Pith. sign in

REVIEW 3 major objections 5 minor 63 references

Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a 47-gram nano-drone can hover, track, and land on a moving quadruped by sensing AC magnetic fields emitted from the ground robot, achieving about 5 cm positioning error when stationary and 8-11 cm while tracking, wit

desk verdict Credible MI docking demo for translational motion; the yaw-rotation limitation is real and should be front-and-center, but the core result holds. read the letter →

arxiv 2603.08926 v2 pith:LB32JQZ2 submitted 2026-03-09 cs.RO

Valerio Brunacci , Davide Plozza , Alessio De Angelis , Michele Magno , Tommaso Polonelli This is my paper
classification cs.RO
keywords magneto-inductivelocalizationUAV-UGVteamingnanodroneprecisionlandingfrequency-divisionmultiplexingmagneticdipolemodelinfrastructure-lessrelativeposeestimation
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 paper tries to prove that the final, hardest centimeter of drone-to-ground docking can be solved without any external infrastructure. The idea is to turn the ground vehicle into a magnetic lighthouse: four coils on the UGV emit distinct AC frequencies, and a single passive coil on the drone measures their superimposed field. From these measurements, the drone computes its 3D position relative to the UGV in real time on its tiny onboard processor and feeds it into its flight controller. The reported result is centimeter-level hovering and sub-10 cm tracking on a moving platform, with landing success rates above 80 percent. If true, it gives marsupial drone-UGV teams a way to return, dock, and recharge in GNSS-denied or visually degraded environments.

What carries the argument

The load-bearing object is the magnetic dipole measurement model combined with frequency-division multiplexing. Each UGV coil is a sinusoidal magnetic dipole with known position and fixed vertical orientation; the drone's coil senses the phasor sum, and an FFT isolates each anchor's amplitude. This turns localization into a 3D nonlinear least-squares inversion of a closed-form dipole field law (B(r) = μ0/(4πr^3)[3(m·r̂)r̂ − m]), solved by a derivative-free Nelder-Mead simplex with warm start. Its role is to convert a handful of voltage amplitudes into a body-frame-relative position that needs no external anchors, illumination, or line-of-sight.

What would settle it

Drive the UGV through an in-place 180-degree turn (or a trajectory with yaw exceeding the stated ±20° limit) while the drone attempts to track and land; if the 3D RMSE remains below about 15 cm and landing succeeds, then the fixed-orientation assumption is not actually load-bearing, whereas a large error spike or failed touchdown would confirm the paper's own limitation and narrow the claimed operational envelope.

Watch

Extended reading notes

Core claim

In the paper's own terms, the discovery is that an active, frequency-multiplexed magneto-inductive anchor-tag system can provide a continuous, unambiguous 3D relative position reference for a nano-UAV operating close to a mobile UGV, and that this reference can be computed onboard at 20 Hz under the strict size, weight, and power constraints of a palm-size drone. Using four transmit coils at 181-210 kHz modeled as magnetic dipoles, the drone's receive coil measures the induced voltage amplitudes; a warm-started Nelder-Mead optimizer solves the inverse dipole problem for position, and the result is fused into the stock extended Kalman filter alongside IMU and optical flow. Static hovering ach

Load-bearing premise

The whole scheme rests on the assumption that the four transmitter coils keep a fixed, known orientation while the drone flies, so the UGV can only move in planar translation with heading changes of at most about 20 degrees; the moment the ground robot rotates sharply, the position solver confuses rotation for translation and the docking reference degrades.

Editorial extensions

If this is right

  • The magnetic fix plugs into the same EKF interface as UWB, so a two-tier localization stack (UWB/GNSS for long-range homing, MI for final docking) is a direct consequence.
  • Because the reference is tied to the UGV's own field, the drone's estimate is immune to visual aliasing and global drift; it stays locked to the moving platform even when optical flow fails.
  • Static landings succeeded in all trials; dynamic tracking and docking succeeded in 80-100% of trials, suggesting the approach is reliable enough for battery-recharge or data-transfer rendezvous in the field.
  • The full pipeline runs at 20 Hz on the drone's microcontroller with only 9 g of added hardware, implying the SWaP budget is compatible with even smaller platforms or multi-drone teams.

