more than a few percentage points, the reported numbers are parameter-dependent.

Watch

Extended reading notes

Core claim

The paper's central discovery is that coupling an LLM parsing layer with a continuously updated scalar potential field produces a decentralized allocation mechanism that is semantically flexible and spatially adaptive. The scalar field $\phi(x,y,t)=\sum_j w_j(t)\exp(-\|(x,y)-(x_j,y_j)\|^2/(2\sigma_j^2))$ encodes task urgency, and the flow $\frac{dv}{dt}=-\frac{1}{\rho}\nabla p+\nu\nabla^2 v + k\nabla\phi$ guides UAVs toward task hotspots while the diffusion term prevents over-concentration. Each UAV also emits a local vortex repulsion $v_{\theta,i}(r)=\frac{\Gamma_i}{2\pi r}(1-\exp(-(r/r_0)^2))$, so the combined field $v_{\mathrm{new}}=v+\sum_i v_{\theta,i}$ balances attraction and spreading without a central planner. In 50 rounds of 2D simulation, the system reports the best coverage efficiency (95%) and load balance (0.8) among four classical planners, and the best parsing accuracy, coverage, and utilization among the tested language models, which the paper takes as evidence that the field mechanism itself carries the coordination gains.

Load-bearing premise

The load-bearing premise is that the field-following rule—move along the local flow plus the push from other drones—actually settles into a good division of tasks; if it stalls or loops in some layouts, the reported gains would be artifacts of the chosen scenario rather than properties of the mechanism.

Editorial extensions

If this is right

A natural-language layer can replace a hand-coded mission planner: task tuples $(x_j,y_j,w_j,\mathrm{type}_j)$ extracted by an LLM feed directly into the coordination field.
Decentralized field following with vortex repulsion should keep heterogeneous UAVs spread across hotspots, yielding the reported load balance of 0.8 versus 1.3–3.6 for the classical planners.
Because the field is recomputed whenever new tasks appear, the system can absorb emergent tasks without re-running a global optimization.
If the field mechanism is the source of the gains, replacing it with ACO, GWO, WOA, or A* should degrade coverage and utilization, which the paper's Table II reports.

Reading between the lines

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

The vortex repulsion term is essentially a finite-core vortex whose radius $r_0$ and circulation $\Gamma_i$ likely need scenario-specific tuning; the paper gives no sensitivity sweep, so the reported gains may not transfer without recalibration.
The same field construction could extend to 3D by adding a vertical gradient and obstacle-aware exclusion zones, though local minima in cluttered urban canyons would become harder to escape.
A formal convergence proof for the combined gradient-plus-vortex flow, under assumptions such as bounded task weights and positive capability scores, would turn the empirical claim into a stability guarantee; checking whether $v_{\mathrm{new}}$ is divergence-free is a natural first step.
The 96% task-parsing accuracy is reported for one instruction set; testing on longer, multi-clause commands with negations or temporal constraints would reveal whether the LLM layer, rather than the field layer, is the bottleneck.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Gabor,

Read the CoordField paper. Short version: the integrated architecture is sensible, but the central control equation as written cannot be doing the work claimed, and the evaluation is too thin to support the superiority claims. With a clarified update rule and proper statistics, this could become a useful systems paper.

What's actually new is the packaging: an LLM parser turning natural-language commands into structured task tuples, coupled to a decentralized field-based allocation loop. That's a reasonable thing to try, and the comparison against ACO, GWO, WOA, and A* does show the field approach can be competitive on coverage and load balance. The authors also commit to a closed-loop perception-planning-execution cycle, which is a solid structure. Credit for that.

The soft spots are serious. First, the velocity field equation. The paper writes dv/dt = -1/rho grad p + nu laplacian v + k grad phi, says it's based on Navier-Stokes. If that's the standard incompressible N-S, the gradient forcing is gauge-equivalent to pressure—redefine p to absorb phi and the task dependence vanishes from the momentum equation. The only way phi can steer anything is if the flow is compressible, the pressure dropped, or the equation is heuristic. None of these is stated. The method is not reproducible. Second, the 'vortex repulsion' is a tangential rotational field: it makes agents orbit, not repel. The formula and the verbal description don't match. Third, the experiments report single values from 50 rounds with no error bars, no formal metric definitions, no parameter values, and no code or data. The relationship between the CoordField row and the LLM baselines in Table I is also confusing, since CoordField is a system and not a model.

