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REVIEW 3 major objections 5 minor 45 references

VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a vision-language model can serve as a directional prior inside an RRT path planner, cutting the number of sampling iterations to about a third while preserving path quality.

desk verdict VLM-RRT is a plausible incremental idea whose main claim needs a VLM-free control before the iteration gains can be credited to the VLM. read the letter →

arxiv 2505.23267 v1 pith:G2RKOMLU submitted 2025-05-29 cs.RO cs.AI

Jianlin Ye , Savvas Papaioannou , Panayiotis Kolios This is my paper
classification cs.ROcs.AI
keywords VLM-RRTUAVnavigationsampling-basedpathplanningvisionlanguagemodelsRapidly-exploringRandomTreeschain-of-thoughtpromptingwildfiredisasterresponse
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 sets out to show that a vision-language model can make sampling-based path planning much cheaper by telling the sampler where to look next. In the proposed VLM-RRT planner, the VLM is shown a rendered image of the environment with the start, goal, obstacles, and the partially built tree marked, and it answers with a single compass direction. The RRT loop then draws its next random sample from a small sector in that direction most of the time, instead of uniformly across the whole map. The authors report that this guided sampling finds collision-free paths in far fewer iterations than plain RRT and RRT*, with success rates above both when the prompt includes worked examples or chain-of-thought reasoning, and with path lengths close to RRT*'s. If this holds up, the practical payoff is a simple way to inject visual common sense into planners used for time-critical UAV missions such as wildfire search and rescue.

What carries the argument

The load-bearing mechanism is VLM-guided sector sampling: instead of drawing the next sample uniformly from the whole environment, the planner selects a leaf node, asks the vision-language model for one of eight directions toward the goal, and draws a sample uniformly from a sector $R$ of radius 30 m and angle 45 degrees centred at that leaf and aligned with the suggested direction. The parameter $\gamma$, defaulting to 0.85, decides how often this guided draw replaces uniform sampling, so the algorithm keeps an exploration fallback. The prompt system, with zero-shot, few-shot, and chain-of-thought variants, is what elicits the directional output from the VLM, and the evaluation shows the few-shot and chain-of-thought variants are the ones that make the planner outperform the baselines.

What would settle it

Run VLM-RRT on the same benchmark with the VLM's direction replaced by a random draw from the same eight compass directions, and also by the straight-line direction to the goal, keeping the sector radius and angle fixed; if success rate and iteration counts do not clearly worsen, the claimed gains come from the sector sampling geometry rather than the model's visual reasoning.

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Extended reading notes

Core claim

The central claim is that VLM-RRT beats traditional RRT by treating a vision-language model as a general-purpose pattern matcher that supplies a coarse directional heuristic. At each guided step the algorithm picks a leaf of the growing tree, renders the current environment with the goal, obstacles, and explored tree marked, asks the VLM for one word from eight compass directions, and then samples a new point inside a 45-degree sector of radius 30 metres centred on that leaf and oriented along the suggested direction. With a consultation probability of $\gamma=0.85$, the planner alternates between this guided sampling and ordinary uniform RRT sampling. The paper's experiments across 250 random wildfire-inspired scenarios report that this hybrid finds paths in about one-third of the iterations needed by RRT or RRT*, with success rates up to 94% versus 82% for RRT and 88% for RRT*, and with path length essentially matching RRT*.

Load-bearing premise

The result rests on the assumption that the VLM's one-word direction is reliable enough at the eight-direction level to bias sampling usefully; the paper's own robustness test shows that when every step follows the VLM ($\gamma=1$), its mistakes lower success to 79%, below plain RRT's 82%.

Editorial extensions

If this is right

  • With the best prompt style, VLM-RRT finds collision-free paths in roughly 89 iterations on average, compared with 343 for RRT and 302 for RRT*, on the same 250-scenario benchmark.
  • The improvement does not cost path quality: the best VLM-RRT average path length is 46 m, close to RRT*'s 45 m and well under RRT's 58 m.
  • The guided planner can react to a goal that moves during the mission, detecting the relocation in 92% of 50 dynamic scenarios and usually correcting its direction within one extra sampling step.
  • Because $\gamma$ can be reduced, the planner can degrade smoothly toward ordinary RRT when the VLM's advice is not trusted, trading speed for robustness.