Reading between the lines

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

  • The four distinct carrier frequencies are a resource, not just an implementation detail: a swarm with multiple UGVs could in principle reuse the same receiver if each ground platform transmits on a non-overlapping frequency set, extending the scheme to multi-base relative localization.
  • Because the system is a position-only solver that assumes known receiver attitude from the IMU, the same measurement principle could be extended to a joint position-orientation (6-DoF) estimator; that would remove the ±20° yaw restriction and is the natural next step, though at some computational cost.
  • The one-time static calibration assumes the drone can be placed at a known pose at startup; an online or self-initializing calibration would be needed for fully autonomous deployments where a reference pose is unavailable.
  • The reported accuracy is validated against motion capture in a controlled arena; a field test under magnetic interference (e.g., metal floors, high-current wiring) would tell how the dipole model's clean near-field assumption holds up in unstructured environments.
Share X Bluesky LinkedIn Reddit HN

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 a magneto-inductive (MI) relative localization system for a nano-UAV docking on a quadruped UGV. Four frequency-multiplexed transmit coils on the UGV generate AC magnetic fields; a single passive receive coil on a 47 g Crazyflie measures the superimposed fields, and a Nelder-Mead optimizer solves a magnetic-dipole inverse model for the tag's 3D position in the UGV frame. This estimate is fused into the Crazyflie's onboard EKF together with IMU, optical-flow, and ToF measurements. Experiments with Vicon ground truth cover static hover/landing (S1), linear tracking (S2), and composite planar motion (S3), reporting approximately 5 cm static RMSE, 8-11 cm dynamic RMSE, and 80-100% success rates. The central claim is that this enables GNSS- and motion-capture-free centimeter-level hovering, tracking, and landing on a moving platform under nano-UAV SWaP constraints.

Significance. If the results hold, the paper makes a useful contribution: it demonstrates a practical, infrastructure-less, close-range relative localization modality for nano-UAV/UGV teams, with a fully onboard 20 Hz pipeline and a lightweight passive tag. The use of frequency-multiplexed active coils, calibration at a known reference pose rather than fitting to the output, and validation against external motion capture are clear strengths. The most important caveat is the limited operational envelope: the magnetic model is position-only and assumes fixed anchor orientation, so the 'moving platform' capability is demonstrated only for translational UGV motion with limited heading changes. This should be reflected in the claims and, ideally, addressed by 6-DoF estimation or an explicit statement of the restriction.

major comments (3)
  1. [Section VI-D; Abstract; Conclusion] The yaw limitation is load-bearing. The abstract and conclusion claim that the system can 'autonomously hover, track, and land on a moving platform,' but Section VI-D explicitly restricts the operational envelope to 'planar translations with limited heading changes (±20°).' Moreover, the suggested workaround of 'walking forward during rotation' does not restore the model's validity: translation during a turn does not remove the unmodeled rotation of the anchor frame, so the position-only solver would still misinterpret the field variation. The paper should either implement/validate 6-DoF pose estimation, or clearly state in the title/abstract/conclusion that the demonstrated capability is for translational motion with at most small heading changes.
  2. [Abstract vs. Table III] The abstract reports a 7.2 cm RMSE for dynamic scenarios, but Table III gives mean RMSEs of 8.77 cm for S2 and 10.5 cm for S3. The value 7.2 cm appears in Table II as the mean of the 'in-out' Mag+Flow case, which is a static-UGV experiment. This is a direct inconsistency in the headline result. Please correct the abstract or explain the provenance of the 7.2 cm number in a way consistent with Tables II and III.
  3. [Section VI-A; Table II] The claimed 5 cm static accuracy is not an isolated MI estimate. Table II reports RMSE for the complete Mag+Flow EKF system, which also uses optical flow, IMU, and ToF. There is no MI-only position RMSE against Vicon ground truth in the paper. Since the contribution is the magnetic localization module, the paper should either report the MI-only position estimate error or clearly state that the 5 cm figure is the accuracy of the fused system. Without this, the scientific attribution of the accuracy to the magnetic subsystem is not established.
minor comments (5)
  1. [Table II] The table formatting is confusing: rows such as '11.14 4.56 6.93 FAIL' do not clearly separate the test number from the measurement values, and the meaning of '—' in the Flow columns is not stated. Please use explicit column headers and a legend.
  2. [Section III-C] The phrase 'absolute position measurement' is misleading because the MI position is expressed in the moving UGV frame. The text later says the system 'effectively ties the UAV frame to the quadruped frame,' but the mechanism by which a relative measurement is treated as absolute in the Crazyflie EKF, and how this affects optical-flow/IMU innovations and control setpoints, deserves a more careful explanation.
  3. [Figure 6] The third panel is described as 'power spectrum magnitude' of the coil voltage, but the axes and units are not labeled. Please specify the quantity and units so the link-quality discussion is interpretable.
  4. [Section IV] The statement that the UGV can walk forward during rotation to avoid the yaw limitation should be removed or substantiated; as noted in the major comments, translation plus yaw still contains unmodeled rotation. Also, fix the typo 'an heterogeneous' in the Introduction and 'filed sensor' in Section III-A.
  5. [Supplementary Material] The code link is listed as 'provided-after-review.' Please ensure the final version includes a working repository or clearly indicates that code will be released.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: model, calibration, and validation are independent; the UGV-yaw limitation is a scope caveat rather than a circular step.

