Pith. sign in

REVIEW 3 major objections 6 minor 4 cited by

Air-Ground Collaborative Robots for Fire and Rescue Missions: Towards Mapping and Navigation Perspective

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Mapping and navigation decide air-ground rescue robot success

desk verdict A useful entry-level survey of UAV-UGV mapping and navigation for fire and rescue, but the 'systematic' label is unsupported by any documented method and the taxonomy needs defense. read the letter →

arxiv 2412.20699 v2 pith:KZ7Q4ES7 submitted 2024-12-30 cs.RO

Ying Zhang , Haibao Yan , Danni Zhu , Jiankun Wang , Cui-Hua Zhang , Weili Ding , Xi Luo , Changchun Hua
show 1 more author
Max Q.-H. Meng
This is my paper · ORCID
classification cs.RO
keywords air-groundcollaborativerobotsfireandrescueUAVmappingUGVnavigationpathplanningco-localizationtopologicalmapsemantic
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 claims that the efficiency of air-ground collaborative robots in fire and rescue missions rests on a two-stage pipeline: an unmanned aerial vehicle (UAV) maps the scene from above, and an unmanned ground vehicle (UGV) uses that map to localize, plan, and navigate. It argues that no prior survey has examined this domain through the mapping-and-navigation lens, and it delivers a systematic review organized by two axes: the type of map the UAV builds (2-D grid, 3-D, topological, semantic) and the number of UAVs and UGVs in the team (single-single, single-multi, multi-single, multi-multi). The survey catalogs representative works, their merits and demerits, and matches navigation algorithms to map types. If the paper is right, practitioners gain a structured reference for choosing mapping and navigation methods for specific fire-and-rescue deployments, and researchers gain a map of open problems.

What carries the argument

The central organizing device is the UAV-mapping-to-UGV-navigation pipeline: the UAV builds a map (2-D grid, 3-D point cloud or octree, topological graph, or semantic map), the UGV co-localizes within that map, and then plans and executes navigation. The second load-bearing device is the team-cardinality classification (1 UAV–1 UGV, 1 UAV–many UGVs, many UAVs–1 UGV, many UAVs–many UGVs), which the paper uses to discuss scalability, communication burden, and mission practicality. Together these two axes carry the survey's argument that the field is coherent and that its open challenges are identifiable.

What would settle it

Run a reproducible database search for peer-reviewed UAV-UGV fire-and-rescue mapping and navigation papers from 2015 to 2025 with explicit inclusion criteria; if a substantial share of the recovered papers (say, more than 20%) fits none of the four map types or four team-size categories, the survey's systematic-coverage claim fails.

Watch

Extended reading notes

Core claim

The central claim is that the design space of air-ground collaborative robots for fire and rescue can be organized by a clear division of labor: UAVs, with their mobility and aerial sensors, are responsible for rapid large-scale perception and map construction; UGVs, with their ground-level access, are responsible for co-localization, path planning, and navigation toward mission points. The paper asserts that this mapping-then-navigation framework is the key foundation for efficient autonomous collaboration, and it substantiates the claim by classifying maps into four types, localization into five sensor strategies, navigation algorithms by map type, and teams by cardinality of UAVs and UGVs. The survey's contribution is taxonomic and organizational: it provides a coherent reference that situates existing results, highlights their limitations, and identifies research gaps such as task-relevant mapping, multi-modal data fusion in smoke-filled environments, and embodied-AI-driven navigation.

Load-bearing premise

The survey's value depends on its selection of representative works being comprehensive and its two classification axes (map type and team size) being the most meaningful way to organize the field; if major methods or system configurations fall outside these categories, the organizational claim weakens.

Editorial extensions

If this is right

  • A practitioner choosing a fire-and-rescue system can use the four map types as a decision menu: 2-D grid maps for simple, compute-limited settings; 3-D maps for rugged terrain; topological maps for structured road networks; semantic maps for scene understanding and high-level task planning.
  • Matching navigation algorithms to map types becomes systematic: grid-based planners like A* and D* for 2-D maps, sampling-based planners like RRT on point clouds for 3-D maps, graph search on topological maps, and semantic-informed planners for semantic maps.
  • The team-size taxonomy predicts capability trade-offs: single-single systems are simple but limited in scale; single-UAV multi-UGV systems depend on a single aerial vantage and risk a bottleneck; multi-UAV single-UGV systems provide coverage but limit ground mission throughput; many-many systems maximize capability but face coordination, communication, and task-allocation challenges.
  • For researchers, the survey's identified gaps—task-oriented mapping, multi-modal fusion under smoke, and embodied-AI navigation—are concrete openings for new work.

Reading between the lines

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

  • One implicit consequence is that the map type and team cardinality axes are interdependent: for example, semantic maps look more attractive in many-many systems where the cost of building them can be amortized across multiple ground vehicles, a link the paper does not explicitly make.
  • A testable extension is to turn the survey's taxonomy into a benchmark: define representative fire-and-rescue scenarios (indoor high-rise, forest wildfire, chemical plant) and score how well each map-type and team-size combination performs, which would validate or refine the paper's organizational claims.
  • The framework suggests that the communication channel between UAV and UGV—bandwidth, latency, reliability—is a hidden variable that may dominate the choice of map type; the paper mentions communication challenges but does not elevate channel constraints to a first-class design axis alongside map type and team size.
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 / 6 minor

Summary. The paper is a survey of air-ground collaborative robots for fire and rescue missions, organized around the roles of UAVs in mapping and UGVs in navigation. It introduces a framework in which UAVs build maps (2D grid, 3D, topological, semantic) that UGVs use for co-localization and path planning, and it classifies collaborative systems into four categories based on the number of UAVs and UGVs: single-single, single-multi, multi-single, and multi-multi. It then presents application examples in firefighting and rescue scenarios and closes with research challenges and future directions. The paper does not report new experimental results; its contribution is a structured synthesis and classification of existing work.

Significance. If the survey's coverage and taxonomy are sound, the paper would provide a useful entry point for practitioners and researchers in a fragmented area, and its tables summarising mapping types, localisation methods, and team configurations are a helpful reference resource. The authors also credit a specific application-oriented perspective (UAV mapping for UGV navigation) that distinguishes the survey from earlier reviews. However, the value of the survey depends on the completeness of the literature selection and on whether the proposed classification is apt; both are assumed rather than demonstrated. The paper is therefore a potentially valuable reference, but its central claim to being a systematic review is not yet supported by the manuscript as written.

