Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles

Jingwen Wei

REVIEW 4 major objections 4 minor 190 references

Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The report claims that a hybrid of global RRT planning and reactive occlusion-line steering yields real-time collision-free navigation for UAVs and UGVs in dynamic 3D environments, with forest-fire rescue as the target application.

desk verdict A self-admitted compilation of prior conference papers whose forest-fire simulation violates its own feasibility condition; the central rescue claim is unreliable. read the letter →

arxiv 2506.20311 v1 pith:7UCMNL3J submitted 2025-06-25 cs.RO cs.SYeess.SY

Jingwen Wei This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords obstacleavoidancehybridpathplanningreactivenavigationunmannedaerialvehiclegroundforestfirerescueRRTcoverage

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This report is trying to establish that a hybrid navigation architecture—a global sampler (RRT-connect, a sampling-based path finder) that lays down a reference path, plus a reactive sensor-driven controller that steers around local obstacles—can give unmanned aerial and ground vehicles real-time, collision-free movement in dynamic three-dimensional environments. The target application is search and rescue in forest fires, where the fire itself is modeled as a deformable, moving obstacle with a measurable surface velocity. The claim is supported stage by stage: a 2D ground-robot fusion method, a 2D deformable-obstacle extension with replanning, a 3D reactive method over uneven terrain, a fire-rescue hybrid planner, and finally a multi-UAV/UGV cooperative framework. Across the stages the same reactive law—choose the smaller of two angular corrections built from occlusion lines—does the local avoidance, while a switcher and a replan rule prevent dead zones. If the claim holds, rescue vehicles could navigate disaster zones with only distance and surface-velocity measurements rather than full maps.

What carries the argument

The load-bearing object is the occlusion line, a vector $\tau_i^{(j)}(t)=(v_{\max}-v)[\cos\beta_i^{(j)}(t),\sin\beta_i^{(j)}(t)]$ (or its 3D analogue $l_i(t)=\Delta V(t)[\cos\beta_i(t),\sin\beta_i(t)]$), formed from the sensor-measured distance to an obstacle and an enlarged vision cone with a safe escape angle $\alpha_{\text{safe}}$. These two lines bracket the directions the vehicle could take around the obstacle, and the controller picks the one that makes the smallest angle with the obstacle velocity. The supporting machinery is the mode switcher: the vehicle tracks the RRT waypoints at full speed in routine mode, switches to reactive escape mode when distance crosses $d_{\text{safe}}$ with $\dot d<0$, and switches back only when heading points to the next waypoint and a time-observer has elapsed, which avoids the dead-zone oscillation of distance-only switching. In 3D the avoidance plane $H_a(t)$—constructed from vehicle position, nearest obstacle point, and target, with its normal $\vec n_{H_a}(t)=\vec T\times e_a(t)$—carries the reactive law, and a replan rule triggered by an infeasible turning radius $R<R_{\min}$ restores a global path when local avoidance gets stuck.

What would settle it

A field or hardware-in-the-loop test with realistic range sensors: feed the controller distances with added noise and estimate surface velocities from those measurements instead of supplying them; if the minimum distance $d(t)$ ever falls below $d_{\text{safe}}$ while the vehicle is in reactive mode, the claimed collision-free guarantee is violated.

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Extended reading notes

Core claim

The central claim is that collision-free navigation in unknown, dynamic environments does not require a full map or heavy computation: a layered planner that alternates between tracking a globally planned reference path and executing a reactive escape law is enough. The report shows this in a specific way, deriving occlusion lines from the measured distance to the obstacle and its surface velocity, then choosing the avoidance direction with the smaller angular deviation $\min_{j=1,2}|\varphi(v+\tau_i^{(j)},v_i)|$. In 3D the same idea is lifted onto a collision-avoidance plane defined by the vehicle position, the nearest obstacle point, and the target, using a rotation matrix to convert coordinates into the body frame. The fire-spread simulations treat the advancing fire as a deformable obstacle whose boundary velocity is sensed, and compare the hybrid method with a purely reactive one; the reported result is that the hybrid method reaches the goal with shorter path, less time, and smaller turning angles. The report concludes that the framework provides a safe and efficient rescue navigation capability, grounded in those simulations.

Load-bearing premise

The load-bearing premise is that the onboard sensors can measure, without noise, delay, or occlusion, the exact distance to each obstacle and the velocity of its moving or deforming surface; every simulation gives the controller that information directly.