The paper is not a takedown; the authors are honest about the components. But the mechanism as written is inert under the natural reading. It's a fixable formulation issue, not a deep theoretical flaw. Specify a concrete update rule (e.g., v = k grad phi plus a diffusion term, no pressure), run the comparisons with repetitions and confidence intervals, and the idea would be worth testing.

Who's this for: people building LLM-driven multi-robot systems who want a starting point for field-based decentralized allocation. They'll get the architecture, not the math.

For peer review: I'd send it out. A serious referee can pin the gauge issue and push the authors to define the actual algorithm. It deserves referee time, but it needs major revision before it's publishable.

Yours,

Referee Report

3 major / 5 minor

Summary. CoordField couples an LLM-based semantic parsing module with a coordination-field planner to allocate heterogeneous UAV swarms to emergent tasks in a 2D urban simulation. The planner builds a Gaussian-mixture task potential φ(x,y,t), derives a guidance velocity field v from a Navier–Stokes-style equation with forcing F_task = k∇φ, adds per-UAV vortex repulsion terms to prevent clustering, and directs each UAV to follow the combined local velocity v_new = v + Σ v_θ,i. Experiments over 50 rounds in a 1000×1000 simulated grid with 20 UAVs compare CoordField against three LLM baselines (Table I) and four classical allocation algorithms with the same agentic front end (Table II), reporting superior coverage efficiency, load balance, and utilization. The abstract additionally promises improved response time and adaptability to dynamic changes.

Significance. If established, the central contribution would be a clean integration of LLM-based instruction parsing with a field-based, scalable allocation mechanism for UAV swarms. The paper deserves credit for two design choices: the Table II ablation, which swaps only the allocation core while keeping the agentic front end fixed and thereby isolates the field method, and the simple functional form of the potential field, which could be implemented cheaply in real time. However, the quantitative claims rest on single-point statistics without variance or significance tests, and the defining equation of the mechanism is underdetermined, so the paper as submitted does not establish that the field actually guides UAVs toward high-priority tasks. The significance is therefore conditional on a corrected derivation, full parameter disclosure, and statistically supported experiments.

major comments (3)

[§III-B (Task decomposition)] The defining equation of the method is dv/dt = −(1/ρ)∇p + ν∇²v + F_task(φ), with F_task = k∇φ. As written, this equation does not specify the claimed mechanism. Pressure p is never defined, and a single PDE involving both v and p does not determine v without a pressure law or continuity equation, so the system is underdetermined. If the authors instead intend the incompressible Navier–Stokes system (the text invokes 'Navier–Stokes equations in fluid dynamics', 'fluid viscosity', and 'nominal fluid density'), then the pure-gradient forcing k∇φ is gauge-equivalent to pressure: replacing p by p − kρφ removes φ from the momentum equation entirely, so with homogeneous boundary conditions the steady solution of the unforced problem is the one reached, and the task landscape cannot steer UAVs. The sentence 'UA Vs are able to perceive and exploit the gradient of φ(x,y,t) to identify and navigate toward high-priority task areas' does not follow from the stated equations. The authors must replace this with an explicit, well-posed update rule (for example, v proportional to ∇φ, or an advection–diffusion scheme for φ) together with boundary conditions and a discretization; the current formulation is non-reproducible and its central mechanism is, as stated, inert.
[§IV-B (Tables I and II)] All reported performance values are single numbers from 50 rounds, with no standard deviation, confidence interval, or significance test, so the claimed superiority of CoordField (e.g., CE 95% versus 84%–90% in Table II, UUR 97% versus 53%–91%) cannot be distinguished from run-to-run noise. The metrics TPA, CE, TLB, UUR, and CR are named but never defined by formulas, making it impossible to verify that the comparisons are meaningful or that TLB is dimensionless. The abstract promises 'response time' and 'adaptability to dynamic changes', but no metric for either appears in the tables or the text, so two of the three headline claims are unmeasured. The qualitative direction of the tables is plausible, but the load-bearing empirical claims require error bars, metric definitions, and ideally significance tests.
[§III-B and §IV-A] The method is not reproducible from the text: no parameter values are given for k, ν, r0, σj, or the simulation time step T, and no numerical scheme is described for evolving v(x,y,t) or for updating φ when tasks appear or disappear. In addition, the claim of a 'fully decentralized setting' is contradicted by the construction of v(x,y,t) as a global field assembled from all task tuples and all UAV states by the Planning Agent; only the vortex term is local. The paper also claims the field guides different UAV types toward role-matching task regions, but task type never enters the field equations (capability c_i affects only the vortex amplitude Γ_i), so heterogeneity is not part of the coordination mechanism as specified. Finally, the assertion that the combined field yields 'dynamically stable task allocation and coordination behavior' is made without any convergence analysis, local-minimum argument, or sensitivity study.