major comments (3)
  1. [Section I (Introduction, overall structure)] The paper claims to present a 'systematic review' of air-ground collaborative robots for fire and rescue from a mapping and navigation perspective, but it provides no methodology section: no search databases, query strings, date range, inclusion/exclusion criteria, or screening process are reported. Without such a protocol, the reader cannot verify that the surveyed literature is comprehensive or representative, which is load-bearing because the paper introduces no new experimental results and its usefulness rests entirely on its coverage and organization.
  2. [Section V and Table IV] The classification of collaborative systems into four categories based on the number of UAVs and UGVs is presented as natural without justification. The paper does not explain why robot count is the most meaningful organizing dimension for fire-and-rescue mapping and navigation, or why other dimensions such as communication architecture, autonomy level, or map-sharing strategy are less important. Several entries placed in the taxonomy do not directly support the mapping/navigation focus: Tanner [137] concerns target detection and Nazarova et al. [136] addresses earthquake rescue using search theory. Such placements weaken the paper's practical conclusions about mapping and navigation and suggest the taxonomy is not consistently applied.
  3. [Tables I, II, and III] The tables of 'representative works' do not state how the entries were selected or how many papers were screened to arrive at them. As a result, the reader cannot determine whether the tables are balanced or biased, and the claim that the paper 'rounds up references for practitioners' is unsubstantiated. The paper also gives generic pros and cons for map types and navigation methods without tying these assessments to the experimental conditions or performance metrics of the cited works; for example, the merits and demerits of lidar localization (Section IV-A.4) are stated in general terms rather than derived from the reviewed studies. A critical synthesis that explains which methods transfer to fire/rescue scenarios would strengthen the survey.
minor comments (6)
  1. [Abstract] The phrase 'ground-to-ground cooperative robots' in the abstract contradicts the title and the rest of the paper, which is about air-ground collaboration; this should be corrected to 'air-ground'.
  2. [Section I] The sentence 'which is the basisn' appears at the end of the section and is missing a period and a word; it should read 'which is the basis' or similar.
  3. [Section III-A] The phrase 'the UAG' should be 'the UAV'.
  4. [Section IV-B and Table III] In the row for Zuo et al. [105], 'V oronoi' should be 'Voronoi'.
  5. [Throughout] Spacing between 'UA' and 'V' (e.g., 'UA V', 'UGV ') is likely a LaTeX rendering artifact, but the camera-ready version should ensure the abbreviations appear as 'UAV' and 'UGV' consistently.
  6. [References] Reference [63] contains '2Proc' at the start of the venue name, which appears to be a typographical error.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey organizes prior literature descriptively and makes no derivation whose output is equivalent to its input.

full rationale

This is a survey paper with no mathematical derivation, fitted parameters, or predictive claims. Its contributions are literature organization: a framework in Section II, map-type summaries in Section III, navigation methods in Section IV, a four-category team-size classification in Section V, and application examples in Section VI. None of these steps is defined in terms of a target result, and no result is 'predicted' from fitted inputs. The authors' self-citations ([7] GACF, [46], [60], [63], [102], [103], [113], [141], [149]) appear as representative works or supporting references for general statements; they are descriptive examples within a survey, not load-bearing justification for a derived claim. There is no invoked uniqueness theorem, no ansatz smuggled via citation, and no renaming of an empirical pattern as a derivation. The absence of a documented search protocol and the aptness of the Section V taxonomy are legitimate quality concerns about coverage and organization, but they do not constitute circularity under the specified definitions. Therefore score 0.

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

The paper makes no mathematical derivations and introduces no free parameters or invented entities. It relies on domain assumptions about the centrality of mapping and navigation, the usefulness of its classification, and the representativeness of its cited literature.

assumptions (3)
  • domain assumption UAV mapping and UGV navigation constitute the central operational loop for air-ground collaborative robots in fire and rescue.
    The paper builds its entire framework on this loop (Section II).
  • domain assumption The four-way classification by number of UAVs and UGVs (1-1, 1-many, many-1, many-many) is exhaustive and useful.
    Paper asserts this classification in Section V without evidence for exhaustiveness.
  • domain assumption The cited references are representative of the state of the art and are summarized accurately.
    The review's value depends on the accuracy of its summaries, but no systematic selection protocol is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Air-Ground Collaborative Robots for Fire and Rescue Missions: Towards Mapping and Navigation Perspective." pith.science (2026). https://pith.science/paper/KZ7Q4ES7

@misc{pith2026241220699,
  author       = {Pith},
  title        = {Pith review of: Air-Ground Collaborative Robots for Fire and Rescue Missions: Towards Mapping and Navigation Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KZ7Q4ES7}},
  note         = {Machine review of arXiv:2412.20699}
}
read the original abstract

Air-ground collaborative robots have shown great potential in the field of fire and rescue, which can quickly respond to rescue needs and improve the efficiency of task execution. Mapping and navigation, as the key foundation for air-ground collaborative robots to achieve efficient task execution, have attracted a great deal of attention. This growing interest in collaborative robot mapping and navigation is conducive to improving the intelligence of fire and rescue task execution, but there has been no comprehensive investigation of this field to highlight their strengths. In this paper, we present a systematic review of the ground-to-ground cooperative robots for fire and rescue from a new perspective of mapping and navigation. First, an air-ground collaborative robots framework for fire and rescue missions based on unmanned aerial vehicle (UAV) mapping and unmanned ground vehicle (UGV) navigation is introduced. Then, the research progress of mapping and navigation under this framework is systematically summarized, including UAV mapping, UAV/UGV co-localization, and UGV navigation, with their main achievements and limitations. Based on the needs of fire and rescue missions, the collaborative robots with different numbers of UAVs and UGVs are classified, and their practicality in fire and rescue tasks is elaborated, with a focus on the discussion of their merits and demerits. In addition, the application examples of air-ground collaborative robots in various firefighting and rescue scenarios are given. Finally, this paper emphasizes the current challenges and potential research opportunities, rounding up references for practitioners and researchers willing to engage in this vibrant area of air-ground collaborative robots.

Figures

Figures reproduced from arXiv: 2412.20699 by the authors.

Figure 1
Figure 1. Illustration of air-ground collaborative robots framework for fire and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Schematic of the relationship between air-ground collaborative robots [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Scenario of the parcel delivery task with UGV based on OctoMap [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (12 more)
Figure 5
Figure 5. Figure 5: A topological map of the 3-D environment that is constructed [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: Top view of the trajectory drawn on Google Earth, as well as the [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: UAV firefighting scenario and constructed 3D map with lidar data for [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: An example of 2-D map-based path planning and navigation [110]. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: 3-D map including elevation information, and the feasible path [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Sparse 3-D topological maps constructed by UAVs that can be used [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 13
Figure 13. Figure 13: A pipeline for UAV-UGV cooperative solution based on semantic [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]
Figure 14
Figure 14. Figure 14: A scenario for a single UAV with multi-UGVs performing fire and [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]
Figure 16
Figure 16. Figure 16: Illustration of a multi-UAVs with multi-UGVs collaborative rescue [PITH_FULL_IMAGE:figures/full_fig_p012_16.png]
Figure 18
Figure 18. Figure 18: Collaborative framework for forest fires and rescue tasks. [PITH_FULL_IMAGE:figures/full_fig_p013_18.png]
Figure 19
Figure 19. Figure 19: Path planning and navigation results of UAV and UGV cooperation [PITH_FULL_IMAGE:figures/full_fig_p013_19.png]
Figure 20
Figure 20. Figure 20: Air-ground collaborative robots for firefighting and rescue scenarios. [PITH_FULL_IMAGE:figures/full_fig_p013_20.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.RO 2026-03 conditional novelty 6.0 of 10

    An active magnetic beacon system on a moving quadruped lets a nano-drone estimate its 3D position onboard and land with about 5-11 cm error, without GNSS, cameras, or external infrastructure.

  2. SkyRover: A Modular Simulator for Cross-Domain Pathfinding

    cs.RO 2025-02 conditional novelty 5.0 of 10

    SkyRover is a Gazebo/ROS2 simulator that unifies UAV and AGV pathfinding in 3D grids, with wrappers for search- and learning-based algorithms and low-level controllers, demonstrated by preliminary warehouse benchmarks.

  3. Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement

    stat.AP 2026-03 unverdicted novelty 4.0 of 10

    Citation visibility in generative search is a stochastic sample estimator; single-run point estimates are misleadingly precise and need bootstrap uncertainty and adequate sample sizes.

  4. Environment Modeling for Service Robots From a Task Execution Perspective

    cs.RO 2025-01 conditional novelty 3.0 of 10

    A literature review that classifies environment modeling for home service robots into four task-driven categories, localization, navigation, manipulation, and long-term autonomy, drawing on prior published work.