Editorial extensions

If this is right

A vehicle using this hybrid law needs only distance and obstacle surface velocity, not a prebuilt map, to navigate dynamic environments.
In the reported simulations the hybrid method outperforms the purely reactive method on path length, search time, and turning angle, with the gap growing as the environment becomes more complex.
The same reactive law transfers from 2D ground robots to 3D UAV flight by rotating coordinates into the body frame and performing avoidance on a plane.
Coordinated rescue is feasible with a centralized ground station assigning UAV coverage sub-areas and UGV rescue targets, with each vehicle running the same local avoidance law.

Reading between the lines

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

If the exact-surface-velocity sensing assumption is weakened, the occlusion-line law would need an estimator, and the safe-distance guarantee would become probabilistic; this is not addressed in the paper.
The report's own future-work discussion suggests the same reactive law could apply to autonomous underwater and surface vehicles, so the mechanism is arguably domain-agnostic, but the evidence here is only planar and 3D aerial/ground simulations.
The centralized ground-station design limits scalability; a distributed version of the same avoidance law is a natural next step that the paper leaves open.
The replan rule could be stress-tested in a random obstacle field: if replanning frequency grows without bound, real-time operation would degrade, a behavior the paper does not quantify.

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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

Punchline: this is a compilation, not a new result. Sections III–VII are explicitly based on prior conference papers. The one possible saving grace—a convincing forest-fire rescue validation—collapses on inspection: the simulation sets wind speed to [-8,-8,0,1] with Vmax=4 m/s, giving ||v_wind|| ≈ 11.36, while the paper's own feasibility condition demands ||v_wind|| < Vmax. I checked the stress-test note; it holds up. With the fire boundary allowed to move faster than the UAV, the reported collision-free outcome is scenario-specific at best.

Credit where it's due: the report is a readable overview of one line of hybrid RRT/reactive navigation, from 2D ground robots through deformable obstacles to a 3D reactive extension using an avoidance plane. The switcher's time-hold condition is a sensible fix for chattering. The non-fire simulations do show collision-free paths in the plotted cases, and the comparison against a single reactive baseline is a real check, if limited.

Soft spots, in order of severity. The forest-fire simulation violates the paper's own feasibility condition, so the main application claim is not supported. The abstract promises 'mathematical and simulation-based evidence,' but there are no formal proofs of collision avoidance or stability. The reactive controllers assume perfect real-time sensing of obstacle surface velocity, which is load-bearing and untested; no noise, latency, or occlusion. No code or data are released. Free parameters (alpha_safe, d0, K, kappa, rho) are tuned per case with no sensitivity analysis. The 'disaster coefficient J3' is invoked without derivation or calibration. The fire model is a toy, with coefficients that don't come from any physical source.

The paper is honest that it builds on prior work, and the literature review is broad, though heavily weighted toward the authors' own citations. But these positives don't outweigh the central flaw.

Who's it for? A reader wanting a compact tour of one author's hybrid navigation line, or someone studying how to assemble a thesis from conference papers. It is not for someone seeking new algorithms, verified safety, or reproducible results.

Recommendation: desk reject for a research venue. The paper makes no new contribution and the forest-fire validation has a load-bearing internal inconsistency. If the author reworks it with corrected simulations, real sensor models, code, and proper baselines, there might be a publishable journal paper underneath—but this version isn't it.

Referee Report

4 major / 4 minor

Summary. The report develops a sequence of hybrid global/reactive navigation algorithms: a 2D hybrid planner for non-holonomic ground robots among moving and deformable obstacles, a 3D reactive avoidance method for UAVs over uneven terrain, a forest-fire rescue planner that couples an RRT-based global layer with a reactive layer and a simplified fire-spread model, and a multi-UAV/UGV cooperative surveillance and evacuation framework. Each phase is validated through MATLAB simulations and compared with a purely reactive method, generally showing shorter paths and shorter search times. The abstract and Section VIII claim that the methods are supported by mathematical and simulation-based evidence, but the body provides controller heuristics and simulated trajectories rather than formal guarantees.

Significance. If the algorithms perform as claimed, the work offers a practical template for hierarchical rescue navigation: global waypoints from RRT/RRT-connect, reactive avoidance based on distance and obstacle velocity, and a layered decision architecture with replanning. The comparative experiments in Sections III, IV, and VI provide quantitative evidence that the hybrid approach reduces path length and search time relative to the reactive baseline, and the plotted trajectories in Section V demonstrate collision-free behavior in the tested scenarios. However, the contribution is incremental and largely assembled from the author's own conference papers, the validation is entirely simulation-based under strong sensing assumptions, and the fire-rescue scenarios contain an internal parameter inconsistency that undermines their claimed support for the central 'collision-free in forest fires' statement.