Reading between the lines

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

  • An ablation that replaces the VLM's answer with a fixed rule such as 'sample in a cone toward the goal' would reveal how much of the speed-up is due to the visual reasoning itself rather than to the sector-biased sampling scheme.
  • The eight-word direction vocabulary makes VLM-RRT a discrete local search; coupling it with rewiring, as in RRT*, could turn the iteration savings into asymptotically optimal paths, which the paper does not attempt.
  • The strong dependence of success rate on prompt style suggests that prompt engineering matters as much as the planner's own parameters, and that a deployable system would need a way to detect and recover from VLM misdirections rather than trusting a fixed $\gamma$.
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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

3 major / 5 minor

Summary. The paper proposes VLM-RRT, a hybrid path-planning method that integrates a vision-language model (VLM) into the RRT sampling loop. At each iteration, with probability γ, the VLM is shown a rendered environment image (including start, goal, obstacles, and the current tree) and is asked to output one of eight cardinal/intercardinal directions; the planner then samples a new point from a sector centered at a randomly selected leaf node, with radius r and angle θ oriented along the VLM's suggested direction. The algorithm is evaluated in a 2D UAV navigation setting, comparing GPT-4o and Llama 3.2V under zero-shot, few-shot, and chain-of-thought prompting against RRT, RRT*, and LLM-A*, reporting success rate, iteration count, and path length, plus a robustness sweep over γ and a dynamic-goal experiment.

Significance. If the claimed gains are real, VLM-guided sampling would be a novel and practically relevant way to inject semantic perception into sampling-based planners, and the paper's systematic comparison across two VLMs and three prompting schemes is a useful starting point. However, the evidence as presented is not yet load-bearing: the central attribution of the improvements to the VLM is untested because the sector sampler is never ablated, the quantitative claims are weakened by inconsistent numbers across tables and the absence of variance or significance measures, and the practical-efficiency motivation is not supported because wall-clock time and VLM API latency are never reported. The γ robustness sweep (Table III) is a commendable attempt to characterize sensitivity, but it also exposes that the VLM's direction advice is sometimes harmful, reinforcing the need for the missing control conditions.

major comments (3)
  1. [Section V-B and Algorithm 2 (Lines 6-8)] The central claim that VLM guidance improves sampling efficiency is not supported because no control condition ablates the VLM. The sector sampler (radius r=30 m, angle θ=45°) is always paired with the VLM's directional output, so the observed iteration reductions in Table II could be produced by the local sector sampling mechanism alone, irrespective of VLM accuracy. This concern is sharpened by Table III: at γ=1, where the VLM is consulted at every step, success rate drops to 79%, below plain RRT's 82%, and the text attributes this to 'the VLM can make mistakes' (Section V-B). To attribute the gains to the VLM, the authors should add control conditions that replace the VLM direction with (a) a uniformly random choice among the eight directions and (b) a trivial goal-pointing heuristic (e.g., the direction from the selected leaf toward the goal centroid, which is explicitly marked as a red dot in the rendered image). Without these conditions, the paper's novelty claim is not empirically established.
  2. [Tables I and II] The paper reports inconsistent averages for the same algorithm and prompting configuration. For GPT-4o with CoT prompting, Table I reports an average of 172 iterations and 54.56 m path length, while Table II reports 94 iterations and 48 m for the identical configuration; similar discrepancies appear for RRT (423/56.48 vs 343/58) and RRT* (477/53.89 vs 302/45). The text states that Table I averages 100 MC runs and Table II averages 250 MC runs, but a difference of this magnitude indicates different environment distributions, termination criteria, or success-rate filtering, and the authors do not explain it. Additionally, the paper reports no variance, confidence intervals, or significance tests, so the claim of 'significantly fewer iterations' (Section V-B) is not statistically supported.
  3. [Section V-A and V-B] No wall-clock time or end-to-end latency is reported. The paper motivates the work by 'rapid and efficient' navigation, but each VLM-informed step in Algorithm 2 requires a rendered image and an API call to GPT-4o or Llama 3.2V; the iteration-count reductions in Tables I-III may be offset by VLM inference latency. At minimum, the authors should report planning wall-clock time with and without the VLM, including API latency, and ideally the number of VLM calls per successful plan. Without this, the practical-efficiency claim is incomplete.
minor comments (5)
  1. [Equation (2)] The final constraint 'y(t) ∈ Y, ∀k ∈ {0,...,N−1}' uses the index k instead of t, and the horizon upper limit should be consistent with T−1 rather than N−1.
  2. [Fig. 1 caption] The phrase 'The Trajectory also serve as [Previous state]' has a subject-verb agreement error and missing words; please revise.
  3. [Section V-B] The statement that VLM-RRT 'significantly' improves performance should be supported by statistical tests or at least variance measures; the zero-shot conditions (GPT-4o success rate 68% vs RRT's 82%) show that the VLM can degrade performance, so the claimed improvement is conditional on prompting strategy.
  4. [Algorithm 2, Line 1 and Line 8] The notation r is used both for the sector radius in SampleStateVLM and for the empty set initialized as 'r ← ∅' in Line 1; rename one of these to avoid ambiguity.
  5. [Section III-C] The reference path P is used in the cost function of Eq. (2) before it is formally defined as the output of the planner; consider defining P in the problem formulation or reordering the presentation so that the planning stage is described first.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: VLM-RRT is an empirical method whose claims are evaluated against external baselines, not derived from its own inputs.