Reference graph

Works this paper leans on

151 extracted references · 75 canonical work pages · cited by 4 Pith papers

  1. [137]

    Switched uav-ugv cooperation scheme for target detec- tion,

    H. G. Tanner, “Switched uav-ugv cooperation scheme for target detec- tion,” in Proc. IEEE Int. Conf. Robot. Automat. , 2007, pp. 3457–3462

    work page 2007

  2. [136]

    The application of multi-agent robotic systems for earthquake rescue,

    A. V . Nazarova and M. Zhai, “The application of multi-agent robotic systems for earthquake rescue,” Robot.: Ind. 4.0 Issues New Intell. Control Paradigms, pp. 133–146, 2020

    work page 2020

  3. [1]

    The role of robots in firefighting,

    R. Bogue, “The role of robots in firefighting,” Ind. Robot: Int. J. Robot. Res. Appl., vol. 48, no. 2, pp. 174–178, 2021

    2021

  4. [2]

    Robotic firefighting: A review and future perspective,

    M. Wang, X. Chen, and X. Huang, “Robotic firefighting: A review and future perspective,” Int. Building Fire Safety Smart Firefighting , pp. 475–499, 2024

    2024

  5. [3]

    Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol,

    S. Zhang, J. Yao, R. Wang, Z. Liu, C. Ma, Y . Wang, and Y . Zhao, “Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol,” Robot. Auton. Syst. , vol. 154, p. 104122, 2022

    2022

  6. [4]

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

    R. G. Ribeiro, L. P. Cota, T. A. Euzébio, J. A. Ramírez, and F. G. Guimarães, “Unmanned-aerial-vehicle routing problem with mobile charging stations for assisting search and rescue missions in postdisas- ter scenarios,” IEEE Trans. Syst., Man, Cybern., Syst. , vol. 52, no. 11, pp. 6682–6696, 2022

    2022

  7. [5]

    Multi-robot support system for fighting wildfires in challenging environments: System design and field test report,

    L. Frering, A. Koefler, M. Huber, S. Pfister, R. Feischl, A. Almer, and G. Steinbauer-Wagner, “Multi-robot support system for fighting wildfires in challenging environments: System design and field test report,” in Proc. IEEE Int. Symp. Safety Secur. Rescue Robot. , 2023, pp. 32–38

    2023

  8. [6]

    Enhanced emergency communication services for post-disaster rescue: Multi-irs assisted air-ground integrated data collection,

    Y . Zhou, Z. Jin, H. Shi, L. Shi, N. Lu, and M. Dong, “Enhanced emergency communication services for post-disaster rescue: Multi-irs assisted air-ground integrated data collection,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 5, pp. 4651–4664„ 2024

    2024

Show all 151 references
  1. [7]

    Gacf: Ground-aerial collaborative framework for large-scale emergency res- cue scenarios,

    Y . Zhang, J. Yu, Y . Tang, Y . Deng, X. Tian, Y . Yue, and Y . Yang, “Gacf: Ground-aerial collaborative framework for large-scale emergency res- cue scenarios,” in Proc. IEEE Int. Unmanned Syst. , 2023, pp. 1701– 1707

    2023

  2. [8]

    A comprehensive review of uav-ugv collaboration: Advancements and challenges,

    I. Munasinghe, A. Perera, and R. C. Deo, “A comprehensive review of uav-ugv collaboration: Advancements and challenges,” J. Sens. Actuator Netw., vol. 13, no. 6, p. 81, 2024

    2024

  3. [9]

    Weighted decentralized information filter for collaborative air-ground target ge- olocation in large outdoor environments,

    L. Zhang, F. Gao, B. Chen, L. Xi, F. Deng, and J. Chen, “Weighted decentralized information filter for collaborative air-ground target ge- olocation in large outdoor environments,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 53, no. 11, pp. 7292–7302, 2023

    2023

  4. [10]

    Collab- orative route planning of uavs, workers, and cars for crowdsensing in disaster response,

    L. Han, C. Tu, Z. Yu, Z. Yu, W. Shan, L. Wang, and B. Guo, “Collab- orative route planning of uavs, workers, and cars for crowdsensing in disaster response,” IEEE/ACM Trans. Netw., vol. 32, no. 4, pp. 3606– 3621, 2024

    2024

  5. [11]

    Air- ground collaboration with spomp: Semantic panoramic online mapping and planning,

    I. D. Miller, F. Cladera, T. Smith, C. J. Taylor, and V . Kumar, “Air- ground collaboration with spomp: Semantic panoramic online mapping and planning,” IEEE Trans. Field Robot. , vol. 1, pp. 93–112, 2024

    2024

  6. [12]

    A review of collaborative air-ground robots research,

    C. Liu, J. Zhao, and N. Sun, “A review of collaborative air-ground robots research,” J. Intell. Robot. Syst. , vol. 106, no. 3, p. 60, 2022

    2022

  7. [13]

    Sbc-slam: Semantic bio-inspired collaborative slam for large-scale environment perception of heterogeneous systems,

    D. Liu, J. Wu, Y . Du, R. Zhang, and M. Cong, “Sbc-slam: Semantic bio-inspired collaborative slam for large-scale environment perception of heterogeneous systems,” IEEE Trans. Instrum. Meas. , vol. 73, pp. 1–10, 2024

    2024

  8. [14]

    A review of cloud-edge slam: Toward asynchronous collaboration and implicit representation transmission,

    W. Chen, S. Chen, J. Leng, J. Wang, Y . Guan, M. Q.-H. Meng, and H. Zhang, “A review of cloud-edge slam: Toward asynchronous collaboration and implicit representation transmission,” IEEE Trans. Intell. Transp. Syst. , vol. 25, no. 11, pp. 15 437–15 453, 2024

    2024

  9. [15]

    Mobile robot for power substation inspection: A survey,

    S. Lu, Y . Zhang, and J. Su, “Mobile robot for power substation inspection: A survey,” IEEE/CAA J. Automat. Sinica , vol. 4, no. 4, pp. 830–847, 2017

    2017

  10. [16]

    Uav-based simultaneous localization and mapping in outdoor environments: A systematic scoping review,

    K. Wang, L. Kooistra, R. Pan, W. Wang, and J. Valente, “Uav-based simultaneous localization and mapping in outdoor environments: A systematic scoping review,” J. Field Robot. , vol. 41, p. 1617–1642, 2024

    2024

  11. [17]

    Aerial-ground collaborative continuous risk mapping for autonomous driving of unmanned ground vehicle in off-road environments,

    R. Wang, K. Wang, W. Song, and M. Fu, “Aerial-ground collaborative continuous risk mapping for autonomous driving of unmanned ground vehicle in off-road environments,” IEEE Trans. Aerosp. Electron. Syst., vol. 59, no. 6, pp. 9026–9041, 2023

    2023

  12. [18]

    Coordination between unmanned aerial and ground vehicles: A taxonomy and optimization perspective,

    J. Chen, X. Zhang, B. Xin, and H. Fang, “Coordination between unmanned aerial and ground vehicles: A taxonomy and optimization perspective,” IEEE Trans. Cybern., vol. 46, no. 4, pp. 959–972, 2015

    2015

  13. [19]

    Co- operative motion planning and control for aerial-ground autonomous systems: Methods and applications,

    R. Chai, Y . Guo, Z. Zuo, K. Chen, H.-S. Shin, and A. Tsourdos, “Co- operative motion planning and control for aerial-ground autonomous systems: Methods and applications,” Prog. Aerosp. Sci. , vol. 146, p. 101005, 2024