major comments (4)

[Section VI-B2 Remark and Section VI-D] The feasibility condition in the Remark states 0 ≤ ||v_wind|| < V_max, with V_max = 4 m/s, yet Section VI-D sets the wind speed vector to [-8, -8, 0, 1], whose Euclidean norm is sqrt(129) ≈ 11.36 m/s, exceeding V_max by a factor of 2.84. The same violation occurs in Section VII-D1 Case 2 with wind [8, 8, 0]. Because this condition is the stated necessary condition for the reactive controller to guarantee avoidance, the forest-fire simulations cannot be taken as evidence of collision-free capability. The paper also never reports the actual maximum boundary velocity of the deformable fire domain generated by equations (51)-(53), so the reader cannot verify whether the obstacle velocity constraint was satisfied in the experiments.
[Sections III-C, IV-C, V-C and the abstract's 'mathematical validation' claim] The abstract and Section VIII-A claim that the methods are substantiated by thorough mathematical analysis, but no theorem or proof establishes d(t) ≥ d_safe or d(t) ≥ d0 for all t. For example, Section IV-C Equations (20)-(25) define a switching control law and a sign-based function f(·,·), yet no Lyapunov, barrier-certificate, or reachability argument is provided. This is a load-bearing gap because the central claim is collision-free navigation. The gap is aggravated by Assumptions 1 and 2 in Section V-B2, which require onboard sensors to measure the exact surface velocity vx of unknown obstacles and the relative velocity Δv; the simulations grant this information perfectly, with no treatment of sensing noise, latency, or occlusions.
[Section VI-B2, Equations (51)-(53)] The forest fire spread model is insufficiently specified for verification. Equation (51) gives dTi(t)/dt as a sum over the 26-neighbor set N(i), with H_ij depending on T_trigger, κ, ρ, and the wind dot product W_ij(t) = V_wind(t)·D_ij + κ, but the paper does not state the time discretization scheme, the value of T_trigger, the sample time of the fire model, or how the boundary and boundary velocity of the deformable fire domain are extracted from the temperature field. Furthermore, the wind vector in Section VI-D is listed as [-8, -8, 0, 1], which has four components in a three-dimensional environment, and the meaning of the fourth component is unexplained. Without the boundary-velocity computation, the reported 'collision-free in fire' result cannot be independently checked against the stated feasibility condition.
[Section VI-C, Remark 1 and Equation (59)] The multi-objective optimization Q = αJ1 + βJ2 + γJ3 is presented as a key part of the rescue path planner, but J3, described as a 'disaster coefficient' that minimizes environmental risks from fire proximity and uneven terrain, is never defined mathematically. The text says 'we introduced J3' but gives no expression, constraints, or weighting rationale. As a result, the actual path-planning objective used in the Section VI simulations is not fully specified, which limits the reproducibility of the claimed improvements in path length and search time.

minor comments (4)

[Throughout] The manuscript contains numerous typographical and grammatical errors that should be corrected: for example, 'trrigerd' in the caption of Fig. 10, 'lack og pre-disaster warning' at the start of Section VII, and 'objectives' where 'obstacles' is intended in several places.
[Section VII-B, Equation (60)] Equation (60) writes dot(S_u(t)) = |v(t)| omega(t) with v(t) described as a 'velocity vector', but the notation |v(t)| suggests a scalar speed, and the later text treats v(t) as both scalar and vector in different model descriptions; this should be made consistent.
[Sections V-B and VI-B] The rotation matrix D in Equation (33) and the matrix b_gR in Equation (41) are identical, and a third copy appears as M in Equation (65); the paper should define this matrix once and reuse it, to avoid apparent inconsistency and unnecessary repetition.
[Section VI-D and Section VII-D1] The fire simulation parameters do not report how the discretized temperature field is initialized, the value of T_trigger, or the integration time step; reporting these values would materially improve reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity; the report is heavily self-referential but the core algorithms are implemented and simulated directly rather than reduced to fitted inputs.