full rationale

The paper makes no formal derivation from first principles; its central claims (fewer iterations, shorter paths) are empirical comparisons measured in Monte Carlo simulations. The VLM is an external pretrained model (GPT-4o, Llama 3.2) whose one-word directional output is generated from a rendered image; the reported success rates, iteration counts, and path lengths are not defined in terms of that output, so no prediction reduces to an input by construction. The hyperparameters (gamma, sector radius r, angle theta) are tuned in the robustness sweep of Table III, but this is standard algorithm tuning rather than fitting a parameter and then 'predicting' the same fitted quantity. The paper's self-citations appear in the introduction and related work as domain background (e.g., refs. [1], [2], [4]-[6], [8]-[11], [14]-[16], [19]-[22]) and are not used to justify the VLM-RRT mechanism or to import a uniqueness theorem; the method comparison rests on external baselines RRT [23], RRT* [28], LLM-A* [38], A* [39]. The strongest legitimate concern is an experimental one: the design never ablates the VLM by replacing it with a random direction or a goal-pointing heuristic, so the sector sampler's contribution is not isolated. That missing control is a validity threat, not circularity: there is no equation or definition in the paper that makes the VLM's guidance equivalent to the measured outcome. Consequently no circular step can be quoted, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The method introduces no new physical entities; its load-bearing choices are algorithm parameters (gamma, r, theta) and the assumption that the VLM receives a clean, fully informative map image.

free parameters (5)
  • Gamma (VLM guidance probability) = 0.85 default; swept 0.5-1.0 in Table III
    Chosen by hand; the robustness table shows 0.7-0.8 gives the best success rates, but 0.85 is used in the main comparisons.
  • Sampling sector radius r = 30 m
    Set arbitrarily in Section V-A; no sensitivity analysis is provided.
  • Sampling sector angle theta = 45 degrees
    Set arbitrarily in Section V-A; no sensitivity analysis is provided.
  • Planning horizon multiplier = T = 2.5 * path_length
    Heuristic introduced in Section V-A without justification; used to set the tracking horizon.
  • Cost weights Q and R = 0.9I and 0.1I
    Chosen for the tracking QP in Section V-A; the tracking performance is not evaluated in the results.
assumptions (3)
  • domain assumption The UAV operates at fixed altitude in a 2D plane.
    Section III-C states this simplification; extension to 3D is asserted but not tested.
  • domain assumption The environment, obstacles, and goal are fully known and rendered as a clean image for the VLM.
    Section III-B assumes all environmental information is known; Section IV-A describes the snapshot encoding obstacle and goal positions explicitly, which is stronger than real perception.
  • domain assumption VLM direction outputs are correct with high enough probability.
    The method's gains rely on the VLM choosing the right eight-way direction; Section V-B shows that gamma=1 can fail due to VLM mistakes.