full rationale

The derivation chain is self-contained and empirically grounded. The magnetic measurement model (Eqs. 1–3) is a standard physical dipole model, not an ansatz imported from a self-citation. The calibration coefficients C_i are computed once at a known reference pose as a per-anchor gain normalization (Algorithm 1), and the runtime position estimate minimizes the residual in Eq. (4) against live calibrated coil voltages; no fitted parameter is renamed as a prediction. Accuracy is evaluated against an independent Vicon motion-capture system used exclusively as ground truth, so the reported RMSE and landing success are external measurements rather than outputs forced by construction. The warm-started Nelder–Mead solver uses the previous EKF estimate only as a numerical initialization, while the cost function remains dependent on current magnetic measurements, so no self-confirming loop is introduced. The R_mag noise parameters taken from prior papers [22], [57] are filter-tuning constants, not the load-bearing evidence for the central claim, and those prior works are empirical rather than definitional. Section VI-D explicitly discloses the UGV-yaw limitation, but this is a correctness/scope caveat about the position-only model, not circularity. No load-bearing step reduces to its own inputs, so the appropriate circularity score is 0.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central claim rests on a standard magnetic dipole model plus a set of calibration and threshold parameters. The main nonstandard assumptions are the vertical-anchor mounting with position-only inversion, the reliability of the onboard attitude estimate for coil orientation, and the absence of significant environmental magnetic distortion after calibration.

free parameters (7)
  • Per-anchor calibration coefficients C_i (i=1..4) = not reported
    Algorithm 1 computes C_i = averaged raw voltage / dipole-model voltage at the reference position; these scale factors lump RX gain and transduction and directly affect all position estimates.
  • EKF magnetic measurement noise covariance R_mag = not reported
    Section III-C states R_mag is 'tuned based on the static characterization of the system' with standard deviations 'determined in [22], [57]'; the actual values are not given, and they control how strongly magnetic updates are trusted in the fused output.
  • ToF step-detection threshold delta_thresh = not reported
    Section III-C introduces this threshold to distinguish deck-to-floor jumps from vertical motion; it is a hand-tuned value that affects altitude hold during landing.
  • ADC saturation threshold V_sat_thresh = not reported
    Algorithm 2 excludes any anchor whose calibrated amplitude exceeds this threshold; the choice changes which measurements enter the cost function and can bias position estimates near the pad.
  • Outlier rejection threshold Delta_outlier = not reported
    Algorithm 2 discards position updates that jump more than this threshold from the previous estimate; this hand-tuned value directly filters the magnetic estimates sent to the EKF.
  • Nelder-Mead search box Omega = not reported
    Equation (4) restricts the optimization to a box 'corresponding to the valid flight volume'; the bounds are chosen by the authors and affect whether the solver can find the correct minimum.
  • Transmit coil frequencies = 210, 199, 189, 181 kHz
    The four FDM frequencies are chosen by hand to avoid FFT mixing and ADC saturation; they are fixed system design inputs, not tuned on the experimental output, but the separation quality affects measurement accuracy.
assumptions (7)
  • domain assumption Magnetic dipole model (Eqs. 1-2) is valid in the operating regime: compact sources, magnetoquasistatic, linear homogeneous isotropic media.
    Section III-A asserts the system operates in the compact-source, near-field regime; this is assumed rather than validated by a field-map comparison, and near-field corrections are mentioned as necessary in Section II-B for accuracy at close range.
  • domain assumption All four anchor dipoles are mounted with axes strictly perpendicular to the UGV horizontal plane, and their positions are fixed and known.
    Section III states u_i is aligned with z_B; this mounting choice simplifies the model but makes the system sensitive to UGV yaw, as acknowledged in Section VI-D.
  • domain assumption The receiver coil orientation in the anchor frame, n_B = R_B^T n_T, is accurately reconstructed from the drone's onboard attitude estimate.
    Section III-A relies on the attitude estimate to compute the field projection; any attitude error directly corrupts the measurement model and the estimated position.
  • domain assumption The position-only inverse problem with four vertical dipoles and scalar voltage magnitudes has a unique and stable solution over the search box Omega.
    Equation (4) is solved by Nelder-Mead with warm starts, but no observability, uniqueness, or convergence analysis is provided; experiments show convergence in the tested envelope, yet the yaw limitation shows the model has failure modes.
  • domain assumption Frequency-multiplexed anchor signals are cleanly separable by FFT with negligible intermodulation, and motor EMI is filtered by its distinct frequency content.
    Section II-B and IV-A rely on FFT peak extraction; no spectra or interference measurements are shown, only a qualitative link-quality proxy in Figure 6.
  • domain assumption Nearby ferromagnetic materials and conductive eddy-current effects are negligible after the initial in-situ calibration.
    Section II-B notes such effects can bias the field, but states they are mitigated by calibration and narrow-band filtering; no quantitative distortion characterization is provided.
  • domain assumption The ToF step filter's assumption that a large distance derivative means a change of ground surface, not actual drone vertical motion.
    Section III-C implements this state machine; if the drone itself moves vertically fast (e.g., during takeoff/landing transients), the filter may incorrectly hold altitude and destabilize control.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming." pith.science (2026). https://pith.science/paper/LB32JQZ2