full rationale

The report is a compilation of the author's own prior conference papers, and several parts cite those papers for the reactive obstacle-avoidance components (e.g., Section VI-B1 refers to 'prior research [133]' for the avoidance-plane construction, and Section VII-C2 says the reactive methodology 'draws inspiration from the works presented in [90] and [132]'). These self-citations are visible but not load-bearing: the underlying 2D and 3D reactive controllers, RRT-based planners, switching rules, and coordinate transformations are re-derived and stated directly in Sections III, IV, and V of the same report, so the reader can check the derivation without relying solely on the self-citations. No parameter is fitted to reproduce a target path or a target safety distance; the simulations execute the proposed controllers and report path length, time, and clearance, which is a direct implementation check rather than a tautology. The paper's sensitivity to sensing assumptions (exact real-time knowledge of obstacle surface velocities) and the internal inconsistency in Section VI-D, where the wind vector [-8,-8,0,1] has norm about 11.36 m/s while the feasibility condition requires ||v_wind|| < V_max = 4 m/s, are serious validity concerns but they are not circular reductions: they concern whether the simulation scenario satisfies the algorithm's assumptions, not whether the derivation secretly assumes its conclusion. Accordingly, the central claim does not reduce by construction to its inputs, so the appropriate circularity finding is low with no specific circular step identified.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The central algorithm depends on several hand-picked thresholds, safety distances, avoidance angles, and mode-switch times, plus an uncalibrated fire-spread model. These parameters are scenario-specific rather than derived or fitted to external data.

free parameters (6)

alpha_safe (α_safe / α_0 / α_O) = π/5 in most simulations
Constant avoiding-angle parameter used to enlarge the vision cone around obstacles in Equations (23), (38), and (57); chosen by hand with no tuning rule or sensitivity analysis.
safety distance d0/d_epsilon = 4.5 m, 3 m, 8 m, 5 m, 4 m per case
Minimum allowed clearance to obstacles; set manually per simulation scenario and directly affects when avoidance or replanning triggers.
mode-switch time K (T_ob) = 1.5 s
Minimum time in avoidance mode before switching back to path tracking; chosen ad hoc to prevent mode chattering.
fire model coefficients κ and ρ = κ=0.05, ρ=1 in Section VI-D
Influence factors for wind and no-wind fire spread in Equations (52-53); not calibrated against real fire data.
detection and trigger distances C, d_safe, R_sensor = C=1.1dε or 3dε; R_sensor=5 to 30 m
Distances at which reactive avoidance or replanning is triggered; hand-picked per simulation case.
T_trigger = not specified
Temperature threshold in the fire spread model Equation (52); never assigned a numerical value, leaving the model under-specified.

assumptions (5)

domain assumption The nonholonomic kinematic model with bounds on linear and angular velocity (Equations 2-4, 26-28, 44-48) accurately represents UAV/UGV motion, ignoring rotor drag and disturbances.
Used throughout all sections to design controllers; not justified for multirotor UAVs beyond planar motion and ignores real aerodynamics.
domain assumption Obstacles are closed and bounded, and their velocity is measurable in real time by onboard sensors; obstacle speed is below Vmax (Assumptions 1-2 in Section V-B and Section IV-B).
Required for the reactive occlusion-line law; never validated with a sensor model or noise analysis.
ad hoc to paper Forest fire can be represented as a deformable moving plane or domain with a known boundary velocity; fire spread is intentionally left undefined (Section IV-B, Section VI-B2).
The simulation obstacle is not a physically validated fire model, so collision-free behavior in simulation may not transfer to real fire dynamics.
ad hoc to paper The simplified fire spread model (Equations 51-53) with temperature triggers and wind weighting is adequate for testing navigation (Section VI-B2).
Parameters κ, ρ, and T_trigger are uncalibrated, and the model is not compared with real fire spread data.
domain assumption Obstacles in 3D can be approximated as cylinders or ellipsoids to define the collision avoidance plane (Remark 3.4, Section V-C).
The 3D reactive method depends on this geometric simplification for computing tangent directions and avoidance planes.

invented entities (1)

Disaster coefficient J3
purpose: Intended to penalize proximity to fire and terrain risk in the multi-objective path optimization Q in Equation (59).
Mentioned in Remark 1 of Section VI but never defined numerically or used in the simulations, so it has no falsifiable handle.

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Cite this review

Pith. "Pith review of Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles." pith.science (2026). https://pith.science/paper/7UCMNL3J

@misc{pith2026250620311,
  author       = {Pith},
  title        = {Pith review of: Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7UCMNL3J}},
  note         = {Machine review of arXiv:2506.20311}
}