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Cite this review

Pith. "Pith review of VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation." pith.science (2026). https://pith.science/paper/G2RKOMLU

@misc{pith2026250523267,
  author       = {Pith},
  title        = {Pith review of: VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G2RKOMLU}},
  note         = {Machine review of arXiv:2505.23267}
}
read the original abstract

Path planning is a fundamental capability of autonomous Unmanned Aerial Vehicles (UAVs), enabling them to efficiently navigate toward a target region or explore complex environments while avoiding obstacles. Traditional pathplanning methods, such as Rapidly-exploring Random Trees (RRT), have proven effective but often encounter significant challenges. These include high search space complexity, suboptimal path quality, and slow convergence, issues that are particularly problematic in high-stakes applications like disaster response, where rapid and efficient planning is critical. To address these limitations and enhance path-planning efficiency, we propose Vision Language Model RRT (VLM-RRT), a hybrid approach that integrates the pattern recognition capabilities of Vision Language Models (VLMs) with the path-planning strengths of RRT. By leveraging VLMs to provide initial directional guidance based on environmental snapshots, our method biases sampling toward regions more likely to contain feasible paths, significantly improving sampling efficiency and path quality. Extensive quantitative and qualitative experiments with various state-of-the-art VLMs demonstrate the effectiveness of this proposed approach.

Figures

Figures reproduced from arXiv: 2505.23267 by the authors.

Figure 1
Figure 1. Our basic system consists of two types of prompts, task descriptions and basic inputs. We match a snapshot of the current environment with the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Comparison of different prompt engineering techniques for navigation decision-making. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Illustrative example of the path-planning behavior obtained with: (a) RRT, (b) RRT* and (c) VLM-RRT. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Illustration of the VLM-RRT algorithm navigating toward dynamic goals in a 2D environment. The scenario involves three goal relocations. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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Works this paper leans on

45 extracted references · 34 canonical work pages

  1. [1]

    Autonomous 4D trajectory planning for dynamic and flexible air traffic management,

    C. Vitale, S. Papaioannou, P. Kolios, and G. Ellinas, “Autonomous 4D trajectory planning for dynamic and flexible air traffic management,” Journal of Intelligent & Robotic Systems , vol. 106, no. 1, p. 11, 2022

    work page 2022

  2. [2]

    Probabilistically robust trajectory planning of multiple aerial agents,

    ——, “Probabilistically robust trajectory planning of multiple aerial agents,” in 2024 18th International Conference on Control, Automa- tion, Robotics and Vision (ICARCV) . IEEE, 2024, pp. 852–859

    work page 2024

  3. [3]

    S. M. LaValle, Planning algorithms . Cambridge university press, 2006

    work page 2006

  4. [4]

    Distributed search planning in 3-d environments with a dynamically varying number of agents,

    S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou, and M. M. Polycarpou, “Distributed search planning in 3-d environments with a dynamically varying number of agents,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 53, no. 7, pp. 4117– 4130, 2023

    work page 2023

  5. [5]

    Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning,

    ——, “Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning,” IEEE Transactions on Mobile Computing, 2025, doi:10.1109/TMC.2025.3551362

    arXiv 2025

  6. [6]

    Rolling horizon coverage control with collaborative au- tonomous agents,

    ——, “Rolling horizon coverage control with collaborative au- tonomous agents,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , vol. 383, no. 2289, 2025

    work page 2025

  7. [7]

    An accurate uav 3-d path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm,

    Y . Wan, Y . Zhong, A. Ma, and L. Zhang, “An accurate uav 3-d path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm,” IEEE Trans- actions on Cybernetics , vol. 53, no. 4, pp. 2658–2671, 2022

    work page 2022

  8. [8]

    3D Trajectory Planning for UA V-based Search Missions: An Integrated Assessment and Search Planning Approach,

    S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou, and M. M. Polycarpou, “3D Trajectory Planning for UA V-based Search Missions: An Integrated Assessment and Search Planning Approach,” in 2021 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2021, pp. 517–526

    work page 2021

Show all 45 references
  1. [9]

    Towards Automated 3D Search Planning for Emergency Re- sponse Missions,

    ——, “Towards Automated 3D Search Planning for Emergency Re- sponse Missions,” Journal of Intelligent & Robotic Systems , vol. 103, no. 1, p. 2, 2021

    2021

  2. [10]

    Coordinated CRLB-based control for tracking multiple first responders in 3D en- vironments,

    S. Papaioannou, S. Kim, C. Laoudias, P. Kolios, S. Kim, T. Theocharides, C. Panayiotou, and M. Polycarpou, “Coordinated CRLB-based control for tracking multiple first responders in 3D en- vironments,” in 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE,...