minor comments (5)

[Abstract and §III opening] The abstract contains the ungrammatical 'large language models (LLMs) is responsible', and the opening sentence of Section III, 'built upon a Coordination field mechanism is proposed to guide UA V motion and task selection', is garbled and should be rewritten.
[§IV-B (footnotes to Tables I and II)] In the Table II footnote, 'A: A Star Algorithm' loses the asterisk of 'A*'. In Table I, CoordField is listed among 'models', although it is a system that contains an LLM; the caption should state which LLM drives the CoordField semantic module in that comparison and what exactly the other rows represent.
[§IV-A and Fig. 4] The setup states that 20 UAVs operate in the simulation, but Fig. 4 shows ten trajectories; the caption should clarify whether this is a subset, a single task type, or a different configuration.
[§II-C] The sentence 'This approach has been applied applications in traffic flow modeling' contains a typo, and reference [21] concerns AUV rather than UAV path planning, which slightly weakens the positioning in that paragraph.
[§III-B (perception mapping and task assignment)] The index conventions are inconsistent: M tasks are used in the definition of φ but N appears without definition in the formula for Γ_i(t), and the denominator of that formula is not explained. Also, φ is set to zero inside obstacles, which introduces discontinuities of ∇φ at obstacle boundaries; the text should state how the gradient is computed across those boundaries.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: CoordField's empirical comparisons are independent of its design inputs; self-citations are contextual only.

full rationale

The central claims—coverage efficiency, load balance, response time, and adaptability—are established by 50 rounds of simulation comparing CoordField against ACO, GWO, WOA, and A* (Table II) and against several LLM baselines (Table I). The coordination field is built from task urgency weights w_j(t), Gaussian task densities, and capability-weighted circulation Γ_i(t), but the evaluation metrics (TPA, CE, TLB, UUR, CR) are measured outcomes of the simulated executions, not algebraic rearrangements of those inputs. No parameter is fitted to a subset of the reported metrics and then presented as a prediction; no uniqueness theorem is invoked; the method is explicitly an APF-style heuristic (Section II-C). The self-citations [12]–[14] and [19] support background statements about agentic systems and UAV-LLM integration and are not load-bearing for the field mechanism's empirical comparison. The Navier-Stokes gauge objection in the skeptic note is a correctness concern about whether the stated equation actually steers UAVs, not a circularity: even if the mechanism as written is inert, the coverage/balance results would be artifacts of the simulation implementation rather than consequences of the equations by construction. Under the circularity-specific criteria, no reduction of claim to input is exhibited.

Assumptions & free parameters 4 free parameters · 3 assumptions · 2 invented entities

The central claim rests on several hand-specified control parameters and unproved behavioral assumptions about field-guided swarm convergence. These are not fitted to external data in the paper; they are design choices, which makes the system reproducible only if the authors release exact values.

free parameters (4)

k
Scaling coefficient in F_task = k * grad(phi); no value or tuning procedure is given in Section III-B.
nu (fluid viscosity)
Controls the smoothness and responsiveness of the velocity field in Equation (2); value not reported.
r0 (vortex influence radius)
Influence radius of the local vortex field around each UAV in Section III-B; no value or adaptation rule stated.
sigma_j (task influence radius)
Spatial influence radius of each task's Gaussian in Equation (1); how it is set from task type is unspecified.