    2024

  14. [20]

    A review of recent advances in co- ordination between unmanned aerial and ground vehicles,

    Y . Ding, B. Xin, and J. Chen, “A review of recent advances in co- ordination between unmanned aerial and ground vehicles,” Unmanned Syst., vol. 9, no. 02, pp. 97–117, 2021

    2021

  15. [21]

    Unmanned air/ground vehicles heterogeneous cooperative techniques: Current status and prospects,

    H. Duan and S. Liu, “Unmanned air/ground vehicles heterogeneous cooperative techniques: Current status and prospects,” Sci. China Technol. Sci., vol. 53, pp. 1349–1355, 2010

    2010

  16. [22]

    Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review,

    J. Tang, H. Duan, and S. Lao, “Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4295–4327, 2023

    2023

  17. [23]

    Space-air-ground integrated network: A survey,

    J. Liu, Y . Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: A survey,” IEEE Commun. Surveys Tuts. , vol. 20, no. 4, pp. 2714–2741, 2018. 15

    2018

  18. [24]

    Service coordination in the space-air-ground integrated network,

    Y . Guo, Q. Li, Y . Li, N. Zhang, and S. Wang, “Service coordination in the space-air-ground integrated network,” IEEE Netw., vol. 35, no. 5, pp. 168–173, 2021

    2021

  19. [25]

    Space-air-ground integrated network development and applications in high-speed railways: A survey,

    J. Sheng, X. Cai, Q. Li, C. Wu, B. Ai, Y . Wang, M. Kadoch, and P. Yu, “Space-air-ground integrated network development and applications in high-speed railways: A survey,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 8, pp. 10 066–10 085, 2022

    2022

  20. [26]

    Air-ground collaborative mobile edge computing: Architecture, challenges, and opportunities,

    Q. Zhen, H. Shoushuai, W. Hai, Q. Yuben, D. Haipeng, X. Fei, W. Zhenhua, and L. Hailong, “Air-ground collaborative mobile edge computing: Architecture, challenges, and opportunities,” China Com- mun., vol. 21, no. 5, pp. 1–16, 2024

    2024

  21. [27]

    Air-ground integrated sensing and communications: Opportunities and challenges,

    Z. Fei, X. Wang, N. Wu, J. Huang, and J. A. Zhang, “Air-ground integrated sensing and communications: Opportunities and challenges,” IEEE Commun. Mag. , vol. 61, no. 5, pp. 55–61, 2023

    2023

  22. [28]

    Unmanned aerial vehicle (uav)-assisted path planning for unmanned ground vehicles (ugvs) via disciplined convex-concave programming,

    G. Niu, L. Wu, Y . Gao, and M.-O. Pun, “Unmanned aerial vehicle (uav)-assisted path planning for unmanned ground vehicles (ugvs) via disciplined convex-concave programming,” IEEE Trans. Veh. Technol., vol. 71, no. 7, pp. 6996–7007, 2022

    2022

  23. [29]

    Aerial: A meta review and discussion of challenges toward unmanned aerial vehicle operations in logistics, mobility, and monitoring,

    S. Wandelt, S. Wang, C. Zheng, and X. Sun, “Aerial: A meta review and discussion of challenges toward unmanned aerial vehicle operations in logistics, mobility, and monitoring,” IEEE Trans. Intell. Transp. Syst. , vol. 25, no. 7, pp. 6276–6289, 2024

    2024

  24. [30]

    Key technologies and applications of uavs in underground space: A review,

    B. He, X. Ji, G. Li, and B. Cheng, “Key technologies and applications of uavs in underground space: A review,” IEEE Trans. Cogn. Commun. Netw., vol. 10, no. 3, pp. 1026–1049, 2024

    2024

  25. [31]

    Au- tonomous navigation for evtol: Review and future perspectives,

    H. Wei, B. Lou, Z. Zhang, B. Liang, F.-Y . Wang, and C. Lv, “Au- tonomous navigation for evtol: Review and future perspectives,” IEEE Trans. Intell. Vehicles, vol. 9, no. 2, pp. 4145–4171, 2024

    2024

  26. [32]

    State of the art and future trends in obstacle-surmounting unmanned ground vehicle configuration and dynamics,

    M. He, X. Yue, Y . Zheng, J. Chen, S. Wu, Z. Heng, X. Zhou, and Y . Cai, “State of the art and future trends in obstacle-surmounting unmanned ground vehicle configuration and dynamics,” Robotica, vol. 41, no. 9, pp. 2625–2647, 2023

    2023

  27. [33]

    Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles,

    X. Zhou, X. Yu, Y . Zhang, Y . Luo, and X. Peng, “Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles,” IEEE Trans. Autom. Sci. Eng. , vol. 18, no. 4, pp. 1575–1589, 2021

    2021

  28. [34]

    Aerial and ground robot collaboration for autonomous mapping in search and rescue missions,

    D. Chatziparaschis, M. G. Lagoudakis, and P. Partsinevelos, “Aerial and ground robot collaboration for autonomous mapping in search and rescue missions,” Drones, vol. 4, no. 4, pp. 1–24, 2020

    2020

  29. [35]

    Uav-supported route planning for ugvs in semi-deterministic agricultural environments,

    D. Katikaridis, V . Moysiadis, N. Tsolakis, P. Busato, D. Kateris, S. Pearson, C. G. Sørensen, and D. Bochtis, “Uav-supported route planning for ugvs in semi-deterministic agricultural environments,” Agronomy, vol. 12, no. 8, p. 1937, 2022

    1937

  30. [36]

    Colag: A collaborative air-ground framework for perception-limited ugvs’ navigation,

    Z. Li, R. Mao, N. Chen, C. Xu, F. Gao, and Y . Cao, “Colag: A collaborative air-ground framework for perception-limited ugvs’ navigation,” in Proc. Proc. IEEE Int. Conf. Robot. Automat. , 2024, pp. 16 781–16 787

    2024

  31. [37]

    Active autonomous aerial exploration for ground robot path planning,

    J. Delmerico, E. Mueggler, J. Nitsch, and D. Scaramuzza, “Active autonomous aerial exploration for ground robot path planning,” IEEE Robot. Autom. Lett. , vol. 2, no. 2, pp. 664–671, 2017

    2017

  32. [38]

    Decentralized planning and control for uav–ugv cooperative teams,

    B. Arbanas, A. Ivanovic, M. Car, M. Orsag, T. Petrovic, and S. Bogdan, “Decentralized planning and control for uav–ugv cooperative teams,” Auton. Robot., vol. 42, no. 8, pp. 1601–1618, 2018

    2018

  33. [39]

    Air-ground localization and map augmentation using monocular dense reconstruction,

    C. Forster, M. Pizzoli, and D. Scaramuzza, “Air-ground localization and map augmentation using monocular dense reconstruction,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. , 2013, pp. 3971–3978

    2013

  34. [40]

    2d topological map building by uavs for ground robot navigation,

    Y . Wang, X. Zhang, Y . Liu, and Y . Zhuang, “2d topological map building by uavs for ground robot navigation,” inProc. Int. Conf. Robot. Biomimetics, 2021, pp. 663–668

    2021

  35. [41]

    Efficient autonomous exploration with incrementally built topological map in 3-d environments,

    C. Wang, H. Ma, W. Chen, L. Liu, and M. Q.-H. Meng, “Efficient autonomous exploration with incrementally built topological map in 3-d environments,” IEEE Trans. Instrum. Meas. , vol. 69, no. 12, pp. 9853–9865, 2020

    2020

  36. [42]