    2020

  3. [11]

    Synergising human-like responses and machine intelligence for plan- ning in disaster response,

    S. Papaioannou, P. Kolios, C. G. Panayiotou, and M. M. Polycarpou, “Synergising human-like responses and machine intelligence for plan- ning in disaster response,” in 2024 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2024, pp. 1–8

    2024

  4. [12]

    Distributed estimation and control for jamming an aerial target with multiple agents,

    S. Papaioannou, P. Kolios, and G. Ellinas, “Distributed estimation and control for jamming an aerial target with multiple agents,” IEEE Transactions on Mobile Computing , vol. 22, no. 12, pp. 7203–7217, 2022

    2022

  5. [13]

    Cooperative path planning of uavs & ugvs for a persistent surveillance task in urban environments,

    Y . Wu, S. Wu, and X. Hu, “Cooperative path planning of uavs & ugvs for a persistent surveillance task in urban environments,” IEEE Internet of Things Journal , vol. 8, no. 6, pp. 4906–4919, 2020

    2020

  6. [14]

    Probabilistic search and track with multiple mo- bile agents,

    S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou, and M. M. Polycarpou, “Probabilistic search and track with multiple mo- bile agents,” in 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2019, pp. 253–262

    2019

  7. [15]

    A cooperative multiagent probabilistic framework for search and track missions,

    ——, “A cooperative multiagent probabilistic framework for search and track missions,” IEEE Transactions on Control of Network Sys- tems, vol. 8, no. 2, pp. 847–858, 2020

    2020

  8. [16]

    Joint estimation and control for multi-target passive mon- itoring with an autonomous UA V agent,

    S. Papaioannou, C. Laoudias, P. Kolios, T. Theocharides, and C. G. Panayiotou, “Joint estimation and control for multi-target passive mon- itoring with an autonomous UA V agent,” in 2023 31st Mediterranean Conference on Control and Automation (MED) . IEEE, 2023, pp. 176–181

    2023

  9. [17]

    Multi-uav coverage path planning for the inspection of large and complex structures,

    W. Jing, D. Deng, Y . Wu, and K. Shimada, “Multi-uav coverage path planning for the inspection of large and complex structures,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 1480–1486

    2020

  10. [18]

    Automating aircraft scanning for inspection or 3d model creation with a uav and optimal path planning,

    Y . Sun and O. Ma, “Automating aircraft scanning for inspection or 3d model creation with a uav and optimal path planning,” Drones, vol. 6, no. 4, p. 87, 2022

    2022

  11. [19]

    UA V-based receding horizon control for 3D inspection planning,

    S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou, and M. M. Polycarpou, “UA V-based receding horizon control for 3D inspection planning,” in 2022 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2022, pp. 1121–1130

    2022

  12. [20]

    Hierarchical Fault-Tolerant Coverage Control for an Au- tonomous Aerial Agent,

    S. Papaioannou, C. Vitale, P. Kolios, C. G. Panayiotou, and M. M. Polycarpou, “Hierarchical Fault-Tolerant Coverage Control for an Au- tonomous Aerial Agent,” IFAC-PapersOnLine, vol. 58, no. 4, pp. 532– 537, 2024, 12th IFAC Symposium on Fault Detection, Supervision and Safety ...

    2024

  13. [21]

    Cooperative receding horizon 3D coverage con- trol with a team of networked aerial agents,

    S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou, and M. M. Polycarpou, “Cooperative receding horizon 3D coverage con- trol with a team of networked aerial agents,” in 2023 62nd IEEE Conference on Decision and Control (CDC) . IEEE, 2023, pp. 4399– 4404

    2023

  14. [22]

    Unscented optimal control for 3D coverage planning with an autonomous UA V agent,

    ——, “Unscented optimal control for 3D coverage planning with an autonomous UA V agent,” in 2023 International Conference on Unmanned Aircraft Systems (ICUAS) . IEEE, 2023, pp. 703–712

    2023

  15. [23]

    Rapidly-exploring random trees: A new tool for path planning,

    S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Computer Science Department, Iowa State University , 1998

    1998

  16. [24]

    Rrt-connect: An efficient approach to single-query path planning,

    J. J. Kuffner and S. M. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automa- tion. Symposia Proceedings (Cat. No. 00CH37065) , vol. 2. IEEE, 2000, pp....