assumptions (3)

domain assumption The velocity field v(x,y,t) combined with vortex repulsion converges to a stable, balanced task allocation.
Asserted in Section III-B ('naturally converging toward high-demand task regions', 'dynamically stable task allocation') without proof or formal analysis.
domain assumption Task urgency weights w_j(t) and parsed coordinates from the LLM faithfully represent true task demand.
The Gaussian field phi in Equation (1) is built from these values; inaccurate parsing would misdirect UAVs. The paper reports TPA=96% for CoordField but does not analyze failure cases.
domain assumption A 2D flow-following abstraction with buildings modeled as zero-field regions is adequate to evaluate the coordination strategy.
Section IV-A uses a 1000 by 1000 2D grid and fixed-altitude UAVs; no 3D dynamics, sensing noise, or communication constraints are modeled.

invented entities (2)

coordination field phi(x,y,t)
purpose: Scalar field representing task urgency to guide UAV motion and task selection.
A constructed mathematical field, not an observed quantity; its effectiveness is only checked through the in-house simulation.
local vortex repulsion field omega_i(r)
purpose: Prevents UAV clustering and balances task allocation.
A designed controller inspired by fluid vortices; no independent physical evidence; parameters Gamma_i and r0 are chosen by the authors.

how reviews work

0 comments

Cite this review

Pith. "Pith review of CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios." pith.science (2026). https://pith.science/paper/Z5VXOQZS

@misc{pith2026250500091,
  author       = {Pith},
  title        = {Pith review of: CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z5VXOQZS}},
  note         = {Machine review of arXiv:2505.00091}
}

read the original abstract

With the increasing demand for heterogeneous Unmanned Aerial Vehicle (UAV) swarms to perform complex tasks in urban environments, system design now faces major challenges, including efficient semantic understanding, flexible task planning, and the ability to dynamically adjust coordination strategies in response to evolving environmental conditions and continuously changing task requirements. To address the limitations of existing methods, this paper proposes CoordField, a coordination field agent system for coordinating heterogeneous drone swarms in complex urban scenarios. In this system, large language models (LLMs) is responsible for interpreting high-level human instructions and converting them into executable commands for the UAV swarms, such as patrol and target tracking. Subsequently, a Coordination field mechanism is proposed to guide UAV motion and task selection, enabling decentralized and adaptive allocation of emergent tasks. A total of 50 rounds of comparative testing were conducted across different models in a 2D simulation space to evaluate their performance. Experimental results demonstrate that the proposed system achieves superior performance in terms of task coverage, response time, and adaptability to dynamic changes.

Figures

Figures reproduced from arXiv: 2505.00091 by the authors.

**Figure 2.** 2D space simulation map. To validate the proposed architecture, we conducted a series of experiments in a simulated urban environment. We [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 4.** UAV task assignment trajectory route [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 3.** Figure 3: Density map with Hotspots. To validate the effectiveness of the proposed system, a series of systematic experimental evaluations are conducted, along with representative visualizations of key results. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 11 canonical work pages

[1]

A real-time demand management and route-guidance system for elim- inating congestion,

C. Makridis, C. Menelaou, S. Timotheou, and C. G. Panayiotou, “A real-time demand management and route-guidance system for elim- inating congestion,” IEEE Intelligent Transportation Systems Maga- zine, 2024

work page 2024
[2]

Hybrid chaos game and grey wolf optimization algorithms for uav path planning,

J. Yang, F. Yan, J. Zhang, and C. Peng, “Hybrid chaos game and grey wolf optimization algorithms for uav path planning,” Applied Mathematical Modelling, p. 115979, 2025

work page 2025
[3]

Dbo-awoa: An adaptive whale optimization algorithm for global optimization and uav 3d path planning,

T. Xu and C. Chen, “Dbo-awoa: An adaptive whale optimization algorithm for global optimization and uav 3d path planning,” Sensors, vol. 25, no. 7, p. 2336, 2025

work page 2025
[4]

Learning optimizer-based visual analytics method to detect targets in autonomous unmanned aerial vehicles,

R. Selvaraj, V . M. Kuthadi, A. Duraisamy, B. Selvaraj, and M. S. Pethuraj, “Learning optimizer-based visual analytics method to detect targets in autonomous unmanned aerial vehicles,” IEEE Intelligent Transportation Systems Magazine, vol. 16, no. 6, pp. 72–85, 2023

work page 2023
[5]

Llm-planner: Few-shot grounded planning for embodied agents with large language models,

C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y . Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 2998–3009

2023
[6]

Instruct2act: Mapping multi-modality instructions to robotic actions with large language model,