    Hybrid topological and 3d dense mapping through autonomous exploration for large indoor environments,

    C. Gomez, M. Fehr, A. Millane, A. C. Hernandez, J. Nieto, R. Barber, and R. Siegwart, “Hybrid topological and 3d dense mapping through autonomous exploration for large indoor environments,” in Proc. IEEE Int. Conf. Robot. Automat. , 2020, pp. 9673–9679

    2020

  37. [43]

    Collabora- tive semantic understanding and mapping framework for autonomous systems,

    Y . Yue, C. Zhao, Z. Wu, C. Yang, Y . Wang, and D. Wang, “Collabora- tive semantic understanding and mapping framework for autonomous systems,” IEEE/ASME Trans. Mechatronics , vol. 26, no. 2, pp. 978– 989, 2021

    2021

  38. [44]

    Real-time multi-modal semantic fusion on unmanned aerial vehicles with label propagation for cross-domain adaptation,

    S. Bultmann, J. Quenzel, and S. Behnke, “Real-time multi-modal semantic fusion on unmanned aerial vehicles with label propagation for cross-domain adaptation,” Robot. Auton. Syst., vol. 159, p. 104286, 2023

    2023

  39. [45]

    Stronger together: Air-ground robotic collaboration using semantics,

    I. D. Miller, F. Cladera, T. Smith, C. J. Taylor, and V . Kumar, “Stronger together: Air-ground robotic collaboration using semantics,” IEEE Robot. Autom. Lett. , vol. 7, no. 4, pp. 9643–9650, 2022

    2022

  40. [46]

    Cross-level multi-modal features learning with transformer for rgb-d object recognition,

    Y . Zhang, M. Yin, H. Wang, and C. Hua, “Cross-level multi-modal features learning with transformer for rgb-d object recognition,” IEEE Trans. Circuits Syst. Video Technol. , vol. 33, no. 12, pp. 7121–7130, 2023

    2023

  41. [47]

    A survey on vision-based uav navigation,

    Y . Lu, Z. Xue, G.-S. Xia, and L. Zhang, “A survey on vision-based uav navigation,” Geo-spatial Inform. Sci. , vol. 21, no. 1, pp. 21–32, 2018

    2018

  42. [48]

    V oxel map to occupancy map conversion using free space projection for efficient map representation for aerial and ground robots,

    S. Fredriksson, A. Saradagi, and G. Nikolakopoulos, “V oxel map to occupancy map conversion using free space projection for efficient map representation for aerial and ground robots,” IEEE Robot. Autom. Lett., vol. 9, no. 12, pp. 11 625–11 632, 2024

    2024

  43. [49]

    A hybrid path planning method in unmanned air/ground vehicle (uav/ugv) cooperative systems,

    J. Li, G. Deng, C. Luo, Q. Lin, Q. Yan, and Z. Ming, “A hybrid path planning method in unmanned air/ground vehicle (uav/ugv) cooperative systems,” IEEE Trans. Veh. Technol., vol. 65, no. 12, pp. 9585–9596, 2016

    2016

  44. [50]

    Effective safety strategy for mobile robots based on laser-visual fusion in home environments,

    Y . Zhang, G. Tian, X. Shao, and J. Cheng, “Effective safety strategy for mobile robots based on laser-visual fusion in home environments,” IEEE Trans. Syst., Man, Cybern., Syst. , vol. 52, no. 7, pp. 4138–4150, 2022

    2022

  45. [51]

    Uav-borne 2-d and 3-d radar-based grid mapping,

    P. Hügler, T. Grebner, C. Knill, and C. Waldschmidt, “Uav-borne 2-d and 3-d radar-based grid mapping,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2020

    2020

  46. [52]

    Circular accessible depth: A robust traversability representation for ugv naviga- tion,

    S. Xie, R. Song, Y . Zhao, X. Huang, Y . Li, and W. Zhang, “Circular accessible depth: A robust traversability representation for ugv naviga- tion,” IEEE Trans. Robot. , vol. 39, no. 6, pp. 4875–4891, 2023

    2023

  47. [53]

    Milestones in autonomous driving and intelligent vehicles—part i: Control, computing system design, communication, hd map, testing, and human behaviors,

    L. Chen, Y . Li, C. Huang, Y . Xing, D. Tian, L. Li, Z. Hu, S. Teng, C. Lv, J. Wanget al., “Milestones in autonomous driving and intelligent vehicles—part i: Control, computing system design, communication, hd map, testing, and human behaviors,” IEEE Trans. Syst., Man, Cybern....

    2023

  48. [54]

    Multi-constellation- inspired single-shot global lidar localization,

    T. Zhang, G. Wang, Y . Chen, H. Zhang, and J. Hu, “Multi-constellation- inspired single-shot global lidar localization,” inProc. AAAI Conf. Artif. Intell., vol. 38, no. 9, 2024, pp. 10 404–10 412

    2024

  49. [55]

    An intelligent ground- air cooperative navigation framework based on visual-aided method in indoor environments,

    Z.-H. Wang, K.-Y . Qin, T. Zhang, and B. Zhu, “An intelligent ground- air cooperative navigation framework based on visual-aided method in indoor environments,” Unmanned Syst. , vol. 9, no. 03, pp. 237–246, 2021

    2021

  50. [56]

    Global ugv path planning on point cloud maps created by uav,

    R. Fedorenko, A. Gabdullin, and A. Fedorenko, “Global ugv path planning on point cloud maps created by uav,” in Proc. IEEE Int. Conf. Intell. Transp. Eng., 2018, pp. 253–258

    2018

  51. [57]

    Air-ground collaborative mapping based on region matching under terrain constraints,

    S. Pei, X. Zheng, X. Jiang, Z. Li, and W. Yao, “Air-ground collaborative mapping based on region matching under terrain constraints,” in Proc. IEEE Int. Conf. Unmanned Syst. , 2023, pp. 1399–1404

    2023

  52. [58]

    Safe and efficient robot manipulation: Task-oriented environment modeling and object pose estimation,

    Y . Zhang, G. Tian, and X. Shao, “Safe and efficient robot manipulation: Task-oriented environment modeling and object pose estimation,”IEEE Trans. Instrum. Meas. , vol. 70, pp. 1–12, 2021

    2021

  53. [59]

    Perception and navigation in autonomous systems in the era of learning: A survey,

    Y . Tang, C. Zhao, J. Wang, C. Zhang, Q. Sun, W. X. Zheng, W. Du, F. Qian, and J. Kurths, “Perception and navigation in autonomous systems in the era of learning: A survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 12, pp. 9604–9624, 2023

    2023

  54. [60]

    Building metric-topological map to efficient object search for mobile robot,

    Y . Zhang, G. Tian, X. Shao, S. Liu, M. Zhang, and P. Duan, “Building metric-topological map to efficient object search for mobile robot,” IEEE Trans. Ind. Electron. , vol. 69, no. 7, pp. 7076–7087, 2022

    2022

  55. [61]

    Ternformer: Topology-enhanced road network extraction by exploring local con- nectivity,

    B. Wang, Q. Liu, Z. Hu, W. Wang, and Y . Wang, “Ternformer: Topology-enhanced road network extraction by exploring local con- nectivity,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–14, 2023

    2023

  56. [62]

    Topomap: Topological mapping and navigation based on visual slam maps,

    F. Blochliger, M. Fehr, M. Dymczyk, T. Schneider, and R. Siegwart, “Topomap: Topological mapping and navigation based on visual slam maps,” in Proc. IEEE Int. Conf. Robot. Automat., 2018, pp. 3818–3825

    2018

  57. [63]