@misc{pith2026260308926,
  author       = {Pith},
  title        = {Pith review of: Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LB32JQZ2}},
  note         = {Machine review of arXiv:2603.08926}
}
read the original abstract

Persistent air ground robot teams require nano UAVs that can leave a mobile UGV, perform short scouting or inspection tasks, and reliably return to a compact landing interface. This final docking phase remains difficult: it demands centimeter-scale relative localization on a moving platform, while nano UAVs provide only a few grams of payload and severe onboard sensing, power, and computation constraints. We address this problem with an infrastructure-less magneto inductive anchor-tag localization system for heterogeneous UAV-UGV teaming. Unlike passive magnetic docking aids or approaches exploiting environmental magnetic features, the proposed system actively generates a frequency multiplexed AC magnetic field on the UGV and uses it as an onboard localization reference for closed loop flight. The nano UAV carries only a lightweight passive receive coil and estimates its 3D position directly in the UGV frame, providing a local close range reference for hovering, tracking, and landing without external anchors, visual fiducials, GNSS, or motion capture online. The magnetic estimate is fused with the native onboard sensing stack, enabling operation under nano UAV SWaP constraints. Experiments with a Unitree A1 quadruped and a Crazyflie nano UAV, evaluated against motion capture ground truth, show centimeter level accuracy in static hovering and landing and approximately 8-11 cm RMSE during UGV motion, while the flow only baseline frequently violates the safety bound and fails the task.

Figures

Figures reproduced from arXiv: 2603.08926 by the authors.

Figure 1
Figure 1. Representative picture of a nano-size UAV autonomously landing on a moving legged robot and GNSS-denied environment. The infrastructure-free localization systems relies on onboard sensors, combining IMUs, optical-flow camera, UWB, and the MI system. The four reference coils are represented by the red lines with a magnetic dipole shape. The landing deck can be used to recharge the UAV’s batteries. on favorable illumi… view at source ↗
Figure 2
Figure 2. Custom Magnetic Hardware. (a) The Unitree AI legged robot UGV. (b) One of the four lightweight MI coils used for precise localization. (c) The Crazyflie 2.1 nano-UAV. (d) The landing pad with a diameter of 22 cm mounted on UGV. (e) The wiring for the AnchorDeck and the four coils, plus the connections for the wireless battery charger. (f) The ultra-lightweight MI coil mounted on the Crazyflie 2.1 nano-UAV and the Ma… view at source ↗
Figure 3
Figure 3. System Overview Block Diagram. The AnchorDeck (orange box) houses the signal generation and driving logic, powered by the UGV battery. It directly drives four independent anchor coils. The magnetic field is received by the UAV, processed alongside UWB and IMU data within the EKF for state estimation. the relative landing precision needs to provide a consistent accuracy <10 cm. Four transmitting coils are mounted on … view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Visual overview of the experimental validation scenarios using the proposed magnetic localization system. (a)-(c) S1 (Static Hovering & Landing): The nano-UAV performs an autonomous sequence of takeoff, hovering, and precision landing on the stationary UGV. (d)-(f) S2 …
Figure 5
Figure 5. Figure 5: Touchdown accuracy analysis for scenario S1-Hovering. The plot shows the planar 2D position of the UAV relative to the docking pad center at the moment of landing for the proposed Mag+Flow method (circles) [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Scenario S2: time-history qualitative results for a representa￾tive linear docking sequence. The colored bands highlight the mission phases (pre-takeoff, takeoff/approach, hovering, return over the UGV, landing). Top: x position in the world frame. Middle: altitude z. …
Figure 7
Figure 7. Figure 7: Scenario S3 (Test 1): qualitative time-aligned analysis of dynamic tracking. The numbered markers (1–7) identify key flight phases via synchronized video snapshots and the corresponding dashed vertical lines in the time traces. The trajectory plots (3D and top-down) co…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