S. Huang, Z. Jiang, H. Dong, Y . Qiao, P. Gao, and H. Li, “Instruct2act: Mapping multi-modality instructions to robotic actions with large language model,” arXiv preprint arXiv:2305.11176 , 2023

arXiv 2023
[7]

Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey,

D. B. Acharya, K. Kuppan, and B. Divya, “Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey,”IEEE Access, 2025

2025
[8]

Llm-based agentic systems in medicine and healthcare,

J. Qiu, K. Lam, G. Li, A. Acharya, T. Y . Wong, A. Darzi, W. Yuan, and E. J. Topol, “Llm-based agentic systems in medicine and healthcare,” Nature Machine Intelligence , vol. 6, no. 12, pp. 1418–1420, 2024

work page 2024

Show all 24 references

[9]

A-mem: Agentic memory for llm agents,

W. Xu, Z. Liang, K. Mei, H. Gao, J. Tan, and Y . Zhang, “A-mem: Agentic memory for llm agents,” arXiv preprint arXiv:2502.12110 , 2025

2025 arXiv
[10]

Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents,

W. Chen, Y . Su, J. Zuo, C. Yang, C. Yuan, C. Qian, C.-M. Chan, Y . Qin, Y . Lu, R. Xie et al. , “Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents,” arXiv preprint arXiv:2308.10848, vol. 2, no. 4, p. 6, 2023

2023 arXiv
[11]

Multi-agent collaboration mechanisms: A survey of llms,

K.-T. Tran, D. Dao, M.-D. Nguyen, Q.-V . Pham, B. O’Sullivan, and H. D. Nguyen, “Multi-agent collaboration mechanisms: A survey of llms,” arXiv preprint arXiv:2501.06322 , 2025

2025 arXiv
[12]

Uavs meet llms: Overviews and perspectives toward agentic low-altitude mobility,

Y . Tian, F. Lin, Y . Li, T. Zhang, Q. Zhang, X. Fu, J. Huang, X. Dai, Y . Wang, C. Tian et al. , “Uavs meet llms: Overviews and perspectives toward agentic low-altitude mobility,” arXiv preprint arXiv:2501.02341, 2025

2025 arXiv
[13]

Airvista: Empowering uavs with 3d spatial reasoning abilities through a multimodal large language model agent,

F. Lin, Y . Tian, Y . Wang, T. Zhang, X. Zhang, and F.-Y . Wang, “Airvista: Empowering uavs with 3d spatial reasoning abilities through a multimodal large language model agent,” in 2024 IEEE 27th In- ternational Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2...

2024
[14]

Airvista-ii: An agentic system for embodied uavs toward dynamic scene semantic understanding,

F. Lin, Y . Tian, T. Zhang, J. Huang, S. Guan, and F.-Y . Wang, “Airvista-ii: An agentic system for embodied uavs toward dynamic scene semantic understanding,” arXiv preprint arXiv:2504.09583 , 2025

2025 arXiv
[15]

Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles,

C. Cui, Y . Ma, X. Cao, W. Ye, and Z. Wang, “Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles,” IEEE Intelligent Transportation Systems Magazine , vol. 16, no. 4, pp. 81–94, 2024

2024
[16]

Code as policies: Language model programs for em- bodied control,

J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for em- bodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500

2023
[17]

Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis,

Y . Liang, C. Wu, T. Song, W. Wu, Y . Xia, Y . Liu, Y . Ou, S. Lu, L. Ji, S. Mao et al., “Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis,” Intelligent Computing , vol. 3, p. 0063, 2024

2024
[18]

Gsce: A prompt framework with enhanced reasoning for reliable llm-driven drone control,

W. Wang, Y . Li, L. Jiao, and J. Yuan, “Gsce: A prompt framework with enhanced reasoning for reliable llm-driven drone control,” arXiv preprint arXiv:2502.12531, 2025

2025 arXiv
[19]

Logisticsvista: 3d terminal delivery services with uavs, ugvs and usvs based on foundation models and scenarios engineering,

Y . Tian, F. Lin, X. Zhang, J. Ge, Y . Wang, X. Dai, Y . Lv, and F.-Y . Wang, “Logisticsvista: 3d terminal delivery services with uavs, ugvs and usvs based on foundation models and scenarios engineering,” in 2024 IEEE International Conference on Service Operations and Logistic...