    Leveraging multimodal sensing and topometric mapping for human- like autonomous navigation in complex environments,

    K. Tsiakas, D. Alexiou, D. Giakoumis, A. Gasteratos, and D. Tzovaras, “Leveraging multimodal sensing and topometric mapping for human- like autonomous navigation in complex environments,” in 2Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. , 2023, pp. 7415–7421

    2023

  58. [64]

    Topology density map for urban data visualization and analysis,

    Z. Feng, H. Li, W. Zeng, S.-H. Yang, and H. Qu, “Topology density map for urban data visualization and analysis,” IEEE Trans. Vis. Comp. Graphics, vol. 27, no. 2, pp. 828–838, 2021

    2021

  59. [65]

    Semantics-empowered space-air-ground-sea integrated network: New paradigm, frameworks, and challenges,

    S. Meng, S. Wu, J. Zhang, J. Cheng, H. Zhou, and Q. Zhang, “Semantics-empowered space-air-ground-sea integrated network: New paradigm, frameworks, and challenges,” IEEE Commun. Surveys Tuts., 2024, to be published, DOI: 10.1109/COMST.2024.3416309

    2024

  60. [66]

    Enhanced scene understanding and situation awareness for autonomous vehicles based on semantic segmentation,

    Y . Zhao, L. Wang, X. Yun, C. Chai, Z. Liu, W. Fan, X. Luo, Y . Liu, and X. Qu, “Enhanced scene understanding and situation awareness for autonomous vehicles based on semantic segmentation,” IEEE Trans. Syst., Man, Cybern., Syst. , vol. 54, no. 11, pp. 6537–6549, 2024. 16

    2024

  61. [67]

    Deep semantic image segmentation for uav-ugv cooperative path planning: A car park use case,

    M. K. Vasi ´c, A. Drak, N. Bugarin, S. Kruži ´c, J. Musi ´c, C. Pomrehn, M. Schöbel, M. Johenneken, I. Stan ˇci´c, V . Papi´c et al., “Deep semantic image segmentation for uav-ugv cooperative path planning: A car park use case,” in Proc. Int. Conf. Softw. Telecommun. Comput. N...

    2020

  62. [68]

    Real-time semantic mapping for autonomous off-road navigation,

    D. Maturana, P.-W. Chou, M. Uenoyama, and S. Scherer, “Real-time semantic mapping for autonomous off-road navigation,” in Proc. Int. Conf. Field Service Robot. , 2018, pp. 335–350

    2018

  63. [69]

    An informative path planning framework for active learning in uav-based semantic mapping,

    J. Rückin, F. Magistri, C. Stachniss, and M. Popovi ´c, “An informative path planning framework for active learning in uav-based semantic mapping,” IEEE Trans. Robot. , vol. 39, no. 6, pp. 4279–4296, 2023

    2023

  64. [70]

    Challenges and opportunities for large-scale exploration with air-ground teams using semantics,

    F. Cladera, I. D. Miller, Z. Ravichandran, V . Murali, J. Hughes, M. A. Hsieh, C. Taylor, and V . Kumar, “Challenges and opportunities for large-scale exploration with air-ground teams using semantics,” inProc. IEEE Int. Conf. Robot. Automat. , 2024, pp. 1–6

    2024

  65. [71]

    Consistent localization for autonomous robots with inter-vehicle gnss information fusion,

    X. Li, B. Song, Z. Shen, Y . Zhou, H. Lyu, and Z. Qin, “Consistent localization for autonomous robots with inter-vehicle gnss information fusion,” IEEE Commun. Lett. , vol. 27, no. 1, pp. 120–124, 2023

    2023

  66. [72]

    Gps data correction based on fuzzy logic for tracking land vehicles,

    P. J. Correa-Caicedo, H. Rostro-González, M. A. Rodriguez-Licea, Ó. O. Gutiérrez-Frías, C. A. Herrera-Ramírez, I. I. Méndez-Gurrola, M. Cano-Lara, and A. I. Barranco-Gutiérrez, “Gps data correction based on fuzzy logic for tracking land vehicles,” Mathematics, vol. 9, no. 21, ...

    2021

  67. [73]

    A novel position determination method for the modular snake-like natural gas pipeline inspection robot in a gps denied environment,

    M. Yılmaz, H. A. Yavasoglu, and K. Gokce, “A novel position determination method for the modular snake-like natural gas pipeline inspection robot in a gps denied environment,” Int. J. Global Warming, vol. 29, no. 3, pp. 240–253, 2023

    2023

  68. [74]

    Wi-fi rtt/encoder/ins- based robot indoor localization using smartphones,

    B. Zhou, Z. Wu, Z. Chen, X. Liu, and Q. Li, “Wi-fi rtt/encoder/ins- based robot indoor localization using smartphones,” IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 6683–6694, 2023

    2023

  69. [75]

    Factor graph-based high-precision visual positioning for agricultural robots with fiducial markers,

    W. Zhang, L. Gong, S. Huang, S. Wu, and C. Liu, “Factor graph-based high-precision visual positioning for agricultural robots with fiducial markers,” Comput. Electron. Agriculture, vol. 201, p. 107295, 2022

    2022

  70. [76]

    Robot localization based on semantic information in dynamic indoor environments with similar layouts,

    R. Song, J. Liu, W. Bi, Y . Zhang, M. Zhang, C.-H. Zhang, and C. Hua, “Robot localization based on semantic information in dynamic indoor environments with similar layouts,” in Proc. IEEE Int. Conf. Robot. Biomimetics, 2024, pp. 1–6

    2024

  71. [77]

    Air-ground collaborative localisation in forests using lidar canopy maps,

    L. C. de Lima, M. Ramezani, P. Borges, and M. Brünig, “Air-ground collaborative localisation in forests using lidar canopy maps,” IEEE Robot. Autom. Lett. , vol. 8, no. 3, pp. 1818–1825, 2023

    2023

  72. [78]

    Dll: Direct lidar localization. a map-based localization approach for aerial robots,

    F. Caballero and L. Merino, “Dll: Direct lidar localization. a map-based localization approach for aerial robots,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. , 2021, pp. 5491–5498

    2021

  73. [79]

    Tag-based visual-inertial localization of unmanned aerial vehicles in indoor con- struction environments using an on-manifold extended kalman filter,

    N. Kayhani, W. Zhao, B. McCabe, and A. P. Schoellig, “Tag-based visual-inertial localization of unmanned aerial vehicles in indoor con- struction environments using an on-manifold extended kalman filter,” Autom. Construct., vol. 135, p. 104112, 2022

    2022

  74. [80]

    A robust autonomous navigation and mapping system based on gps and lidar data for unconstraint environment,

    J. Patoliya, H. Mewada, M. Hassaballah, M. A. Khan, and S. Kadry, “A robust autonomous navigation and mapping system based on gps and lidar data for unconstraint environment,” Earth Sci. Informat. , vol. 15, no. 4, pp. 2703–2715, 2022

    2022

  75. [81]

    Sensor fusion of ins, odometer and gps for robot localization,

    S. Yousuf and M. B. Kadri, “Sensor fusion of ins, odometer and gps for robot localization,” in Proc. IEEE Conf. Syst. Process Control , 2016, pp. 118–123

    2016

  76. [82]

    Milestones in autonomous driving and intelligent vehicles—part ii: Perception and planning,

    L. Chen, S. Teng, B. Li, X. Na, Y . Li, Z. Li, J. Wang, D. Cao, N. Zheng, and F.-Y . Wang, “Milestones in autonomous driving and intelligent vehicles—part ii: Perception and planning,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 53, no. 10, pp. 6401–6415, 2023