    2000

  17. [25]

    Machine learning guided exploration for sampling-based motion planning algorithms,

    O. Arslan and P. Tsiotras, “Machine learning guided exploration for sampling-based motion planning algorithms,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 2646–2652

    2015

  18. [26]

    Deep neural network enhanced sampling-based path planning in 3d space,

    J. Wang, X. Jia, T. Zhang, N. Ma, and M. Q.-H. Meng, “Deep neural network enhanced sampling-based path planning in 3d space,” IEEE Transactions on Automation Science and Engineering , vol. 19, no. 4, pp. 3434–3443, 2021

    2021

  19. [27]

    Deep sequential models for sampling-based planning,

    Y .-L. Kuo, A. Barbu, and B. Katz, “Deep sequential models for sampling-based planning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 6490– 6497

    2018

  20. [28]

    Sampling-based algorithms for optimal motion planning,

    S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The international journal of robotics research , vol. 30, no. 7, pp. 846–894, 2011

    2011

  21. [29]

    Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,

    J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” in 2014 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2014, pp. 2997– 3004

    2014

  22. [30]

    Path planning based on mixed algorithm of rrt and artificial potential field method,

    S. Huang, “Path planning based on mixed algorithm of rrt and artificial potential field method,” in 2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) . IEEE, 2021, pp. 149–155

    2021

  23. [31]

    Path planning of a mobile robot based on the improved rrt algorithm,

    X. Li and Y . Tong, “Path planning of a mobile robot based on the improved rrt algorithm,” Applied Sciences, vol. 14, no. 1, 2024

    2024

  24. [32]

    Rrt-cbf based motion planning,

    L. Liu, Y . Zhang, L. Zhang, and M. Kermanshabi, “Rrt-cbf based motion planning,” arXiv preprint arXiv:2410.00343 , 2024

    2024 arXiv

  25. [33]

    Neural informed rrt*: Learning-based path planning with point cloud state representations under admissible ellipsoidal constraints,

    Z. Huang, H. Chen, J. Pohovey, and K. Driggs-Campbell, “Neural informed rrt*: Learning-based path planning with point cloud state representations under admissible ellipsoidal constraints,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp....

    2024

  26. [34]

    Neural rrt*: Learning-based optimal path planning,

    J. Wang, W. Chi, C. Li, C. Wang, and M. Q.-H. Meng, “Neural rrt*: Learning-based optimal path planning,” IEEE Transactions on Automation Science and Engineering , vol. 17, no. 4, pp. 1748–1758, 2020

    2020

  27. [35]

    Llama: Open and efficient foundation language models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi`ere, N. Goyal, E. Hambro, F. Azhar et al., “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  28. [36]

    Language models are few-shot learners,

    T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165, 2020

    2005 arXiv

  29. [37]

    Navgpt: Explicit reasoning in vision- and-language navigation with large language models,

    G. Zhou, Y . Hong, and Q. Wu, “Navgpt: Explicit reasoning in vision- and-language navigation with large language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 7, 2024, pp. 7641–7649

    2024

  30. [38]

    Llm-a*: Large language model enhanced incremental heuristic search on path planning,

    S. Meng, Y . Wang, C.-F. Yang, N. Peng, and K.-W. Chang, “Llm-a*: Large language model enhanced incremental heuristic search on path planning,” arXiv preprint arXiv:2407.02511 , 2024

    2024 arXiv

  31. [39]

    A formal basis for the heuristic determination of minimum cost paths,

    P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics , vol. 4, no. 2, pp. 100–107, 1968. [Online]. Available: https://doi.org/10.1109/tssc.1968.300136

    1968

  32. [40]

    Linear flight control techniques for unmanned aerial vehicles,

    J. P. How, E. Frazzoli, and G. V . Chowdhary, “Linear flight control techniques for unmanned aerial vehicles,” in Handbook of unmanned aerial vehicles. Springer, 2015, pp. 529–576

    2015

  33. [41]

    Quadratic programming,

    J. Nocedal and S. J. Wright, “Quadratic programming,” Numerical optimization, pp. 448–492, 2006

    2006

  34. [42]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V . Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems, vol. 35, pp. 24 824–24 837, 2022

    2022

  35. [43]

    Arc-length parameterized spline curves for real-time simulation,

    H. Wang, J. Kearney, and K. Atkinson, “Arc-length parameterized spline curves for real-time simulation,” in Proc. 5th International Conference on Curves and Surfaces , vol. 387396, 2002

    2002

  36. [44]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

    2023 arXiv

  37. [45]

    The llama 3 herd of models,

    A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Yang, A. Fan et al., “The llama 3 herd of models,” arXiv preprint arXiv:2407.21783 , 2024

    2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.