63 extracted references · 2 linked inside Pith

  1. [1]

    Racer: Rapid collaborative exploration with a decentral- ized multi-uav system,

    B. Zhouet al., “Racer: Rapid collaborative exploration with a decentral- ized multi-uav system,”IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1816–1835, 2023

    2023

  2. [2]

    Probabilistic modeling and control for multi-uav search over uneven terrain,

    L. Lan ˇcaet al., “Probabilistic modeling and control for multi-uav search over uneven terrain,”IEEE Transactions on Robotics, vol. 42, pp. 418– 438, 2025

    2025

  3. [3]

    Omni-swarm: A decentralized omnidirectional visual– inertial–uwb state estimation system for aerial swarms,

    H. Xuet al., “Omni-swarm: A decentralized omnidirectional visual– inertial–uwb state estimation system for aerial swarms,”Ieee transac- tions on robotics, vol. 38, no. 6, pp. 3374–3394, 2022

    2022

  4. [4]

    State-of-the-art and future research challenges in uav swarms,

    S. Javedet al., “State-of-the-art and future research challenges in uav swarms,”IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19 023– 19 045, 2024

    2024

  5. [5]

    Energy-aware, collision-free information gathering for heterogeneous robot teams,

    X. Caiet al., “Energy-aware, collision-free information gathering for heterogeneous robot teams,”IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2585–2602, 2023

    2023

  6. [6]

    CERBERUS in the darpa subterranean challenge,

    M. Tranzattoet al., “CERBERUS in the darpa subterranean challenge,” Science Robotics, vol. 7, no. 66, 2022

    2022

  7. [7]

    Heterogeneous targets trapping with swarm robots by using adaptive density-based interaction,

    S. Zhanget al., “Heterogeneous targets trapping with swarm robots by using adaptive density-based interaction,”IEEE Transactions on Robotics, vol. 40, pp. 2729–2748, 2024. 14 Fig. 7:Scenario S3 (Test 1): qualitative time-aligned analysis of dynamic tracking. The numbered markers (1–7) identify key flight phases via synchronized video snapshots and the cor...

    2024

  8. [8]

    Ultra-lightweight collaborative slam for robot swarms,

    V . Niculescuet al., “Ultra-lightweight collaborative slam for robot swarms,”IEEE Access, 2025

    2025

Show all 63 references
  1. [9]

    A survey on swarm microrobotics,

    L. Yanget al., “A survey on swarm microrobotics,”IEEE Transactions on Robotics, vol. 38, no. 3, pp. 1531–1551, 2021

    2021

  2. [10]

    Unmanned-aerial-vehicle routing problem with mobile charging stations for assisting search and rescue missions in postdisaster scenarios,

    R. G. Ribeiroet al., “Unmanned-aerial-vehicle routing problem with mobile charging stations for assisting search and rescue missions in postdisaster scenarios,”IEEE transactions on systems, man, and cyber- netics: Systems, vol. 52, no. 11, pp. 6682–6696, 2021

    2021

  3. [11]

    Land & localize: An infrastructure-free and scalable nano-drones swarm with uwb-based localization,

    M. Pourjabaret al., “Land & localize: An infrastructure-free and scalable nano-drones swarm with uwb-based localization,” in2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE, 2023, pp. 654–660

    2023

  4. [12]

    Exploring beyond earth using space robotics,

    S. A. Chienet al., “Exploring beyond earth using space robotics,” Science Robotics, vol. 9, no. 91, p. eadi6424, 2024

    2024

  5. [13]

    Swarm of micro flying robots in the wild,

    X. Zhouet al., “Swarm of micro flying robots in the wild,”Science Robotics, vol. 7, no. 66, p. eabm5954, 2022

    2022

  6. [14]

    A relative infrastructure-less localization algorithm for decentralized and autonomous swarm formation,

    D. Schindleret al., “A relative infrastructure-less localization algorithm for decentralized and autonomous swarm formation,” in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 5288–5295

    2023

  7. [15]