    2023

  77. [83]

    A review on absolute visual localization for uav,

    A. Couturier and M. A. Akhloufi, “A review on absolute visual localization for uav,” Robot. Auton. Syst. , vol. 135, p. 103666, 2021

    2021

  78. [84]

    Accuracy improvement of cooperative localization using uav and ugv,

    R. Shimizu and Y . Sugita, “Accuracy improvement of cooperative localization using uav and ugv,” Adv. Robot., vol. 37, no. 16, pp. 999– 1011, 2023

    2023

  79. [85]

    Information-aided inertial navigation: A review,

    D. Engelsman and I. Klein, “Information-aided inertial navigation: A review,” IEEE Trans. Instrum. Meas. , vol. 72, pp. 1–18, 2023

    2023

  80. [86]

    Spins: A structure priors aided inertial navigation system,

    Y . Lyu, T.-M. Nguyen, L. Liu, M. Cao, S. Yuan, T. H. Nguyen, and L. Xie, “Spins: A structure priors aided inertial navigation system,” J. Field Robot., vol. 40, no. 4, pp. 879–900, 2023

    2023

  81. [87]

    Inertial navigation meets deep learning: A survey of current trends and future directions,

    N. Cohen and I. Klein, “Inertial navigation meets deep learning: A survey of current trends and future directions,” Results Eng., p. 103565, 2024

    2024

  82. [88]

    Positioning systems for unmanned underwater vehicles: A comprehensive review,

    C. Alexandris, P. Papageorgas, and D. Piromalis, “Positioning systems for unmanned underwater vehicles: A comprehensive review,” Appl. Sci., vol. 14, no. 21, p. 9671, 2024

    2024

  83. [89]

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

    G. Niu, Q. Yang, Y . Gao, and M.-O. Pun, “Vision-based autonomous landing for unmanned aerial and ground vehicles cooperative systems,” IEEE Robot. Autom. Lett. , vol. 7, no. 3, pp. 6234–6241, 2022

    2022

  84. [90]

    Search planning of a uav/ugv team with localization uncertainty in a subterranean environment,

    M. De Petrillo, J. Beard, Y . Gu, and J. N. Gross, “Search planning of a uav/ugv team with localization uncertainty in a subterranean environment,” IEEE Aerosp. Electron. Syst. Mag. , vol. 36, no. 6, pp. 6–16, 2021

    2021

  85. [91]

    A unmanned aerial vehicle (uav)/unmanned ground vehicle (ugv) dynamic autonomous docking scheme in gps-denied environments,

    C. Cheng, X. Li, L. Xie, and L. Li, “A unmanned aerial vehicle (uav)/unmanned ground vehicle (ugv) dynamic autonomous docking scheme in gps-denied environments,” Drones, vol. 7, no. 10, p. 613, 2023

    2023

  86. [92]

    Omega: Efficient occlusion-aware navigation for air-ground robot in dynamic environments via state space model,

    J. Wang, D. Huang, X. Guan, Z. Sun, T. Shen, F. Liu, and H. Cui, “Omega: Efficient occlusion-aware navigation for air-ground robot in dynamic environments via state space model,” IEEE Robot. Autom. Lett., 2024

    2024

  87. [93]

    Design of dual-lidar high precision natural navigation system,

    H. Zhang, L. Yu, and S. Fei, “Design of dual-lidar high precision natural navigation system,” IEEE Sensors J. , vol. 22, no. 7, pp. 7231– 7239, 2022

    2022

  88. [94]

    A survey on map-based localization techniques for autonomous vehicles,

    A. Chalvatzaras, I. Pratikakis, and A. A. Amanatiadis, “A survey on map-based localization techniques for autonomous vehicles,” IEEE Trans. Intell. Vehicles, vol. 8, no. 2, pp. 1574–1596, 2023

    2023

  89. [95]

    Lidar-based place recognition for au- tonomous driving: A survey,

    Y . Zhang, P. Shi, and J. Li, “Lidar-based place recognition for au- tonomous driving: A survey,” ACM Comput. Surv., vol. 57, no. 4, pp. 1–36, 2025

    2025

  90. [96]

    Gnss/multi- sensor fusion using continuous-time factor graph optimization for robust localization,

    H. Zhang, C.-C. Chen, H. Vallery, and T. D. Barfoot, “Gnss/multi- sensor fusion using continuous-time factor graph optimization for robust localization,” IEEE Trans. Robot., vol. 40, pp. 4003–4023, 2024

    2024

  91. [97]

    A tightly-coupled and keyframe-based visual-inertial-lidar odometry system for ugvs with adaptive sensor reliability evaluation,

    J. Yin, Y . Zhuang, F. Yan, Y .-J. Liu, and H. Zhang, “A tightly-coupled and keyframe-based visual-inertial-lidar odometry system for ugvs with adaptive sensor reliability evaluation,”IEEE Trans. Syst., Man, Cybern., Syst., vol. 54, no. 8, pp. 4976–4985, 2024

    2024

  92. [98]

    3d distance filter for the autonomous navigation of uavs in agricultural scenarios,

    C. Donati, M. Mammarella, L. Comba, A. Biglia, P. Gay, and F. Dabbene, “3d distance filter for the autonomous navigation of uavs in agricultural scenarios,” Remote Sensing, vol. 14, no. 6, p. 1374, 2022

    2022

  93. [99]

    Multi-sensor fusion and cooperative perception for autonomous driv- ing: A review,

    C. Xiang, C. Feng, X. Xie, B. Shi, H. Lu, Y . Lv, M. Yang, and Z. Niu, “Multi-sensor fusion and cooperative perception for autonomous driv- ing: A review,” IEEE Intell. Transp. Syst. Mag. , vol. 15, no. 5, pp. 36–58, 2023

    2023

  94. [100]

    Mobile robot localization: Current challenges and future prospective,

    I. Ullah, D. Adhikari, H. Khan, M. S. Anwar, S. Ahmad, and X. Bai, “Mobile robot localization: Current challenges and future prospective,” Comput. Sci. Rev., vol. 53, p. 100651, 2024

    2024

  95. [101]

    A comparative review on multi-modal sensors fusion based on deep learning,

    Q. Tang, J. Liang, and F. Zhu, “A comparative review on multi-modal sensors fusion based on deep learning,” Signal Process. , p. 109165, 2023

    2023

  96. [102]

    User preference-aware naviga- tion for mobile robot in domestic via defined virtual area,

    Y . Zhang, C.-H. Zhang, and X. Shao, “User preference-aware naviga- tion for mobile robot in domestic via defined virtual area,” J. Netw. Comput. Appl., vol. 173, p. 102885, 2021

    2021

  97. [103]

    Bi-am-rrt*: A fast and efficient sampling-based motion planning algorithm in dynamic environments,

    Y . Zhang, H. Wang, M. Yin, J. Wang, and C. Hua, “Bi-am-rrt*: A fast and efficient sampling-based motion planning algorithm in dynamic environments,” IEEE Trans. Intell. Vehicles , vol. 9, no. 1, pp. 1282– 1293, 2024

    2024

  98. [104]

    A hybrid topological mapping and navigation method for large area robot mapping,

    A. A. Ravankar, A. Ravankar, T. Emaru, and Y . Kobayashi, “A hybrid topological mapping and navigation method for large area robot mapping,” in Proc. Annu. Conf. Soc. Instrum. Control Eng. Jpn. , 2017, pp. 1104–1107

    2017

  99. [105]

    Real-time global action planning for unmanned ground vehicle exploration in three- dimensional spaces,