read the original abstract

The growing use of mobile robots in sectors such as automotive, agriculture, and rescue operations reflects progress in robotics and autonomy. In unmanned aerial vehicles (UAVs), most research emphasizes visual SLAM, sensor fusion, and path planning. However, applying UAVs to search and rescue missions in disaster zones remains underexplored, especially for autonomous navigation. This report develops methods for real-time and secure UAV maneuvering in complex 3D environments, crucial during forest fires. Building upon past research, it focuses on designing navigation algorithms for unfamiliar and hazardous environments, aiming to improve rescue efficiency and safety through UAV-based early warning and rapid response. The work unfolds in phases. First, a 2D fusion navigation strategy is explored, initially for mobile robots, enabling safe movement in dynamic settings. This sets the stage for advanced features such as adaptive obstacle handling and decision-making enhancements. Next, a novel 3D reactive navigation strategy is introduced for collision-free movement in forest fire simulations, addressing the unique challenges of UAV operations in such scenarios. Finally, the report proposes a unified control approach that integrates UAVs and unmanned ground vehicles (UGVs) for coordinated rescue missions in forest environments. Each phase presents challenges, proposes control models, and validates them with mathematical and simulation-based evidence. The study offers practical value and academic insights for improving the role of UAVs in natural disaster rescue operations.

Figures

Figures reproduced from arXiv: 2506.20311 by the authors.

**Figure 2.** Description of fixed angle and observation angle of [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Simulation results of the Hybrid algorithm in [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (27 more)

**Figure 4.** Figure 4: Simulation results of hybrid algorithm operated in a [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 6.** Figure 6: The distance between each obstacles by hybrid [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 5.** Figure 5: The operations by hybrid method and reactive [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 7.** Figure 7: The turning angle of the vehicle operated in case 4. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

**Figure 8.** Figure 8: The principle of hybrid navigation strategy with hier [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

**Figure 9.** Figure 9: Pruning progress algorithm 2) Execution Layer Since this section is an extension of section III-C, the navigation modes used here are consistent with those discussed earlier. However, considering the impact of these modes on the robot model, we have rewritten and retai…

**Figure 12.** Figure 12: Distance to obstacles, (a) is UAV operated by hybrid [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

**Figure 10.** Figure 10: UAV navigated by the hybrid algorithm operates in [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

**Figure 11.** Figure 11: UAV navigated by the purely reactive algorithm [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

**Figure 14.** Figure 14: illustrates the process of rotational transformation from the Fg frame to the Fu frame. In the figure, Pst represents the starting point, and Pgo represents the end point. The coordinate conversion matrix D is given by: D =   sin α cos β cos α cos β sin β − sin α co…

**Figure 15.** Figure 15: Choosing a collision avoidance plane when the UAV [PITH_FULL_IMAGE:figures/full_fig_p021_15.png]

**Figure 16.** Figure 16: One probably method for determining the avoiding [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]

**Figure 18.** Figure 18: Case 2: Simulation results with no unknown obstacles [PITH_FULL_IMAGE:figures/full_fig_p023_18.png]

**Figure 22.** Figure 22: In Fig. 21 illustrates the simulation scenario of Case [PITH_FULL_IMAGE:figures/full_fig_p023_22.png]

**Figure 19.** Figure 19: Simulation results in adding multiply moving obsta [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]

**Figure 21.** Figure 21: Case 3(1): Simulation results with region spanned [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]

**Figure 22.** Figure 22: Case 3(2): Simulation results with region spanned [PITH_FULL_IMAGE:figures/full_fig_p024_22.png]

**Figure 23.** Figure 23: Motion Planning Strategy 1) Deployment layer In the Deployment layer, the algorithm begins by utilizing prior knowledge to generate a feasible route. The UAV’s initial position, target destination, and surrounding terrain data are defined as key variables in this proc…

**Figure 25.** Figure 25: Sequential Visualization of Pathway Optimization: [PITH_FULL_IMAGE:figures/full_fig_p028_25.png]

**Figure 26.** Figure 26: The case in forest environment without fire spread: [PITH_FULL_IMAGE:figures/full_fig_p029_26.png]

**Figure 27.** Figure 27: The case in forest environment with fire spread: [PITH_FULL_IMAGE:figures/full_fig_p030_27.png]

**Figure 28.** Figure 28: UAV Model cameras will not be examined in depth, and by default they are capable of functioning at a safe distance from a bush fire.In addition the UAV is equipped with a horn for vocalization to inform and evacuate the crowd. Again, it has been simplified in the mode…

**Figure 30.** Figure 30: Single UAV and UGV cooperation in simple case: [PITH_FULL_IMAGE:figures/full_fig_p034_30.png]

**Figure 32.** Figure 32: Simulation of UAV patrol status without wildfire [PITH_FULL_IMAGE:figures/full_fig_p035_32.png]

**Figure 31.** Figure 31: Proposed UAV/UGV cooperation algorithm simula [PITH_FULL_IMAGE:figures/full_fig_p035_31.png]

**Figure 33.** Figure 33: Simulation of single UAV and single UGV coopera [PITH_FULL_IMAGE:figures/full_fig_p035_33.png]

**Figure 34.** Figure 34: Simulation of multiple UAVs and UGVs cooperation [PITH_FULL_IMAGE:figures/full_fig_p036_34.png]