    Performance comparison between decawave dw1000 and dw3000 in low-power double side ranging applications,

    T. Polonelliet al., “Performance comparison between decawave dw1000 and dw3000 in low-power double side ranging applications,” in2022 IEEE Sensors Applications Symposium (SAS). IEEE, 2022, pp. 1–6

    2022

  8. [16]

    Toward the future generation of railway localiza- tion exploiting rtk and gnss,

    D. Mikhaylovet al., “Toward the future generation of railway localiza- tion exploiting rtk and gnss,”IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–10, 2023

    2023

  9. [17]

    An integrated uwb-vision framework for autonomous ap- proach and landing of uavs in gps-denied environments,

    J. Liuet al., “An integrated uwb-vision framework for autonomous ap- proach and landing of uavs in gps-denied environments,”Mathematical Foundations of Computing, vol. 8, pp. 710–724, 1 2025

    2025

  10. [18]

    Low-complexity control for vision-based landing of quadrotor uav on unknown moving platform,

    J. Linet al., “Low-complexity control for vision-based landing of quadrotor uav on unknown moving platform,”IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5348–5358, 2021

    2021

  11. [19]

    Vision-based autonomous landing for unmanned aerial and ground vehicles cooperative systems,

    G. Niuet al., “Vision-based autonomous landing for unmanned aerial and ground vehicles cooperative systems,”IEEE robotics and automa- tion letters, vol. 7, no. 3, pp. 6234–6241, 2021

    2021

  12. [20]

    Autonomous landing on a moving platform using vision-based deep reinforcement learning,

    P. Ladoszet al., “Autonomous landing on a moving platform using vision-based deep reinforcement learning,”IEEE Robotics and Automa- tion Letters, vol. 9, no. 5, pp. 4575–4582, 2024

    2024

  13. [21]

    An advanced unmanned aerial vehicle (uav) approach via learning-based control for overhead power line monitoring: A comprehensive review,

    H. A. Foudehet al., “An advanced unmanned aerial vehicle (uav) approach via learning-based control for overhead power line monitoring: A comprehensive review,”IEEE Access, vol. 9, pp. 130 410–130 433, 2021

    2021

  14. [22]

    Fusion of uwb and magnetic ranging systems for robust positioning,

    V . Brunacciet al., “Fusion of uwb and magnetic ranging systems for robust positioning,”IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–12, 2023

    2023

  15. [23]

    Wireless-sensor communication based wireless- charging coil positioning system for uavs with maximum power point tracking,

    P. K. Chittooret al., “Wireless-sensor communication based wireless- charging coil positioning system for uavs with maximum power point tracking,”IEEE Sensors Journal, vol. 22, no. 8, pp. 8175–8182, 2022

    2022

  16. [24]

    Aage: Air-assisted ground robotic autonomous ex- ploration in large-scale unknown environments,

    L. Zhenget al., “Aage: Air-assisted ground robotic autonomous ex- ploration in large-scale unknown environments,”IEEE Transactions on Robotics, 2025

    2025

  17. [25]

    Learning robust perceptive locomotion for quadrupedal robots in the wild,

    T. Mikiet al., “Learning robust perceptive locomotion for quadrupedal robots in the wild,”Science robotics, vol. 7, no. 62, p. eabk2822, 2022

    2022

  18. [26]

    One filter to deploy them all: Robust safety for quadrupedal navigation in unknown environments,

    A. Linet al., “One filter to deploy them all: Robust safety for quadrupedal navigation in unknown environments,”IEEE Transactions on Robotics, vol. 42, pp. 545–560, 2025

    2025

  19. [27]

    Anymal parkour: Learning agile navigation for quadrupedal robots,

    D. Hoelleret al., “Anymal parkour: Learning agile navigation for quadrupedal robots,”Science Robotics, vol. 9, no. 88, p. eadi7566, 2024

    2024

  20. [28]

    Legged locomotion in challenging terrains using egocentric vision,

    A. Agarwalet al., “Legged locomotion in challenging terrains using egocentric vision,” inConference on robot learning. PMLR, 2023, pp. 403–415

    2023

  21. [29]

    Vision aided dynamic exploration of unstructured terrain with a small-scale quadruped robot,

    D. Kimet al., “Vision aided dynamic exploration of unstructured terrain with a small-scale quadruped robot,” in2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 2464–2470

    2020

  22. [30]

    Uwb and imu-based uav’s assis- tance system for landing operations in wind turbine inspection,