2024
[20]

Securing the future after pagerbombs: Lifecycle protection of smart devices via blockchain intelligence,

F. Lin, Q. Ni, J. Yang, J. Li, N. Zheng, L. Kovács, R. Prodan, M. Dotoli, Q.-L. Han, and F.-Y . Wang, “Securing the future after pagerbombs: Lifecycle protection of smart devices via blockchain intelligence,” IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 12, pp. 2355–2358, 2024

2024
[21]

Improved artificial potential field method applied for auv path planning,

X. Fan, Y . Guo, H. Liu, B. Wei, and W. Lyu, “Improved artificial potential field method applied for auv path planning,” Mathematical Problems in Engineering , vol. 2020, no. 1, p. 6523158, 2020

2020
[22]

Obstacle avoidance of mobile robots using modified artificial potential field algorithm,

S. M. H. Rostami, A. K. Sangaiah, J. Wang, and X. Liu, “Obstacle avoidance of mobile robots using modified artificial potential field algorithm,” EURASIP Journal on Wireless Communications and Net- working, vol. 2019, no. 1, pp. 1–19, 2019

2019
[23]

An improved artificial potential field method for path planning and formation control of the multi-uav systems,

Z. Pan, C. Zhang, Y . Xia, H. Xiong, and X. Shao, “An improved artificial potential field method for path planning and formation control of the multi-uav systems,” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 69, no. 3, pp. 1129–1133, 2021

2021
[24]

Reasoning before comparison: Llm-enhanced semantic similarity metrics for domain specialized text analysis,

S. Xu, Z. Wu, H. Zhao, P. Shu, Z. Liu, W. Liao, S. Li, A. Sikora, T. Liu, and X. Li, “Reasoning before comparison: Llm-enhanced semantic similarity metrics for domain specialized text analysis,” arXiv preprint arXiv:2402.11398, 2024

2024 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

A real-time demand management and route-guidance system for elim- inating congestion,

C. Makridis, C. Menelaou, S. Timotheou, and C. G. Panayiotou, “A real-time demand management and route-guidance system for elim- inating congestion,” IEEE Intelligent Transportation Systems Maga- zine, 2024

work page 2024

[2] [2]

Hybrid chaos game and grey wolf optimization algorithms for uav path planning,

J. Yang, F. Yan, J. Zhang, and C. Peng, “Hybrid chaos game and grey wolf optimization algorithms for uav path planning,” Applied Mathematical Modelling, p. 115979, 2025

work page 2025

[3] [3]

Dbo-awoa: An adaptive whale optimization algorithm for global optimization and uav 3d path planning,

T. Xu and C. Chen, “Dbo-awoa: An adaptive whale optimization algorithm for global optimization and uav 3d path planning,” Sensors, vol. 25, no. 7, p. 2336, 2025

work page 2025

[4] [4]

Learning optimizer-based visual analytics method to detect targets in autonomous unmanned aerial vehicles,

R. Selvaraj, V . M. Kuthadi, A. Duraisamy, B. Selvaraj, and M. S. Pethuraj, “Learning optimizer-based visual analytics method to detect targets in autonomous unmanned aerial vehicles,” IEEE Intelligent Transportation Systems Magazine, vol. 16, no. 6, pp. 72–85, 2023

work page 2023

[5] [5]

Llm-planner: Few-shot grounded planning for embodied agents with large language models,

C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y . Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 2998–3009

2023

[6] [6]

Instruct2act: Mapping multi-modality instructions to robotic actions with large language model,

S. Huang, Z. Jiang, H. Dong, Y . Qiao, P. Gao, and H. Li, “Instruct2act: Mapping multi-modality instructions to robotic actions with large language model,” arXiv preprint arXiv:2305.11176 , 2023

arXiv 2023

[7] [7]

Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey,

D. B. Acharya, K. Kuppan, and B. Divya, “Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey,”IEEE Access, 2025

2025

[8] [8]

Llm-based agentic systems in medicine and healthcare,

J. Qiu, K. Lam, G. Li, A. Acharya, T. Y . Wong, A. Darzi, W. Yuan, and E. J. Topol, “Llm-based agentic systems in medicine and healthcare,” Nature Machine Intelligence , vol. 6, no. 12, pp. 1418–1420, 2024

work page 2024