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Motion planning with sequential convex optimization and convex collision checking,

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2014
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A novel real-time penetration path planning algorithm for stealth UA V in 3d complex dynamic environment,

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2020
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3D path planning and stereo-based obstacle avoidance for rotorcraft UA Vs,

S. Hrabar, “3D path planning and stereo-based obstacle avoidance for rotorcraft UA Vs,” in2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2008, pp. 807–814

2008
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RRT* N: An efficient approach to path planning in 3D for static and dynamic environments,

H. Mohammed, L. Romdhane, and M. A. Jaradat, “RRT* N: An efficient approach to path planning in 3D for static and dynamic environments,”Advanced Robotics, vol. 35, no. 3-4, pp. 168–180, 2021

2021
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Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment,

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Obstacle avoidance for mobile robot based on improved dynamic window approach,

X. Li, F. Liu, J. Liu, and S. Liang, “Obstacle avoidance for mobile robot based on improved dynamic window approach,”Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, no. 2, pp. 666–676, 2017

2017
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Sensor-based robotic model for vehicle accident avoidance,

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A sensor based robotic model for vehicle collision reduction,

I. Ullah, F. Ullah, and Q. Ullah, “A sensor based robotic model for vehicle collision reduction,” inInternational Conference on Computer Networks and Information Technology. IEEE, 2011, pp. 251–255

2011
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Bioinspired bearing only motion camou- flage UA V guidance for covert video surveillance of a moving target,

A. V . Savkin and H. Huang, “Bioinspired bearing only motion camou- flage UA V guidance for covert video surveillance of a moving target,” IEEE Systems Journal, vol. 15, no. 4, pp. 5379–5382, 2020

2020
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Joint multi-uav path planning and LoS communication for mobile-edge computing in IoT networks with RISs,

A. V . Savkin, C. Huang, and W. Ni, “Joint multi-uav path planning and LoS communication for mobile-edge computing in IoT networks with RISs,”IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2720–2727, 2022

2022
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Multi-goal multi-agent pickup and delivery,

Q. Xu, J. Li, S. Koenig, and H. Ma, “Multi-goal multi-agent pickup and delivery,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 9964–9971

2022
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Path planning strategies for UA VS in 3D environments,

L. De Filippis, G. Guglieri, and F. Quagliotti, “Path planning strategies for UA VS in 3D environments,”Journal of Intelligent & Robotic Systems, vol. 65, pp. 247–264, 2012

2012
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Three-dimensional offline path planning for UA Vs using multiobjective evolutionary algorithms,

S. Mittal and K. Deb, “Three-dimensional offline path planning for UA Vs using multiobjective evolutionary algorithms,” in2007 IEEE congress on evolutionary computation. IEEE, 2007, pp. 3195–3202

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Predicting energy consumption of ground robots on uneven terrains,

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2021
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Multi-uav navigation for optimized video surveillance of ground vehicles on uneven terrains,

A. V . Savkin and H. Huang, “Multi-uav navigation for optimized video surveillance of ground vehicles on uneven terrains,”IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 10 238–10 242, 2023

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Path planning for wheeled mobile robot in partially known uneven terrain,

B. Zhang, G. Li, Q. Zheng, X. Bai, Y . Ding, and A. Khan, “Path planning for wheeled mobile robot in partially known uneven terrain,” Sensors, vol. 22, no. 14, p. 5217, 2022

2022
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Proactive deployment of aerial drones for coverage over very uneven terrains: A version of the 3D art gallery problem,

A. V . Savkin and H. Huang, “Proactive deployment of aerial drones for coverage over very uneven terrains: A version of the 3D art gallery problem,”Sensors, vol. 19, no. 6, p. 1438, 2019

2019
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Forest fire spread simulat- ing model using cellular automaton with extreme learning machine,

Z. Zheng, W. Huang, S. Li, and Y . Zeng, “Forest fire spread simulat- ing model using cellular automaton with extreme learning machine,” Ecological Modelling, vol. 348, pp. 33–43, 2017

2017
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Navigation of a network of aerial drones for monitoring a frontier of a moving environmental disaster area,

A. V . Savkin and H. Huang, “Navigation of a network of aerial drones for monitoring a frontier of a moving environmental disaster area,” IEEE Systems Journal, vol. 14, no. 4, pp. 4746–4749, 2020

2020
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A hybrid algorithm for collision-free navigation of a non- holonomic ground robot in dynamic environments with steady and moving obstacles,