    A. Ochoa-de-Eribe-Landabereaet al., “Uwb and imu-based uav’s assis- tance system for landing operations in wind turbine inspection,”Sensors, vol. 22, no. 7, p. 2599, 2022

    2022

  23. [31]

    Learning-based legged locomotion: State of the art and future perspectives,

    S. Haet al., “Learning-based legged locomotion: State of the art and future perspectives,”The International Journal of Robotics Research, vol. 44, no. 8, pp. 1396–1427, 2025

    2025

  24. [32]

    A review of collaborative air-ground robots research,

    C. Liuet al., “A review of collaborative air-ground robots research,” Journal of Intelligent & Robotic Systems 2022 106:3, vol. 106, pp. 60–, 10 2022

    2022

  25. [33]

    Sensor planning for a symbiotic UA V and UGV sys- tem for precision agriculture,

    P. Tokekaret al., “Sensor planning for a symbiotic UA V and UGV sys- tem for precision agriculture,”IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1498–1511, Dec. 2016

    2016

  26. [34]

    An open platform for efficient drone-to-sensor wire- less ranging and data harvesting,

    T. Polonelliet al., “An open platform for efficient drone-to-sensor wire- less ranging and data harvesting,”Sustainable Computing: Informatics and Systems, vol. 35, p. 100734, 2022

    2022

  27. [35]

    Tightly-coupled perception and navigation of heteroge- neous land-air robots in complex scenarios,

    Y . Yueet al., “Tightly-coupled perception and navigation of heteroge- neous land-air robots in complex scenarios,” in2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 10 052–10 058

    2021

  28. [36]

    Heterogeneous multirobot team: Maritime in- spection and intervention in global navigation satellite system-denied scenarios,

    B. A. Ferreiraet al., “Heterogeneous multirobot team: Maritime in- spection and intervention in global navigation satellite system-denied scenarios,”IEEE robotics & automation magazine, 2025

    2025

  29. [37]

    Flexible layouts for fiducial tags,

    M. Krogiuset al., “Flexible layouts for fiducial tags,” in2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. 15

    2019

  30. [38]

    Practical challenges in landing a uav on a dynamic target,

    A. Salagameet al., “Practical challenges in landing a uav on a dynamic target,”arXiv preprint arXiv:2209.14465, 2022

    2022 arXiv

  31. [39]

    A quadrotor aerial docking system utilizing both vision and magnetic field,

    X. Yuet al., “A quadrotor aerial docking system utilizing both vision and magnetic field,”IEEE Robotics and Automation Letters, 2025

    2025

  32. [40]

    Persistent charging system for crazyflie platform,

    N. P. Nguyenet al., “Persistent charging system for crazyflie platform,” Drones, vol. 6, no. 8, p. 212, 2022

    2022

  33. [41]

    Uav-ugv collaborative localisation with minimum sensing,

    A. E. De Silvaet al., “Uav-ugv collaborative localisation with minimum sensing,”Sensors, vol. 24, no. 14, p. 4629, 2024

    2024

  34. [42]

    Air-ground collaborative robots for fire and rescue missions: Towards mapping and navigation perspective,

    Y . Zhanget al., “Air-ground collaborative robots for fire and rescue missions: Towards mapping and navigation perspective,”arXiv preprint arXiv:2412.20699, 2024

    2024 arXiv

  35. [43]

    Viral-fusion: A visual-inertial-ranging-lidar sensor fusion approach,

    T.-M. Nguyenet al., “Viral-fusion: A visual-inertial-ranging-lidar sensor fusion approach,”IEEE Transactions on Robotics, vol. 38, no. 2, pp. 958–977, 2021

    2021

  36. [44]

    Autonomous landing of a uav on a moving platform using model predictive control,

    Y . Fenget al., “Autonomous landing of a uav on a moving platform using model predictive control,”Drones, vol. 2, no. 4, p. 34, 2018

    2018

  37. [45]

    Autonomous landing on a moving vehicle with an unmanned aerial vehicle,

    T. Bá ˇcaet al., “Autonomous landing on a moving vehicle with an unmanned aerial vehicle,”Journal of Field Robotics, vol. 36, no. 5, pp. 874–891, 2019

    2019

  38. [46]

    Self-spin enabled docking and detaching of a uav-ugv system for aerial-terrestrial amphibious and independent locomotion,

    L. Denget al., “Self-spin enabled docking and detaching of a uav-ugv system for aerial-terrestrial amphibious and independent locomotion,” IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2454–2461, 2023

    2023

  39. [47]