J. Wei, “A hybrid algorithm for collision-free navigation of a non- holonomic ground robot in dynamic environments with steady and moving obstacles,” in2023 42nd Chinese Control Conference (CCC). IEEE, 2023, pp. 2928–2933

2023
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A method for collision-free UA V navigation around moving obstacles over an uneven terrain,

J. Wei and S. Li, “A method for collision-free UA V navigation around moving obstacles over an uneven terrain,” in2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2023, pp. 1–6

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Real-time 3D visualization of forest fire spread based on tree morphology and finite state machine,

J. You, Y . Huai, X. Nie, and Y . Chen, “Real-time 3D visualization of forest fire spread based on tree morphology and finite state machine,” Computers & Graphics, vol. 103, pp. 109–120, 2022

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Simulation of forest fire spread based on artificial intelligence,

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Development and validation of a physics- based urban fire spread model,

K. Himoto and T. Tanaka, “Development and validation of a physics- based urban fire spread model,”Fire Safety Journal, vol. 43, no. 7, pp. 477–494, 2008

2008
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Fire shapes and the adequacy of fire-spread models,

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Integrating fire spread patterns in fire modelling at landscape scale,

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2016
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Manipulator motion planning based on prior knowledge guidance in complex constrained environment,

X. Wang, D. Zhu, C. Guo, C. Cheng, and Z. Zeng, “Manipulator motion planning based on prior knowledge guidance in complex constrained environment,” in2023 42nd Chinese Control Conference (CCC). IEEE, 2023, pp. 4872–4879

2023
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Networked unmanned aerial vehicles for surveillance and monitoring: A survey,

X. Li and A. V . Savkin, “Networked unmanned aerial vehicles for surveillance and monitoring: A survey,”Future Internet, vol. 13, no. 7, p. 174, 2021

2021
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Navigation of a UA V network for optimal surveillance of a group of ground targets moving along a road,

A. V . Savkin and H. Huang, “Navigation of a UA V network for optimal surveillance of a group of ground targets moving along a road,”IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9281–9285, 2021

2021
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Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread,

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2018
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An accurate UA V 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 UA V 3-D path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm,”IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2658–2671, 2022

2022
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HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots,

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2020
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Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning,

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2022
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Collaborative air- ground target searching in complex environments,

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Aerial coverage planning for areas hidden from the view of a moving ground vehicle,

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Asymptotically optimal path planning for ground surveillance by a team of uavs,

A. V . Savkin and H. Huang, “Asymptotically optimal path planning for ground surveillance by a team of uavs,”IEEE Systems Journal, vol. 16, no. 2, pp. 3446–3449, 2021

2021
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Addressing disasters in smart cities through UA Vs path planning and 5G commu- nications: A systematic review,

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2021
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Path planning for a solar-powered UA V inspecting mountain sites for safety and rescue,

H. Huang and A. V . Savkin, “Path planning for a solar-powered UA V inspecting mountain sites for safety and rescue,”Energies, vol. 14, no. 7, p. 1968, 2021

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Optimal path planning of solar-powered UA V using gravitational potential energy,

J.-S. Lee and K.-H. Yu, “Optimal path planning of solar-powered UA V using gravitational potential energy,”IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, pp. 1442–1451, 2017

2017
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Au- tonomous UA V surveillance in complex urban environments,

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2009
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Online UA V trajectory planning for covert video surveillance of mobile targets,

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2021
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Review of power device for solar-powered aircraft applications,

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2019
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Solar powered UA V: Design and experiments,

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Solar-powered aircraft: Energy- optimal path planning and perpetual endurance,

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2009
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Autonomous navigation of a solar- powered UA V for secure communication in urban environments with eavesdropping avoidance,

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2020
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Energy-optimal path planning for solar-powered UA V with tracking moving ground target,

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2016
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Coverage path planning for UA Vs photogrammetry with energy and resolution constraints,

C. Di Franco and G. Buttazzo, “Coverage path planning for UA Vs photogrammetry with energy and resolution constraints,”Journal of Intelligent & Robotic Systems, vol. 83, pp. 445–462, 2016

2016
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Energy efficient optimization for solar-powered UA V communications system,

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Flight path planning of solar-powered UA V for sustainable communication relay,

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Design of a high-altitude long- endurance solar-powered unmanned air vehicle for multi-payload and operations,

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Energy management strategy for solar-powered high-altitude long-endurance aircraft,

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Power cognition: Enabling intelligent energy harvesting and resource allocation for solar-powered UA Vs,

J. Zhang, M. Lou, L. Xiang, and L. Hu, “Power cognition: Enabling intelligent energy harvesting and resource allocation for solar-powered UA Vs,”Future Generation Computer Systems, vol. 110, pp. 658–664, 2020

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Optimal 3D- trajectory design and resource allocation for solar-powered UA V com- munication systems,

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2019
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Comprehensive optimization of energy storage and standoff tracking for solar-powered UA V,

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2020
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UA V-UGV co- operation for objects transportation in an industrial area,

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2015
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Localization in wireless sensor networks: a review,

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Wireless sensor networks for emergency navigation,

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2006
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Wireless sensor network based navigation of micro flying robots in the industrial internet of things,

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2018
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Obstacle avoidance approaches for autonomous navigation of unmanned surface vehicles,

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2018
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Autonomous navigation of a team of unmanned surface vehicles for intercepting intruders on a region boundary,

A. Marzoughi and A. V . Savkin, “Autonomous navigation of a team of unmanned surface vehicles for intercepting intruders on a region boundary,”Sensors, vol. 21, no. 1, p. 297, 2021

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Advancements in the field of autonomous underwater vehicle,

A. Sahoo, S. K. Dwivedy, and P. Robi, “Advancements in the field of autonomous underwater vehicle,”Ocean Engineering, vol. 181, pp. 145–160, 2019

2019
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Optimal navigation of an unmanned surface vehicle and an autonomous underwater vehicle collaborating for reliable acoustic communication with collision avoid- ance,

A. V . Savkin, S. C. Verma, and S. Anstee, “Optimal navigation of an unmanned surface vehicle and an autonomous underwater vehicle collaborating for reliable acoustic communication with collision avoid- ance,”Drones, vol. 6, no. 01, p. 27, 2022

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A formation autonomous navigation system for unmanned surface vehicles with distributed con- trol strategy,

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RobustH ∞ control of uncertain nonlinear systems,

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Basic problems in stability and design of switched systems,

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Stable switched controllers for a swarm of UGVs for hierarchal landmark navigation,

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Adaptive Kalman filtering for vehicle navigation,

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Attitude estimation for UA V using extended Kalman filter,

X. Jing, J. Cui, H. He, B. Zhang, D. Ding, and Y . Yang, “Attitude estimation for UA V using extended Kalman filter,” in2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017, pp. 3307–3312

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Interacting multiple model UA V navigation algorithm based on a robust cubature Kalman filter,

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2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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Develop an unmanned aerial vehicle based automatic aerial spraying system,

X. Xue, Y . Lan, Z. Sun, C. Chang, and W. C. Hoffmann, “Develop an unmanned aerial vehicle based automatic aerial spraying system,” Computers and electronics in agriculture, vol. 128, pp. 58–66, 2016

2016

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Monitoring road traffic with a UA V-based system,

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2018

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Applications of unmanned aerial vehicle (UA V) in road safety, traffic and highway infrastruc- ture management: Recent advances and challenges,

F. Outay, H. A. Mengash, and M. Adnan, “Applications of unmanned aerial vehicle (UA V) in road safety, traffic and highway infrastruc- ture management: Recent advances and challenges,”Transportation research part A: policy and practice, vol. 141, pp. 116–129, 2020

2020

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A review of UA V monitoring in mining areas: Current status and future perspectives,

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2019

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UA V-assisted disaster management: Applications and open issues,

M. Erdelj and E. Natalizio, “UA V-assisted disaster management: Applications and open issues,” in2016 international conference on computing, networking and communications (ICNC). IEEE, 2016, pp. 1–5

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Toward a fully autonomous UA V: Research platform for indoor and outdoor urban search and rescue,

T. Tomic, K. Schmid, P. Lutz, A. Domel, M. Kassecker, E. Mair, I. L. Grixa, F. Ruess, M. Suppa, and D. Burschka, “Toward a fully autonomous UA V: Research platform for indoor and outdoor urban search and rescue,”IEEE robotics & automation magazine, vol. 19, no. 3, pp. 46–56, 2012

2012

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Multi-UA V oxyrrhis marina-inspired search and dynamic formation control for forest fire- fighting,

K. Harikumar, J. Senthilnath, and S. Sundaram, “Multi-UA V oxyrrhis marina-inspired search and dynamic formation control for forest fire- fighting,”IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 863–873, 2018

2018

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The adaptive vortex search algorithm of optimal path planning for forest fire rescue UA V,

C. Wang, P. Liu, T. Zhang, and J. Sun, “The adaptive vortex search algorithm of optimal path planning for forest fire rescue UA V,” in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2018, pp. 400–403

2018