    Comparison of controller performance for ugv- landing platform self-leveling,

    M. N. Alghanimet al., “Comparison of controller performance for ugv- landing platform self-leveling,” in2020 28th Mediterranean conference on control and automation (MED). IEEE, 2020, pp. 471–478

    2020

  40. [48]

    An induction-based localisation technique for wirelessly charged drones,

    L. Lanet al., “An induction-based localisation technique for wirelessly charged drones,” in2020 IEEE PELS Workshop on Emerging Technolo- gies: Wireless Power Transfer (WoW). IEEE, 2020, pp. 275–277

    2020

  41. [49]

    Landing of a quadrotor on a moving target using dynamic image-based visual servo control,

    P. Serraet al., “Landing of a quadrotor on a moving target using dynamic image-based visual servo control,”IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1524–1535, 2016

    2016

  42. [50]

    High speed safe autonomous landing marker tracking of fixed wing drone based on deep learning,

    B. Yuanet al., “High speed safe autonomous landing marker tracking of fixed wing drone based on deep learning,”IEEE Access, vol. 10, pp. 80 415–80 436, 2022

    2022

  43. [51]

    A novel framework of visual detection, tracking and landing for uavs utilizing ar markers,

    C. V . Nguyenet al., “A novel framework of visual detection, tracking and landing for uavs utilizing ar markers,” in2023 International Conference on Control, Robotics and Informatics (ICCRI). IEEE, 2023, pp. 81–85

    2023

  44. [52]

    Implementation of ir lock on poledrone (polman drone education) for precision landing with ros,

    H. Khoirunnisaet al., “Implementation of ir lock on poledrone (polman drone education) for precision landing with ros,” in2023 IEEE 15th International Conference on Computational Intelligence and Communi- cation Networks (CICN). IEEE, 2023, pp. 584–590

    2023

  45. [53]

    Parallel robotic automated docking method for realizing space segment assembly,

    D. Sunet al., “Parallel robotic automated docking method for realizing space segment assembly,”Scientific Reports, vol. 15, no. 1, p. 5754, 2025

    2025

  46. [54]

    Distortion rejecting magneto-inductive 3-d local- ization,

    T. E. Abrudanet al., “Distortion rejecting magneto-inductive 3-d local- ization,”IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2380–2390, 2015

    2015

  47. [55]

    Underground localization in 3-d using magneto- inductive tracking,

    A. Markhamet al., “Underground localization in 3-d using magneto- inductive tracking,”IEEE Sensors Journal, vol. 12, no. 6, pp. 1809– 1818, 2012

    2012

  48. [56]

    A magnetic induction based underground localization system for miner safety,

    A. Chavdaet al., “A magnetic induction based underground localization system for miner safety,” in2023 IEEE Sensors, 2023, pp. 1–4

    2023

  49. [57]

    Comparison of measurement models for 3d mag- netic localization and tracking,

    G. D. Angeliset al., “Comparison of measurement models for 3d mag- netic localization and tracking,”IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 10, pp. 2543–2555, 2017

    2017

  50. [58]

    Wireless power and data transfer via a common inductive link using frequency division multiplexing,

    J. Wuet al., “Wireless power and data transfer via a common inductive link using frequency division multiplexing,”IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7810–7820, 2015

    2015

  51. [59]

    Analysis of simultaneous 3d positioning and attitude estimation of a planar coil using inductive coupling,

    A. Moschittaet al., “Analysis of simultaneous 3d positioning and attitude estimation of a planar coil using inductive coupling,” in2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017, pp. 1–6

    2017

  52. [60]

    Magnetic calibration system with interference compensation,

    M. Janoseket al., “Magnetic calibration system with interference compensation,”IEEE Transactions on Magnetics, vol. 55, no. 1, pp. 1–4, 2019

    2019

  53. [61]

    Landing uav on moving surface vehicle: Visual tracking and motion prediction of landing deck,

    T.-T. Nguyenet al., “Landing uav on moving surface vehicle: Visual tracking and motion prediction of landing deck,” in2024 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2024, pp. 827–833

    2024

  54. [62]

    An approximate electromagnetic model for optimizing wireless charging of biomedical implants,

    K. van Oosterhoutet al., “An approximate electromagnetic model for optimizing wireless charging of biomedical implants,”IEEE Transac- tions on Biomedical Engineering, vol. 69, no. 6, pp. 1954–1963, 2022

    1954

  55. [63]

    Comparison of measurement models for 3d magnetic localization and tracking,

    G. De Angeliset al., “Comparison of measurement models for 3d magnetic localization and tracking,”Sensors, vol. 17, no. 11, 2017

    2017

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.