    X. Zuo, J. Zhou, F. Yang, F. Su, H. Zhu, and L. Li, “Real-time global action planning for unmanned ground vehicle exploration in three- dimensional spaces,” Expert Syst. Appl. , vol. 215, p. 119264, 2023

    2023

  100. [106]

    Robot navigation via spatial and temporal coherent semantic maps,

    I. Kostavelis, K. Charalampous, A. Gasteratos, and J. K. Tsotsos, “Robot navigation via spatial and temporal coherent semantic maps,” Eng. Applicat. Artif. Intell. , vol. 48, pp. 173–187, 2016

    2016

  101. [107]

    Semantic rgb-d slam for rescue robot navigation,

    W. Deng, K. Huang, X. Chen, Z. Zhou, C. Shi, R. Guo, and H. Zhang, “Semantic rgb-d slam for rescue robot navigation,”IEEE Access, vol. 8, pp. 221 320–221 329, 2020

    2020

  102. [108]

    A note on dijkstra’s shortest path algorithm,

    D. B. Johnson, “A note on dijkstra’s shortest path algorithm,” J. ACM, vol. 20, no. 3, pp. 385–388, 1973

    1973

  103. [109]

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

    P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern., vol. 4, no. 2, pp. 100–107, 1968

    1968

  104. [110]

    Event-driven programming-based path planning and nav- igation of uavs around a complex urban environment,

    M. Kazim, A. T. Azar, A. Koubaa, Z. F. Ibrahim, A. Zaidi, and L. Zhang, “Event-driven programming-based path planning and nav- igation of uavs around a complex urban environment,” in Unmanned Aerial Syst., 2021, pp. 531–565

    2021

  105. [111]

    Review of navigation methods for uav-based parcel delivery,

    D. Dissanayaka, T. R. Wanasinghe, O. De Silva, A. Jayasiri, and G. K. Mann, “Review of navigation methods for uav-based parcel delivery,” IEEE Trans. Autom. Sci. Eng. , vol. 21, no. 1, pp. 1068–1082, 2024

    2024

  106. [112]

    A review of uav autonomous navigation in gps-denied environments,

    Y . Chang, Y . Cheng, U. Manzoor, and J. Murray, “A review of uav autonomous navigation in gps-denied environments,” Robot. Auton. Syst., p. 104533, 2023. 17

    2023

  107. [113]

    Envi- ronment modeling for service robots from a task execution perspec- tive,

    Y . Zhang, G. Tian, C.-H. Zhang, C. Hua, and C. K. Ahn, “Envi- ronment modeling for service robots from a task execution perspec- tive,” IEEE/CAA J. Automat. Sinica , 2025, to be published, DOI: 10.1109/JAS.2025.125168

    2025

  108. [114]

    Sharing three-dimensional occupancy grid maps to support multi-uavs cooperative navigation systems,

    M. Basso, A. S. da Silva, D. A. Stocchero, and E. P. de Freitas, “Sharing three-dimensional occupancy grid maps to support multi-uavs cooperative navigation systems,” J. Field Robot. , vol. 41, no. 5, pp. 1386–1407, 2024

    2024

  109. [115]

    Sphere-graph: A compact 3d topological map for robotic navigation and segmentation of complex environments,

    M. Spencer, R. Sawtell, and S. Kitchen, “Sphere-graph: A compact 3d topological map for robotic navigation and segmentation of complex environments,” IEEE Robot. Autom. Lett., vol. 9, no. 3, pp. 2567–2574, 2024

    2024

  110. [116]

    Sparse 3d topo- logical graphs for micro-aerial vehicle planning,

    H. Oleynikova, Z. Taylor, R. Siegwart, and J. Nieto, “Sparse 3d topo- logical graphs for micro-aerial vehicle planning,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. , 2018, pp. 1–9

    2018

  111. [117]

    A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots,

    L. Xia, J. Cui, R. Shen, X. Xu, Y . Gao, and X. Li, “A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots,” Int. J. Adv. Robot. Syst., vol. 17, no. 3, p. 1729881420919185, 2020

    2020

  112. [118]

    Image-based obstacle detection methods for the safe navigation of unmanned vehicles: A review,

    S. Badrloo, M. Varshosaz, S. Pirasteh, and J. Li, “Image-based obstacle detection methods for the safe navigation of unmanned vehicles: A review,” Remote Sens., vol. 14, no. 15, p. 3824, 2022

    2022

  113. [119]

    Pilot-scale development of a uav-ugv hybrid with air-based ugv path planning,

    N. Giakoumidis, J. U. Bak, J. V . Gómez, A. Llenga, and N. Mavridis, “Pilot-scale development of a uav-ugv hybrid with air-based ugv path planning,” in Int. Conf. Frontiers Inf. Technol. , 2012, pp. 204–208

    2012

  114. [120]

    Collaborative navigation for flying and walking robots,

    P. Fankhauser, M. Bloesch, P. Krüsi, R. Diethelm, M. Wermelinger, T. Schneider, M. Dymczyk, M. Hutter, and R. Siegwart, “Collaborative navigation for flying and walking robots,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. , 2016, pp. 2859–2866

    2016

  115. [121]

    Aerial- guided navigation of a ground robot among movable obstacles,

    E. Mueggler, M. Faessler, F. Fontana, and D. Scaramuzza, “Aerial- guided navigation of a ground robot among movable obstacles,” in Proc. IEEE Int. Symp. Safety Secur. Rescue Robot. IEEE, 2014, pp. 1–8

    2014

  116. [122]

    Path planning of messenger uav in air-ground coordination,

    D. Yulong, X. Bin, C. Jie, F. Hao, Z. Yangguang, G. Guanqiang, and D. Lihua, “Path planning of messenger uav in air-ground coordination,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 8045–8051, 2017

    2017

  117. [123]

    A memetic algorithm for curvature-constrained path planning of messenger uav in air-ground coordination,

    Y . Ding, B. Xin, L. Dou, J. Chen, and B. M. Chen, “A memetic algorithm for curvature-constrained path planning of messenger uav in air-ground coordination,” IEEE Trans. Autom. Sci. Eng. , vol. 19, no. 4, pp. 3735–3749, 2022

    2022

  118. [124]

    A scenario for a multi-uav mapping and surveillance system in emergency response applications,

    M. Stampa, A. Sutorma, U. Jahn, F. Willich, S. Pratzler-Wanczura, J. Thiem, C. Röhrig, and C. Wolff, “A scenario for a multi-uav mapping and surveillance system in emergency response applications,” in Proc. IEEE 5th Int. Symp. Smart Wireless Syst. Conferences Intell. Data Acqu...

    2020

  119. [125]

    Path planning for the marsupial double-uavs system in air-ground collaborative applica- tion,

    S. Ren, Y . Chen, L. Xiong, Z. Chen, and M. Chen, “Path planning for the marsupial double-uavs system in air-ground collaborative applica- tion,” in Proc. 37th Chin. Control Conf. , 2018, pp. 5420–5425

    2018

  120. [126]

    A hybrid genetic algorithm on routing and scheduling for vehicle-assisted multi- drone parcel delivery,

    K. Peng, J. Du, F. Lu, Q. Sun, Y . Dong, P. Zhou, and M. Hu, “A hybrid genetic algorithm on routing and scheduling for vehicle-assisted multi- drone parcel delivery,” IEEE Access, vol. 7, pp. 49 191–49 200, 2019

    2019

  121. [127]

    Autonomous exploration and mapping system using heterogeneous uavs and ugvs in gps-denied environments,

    H. Qin, Z. Meng, W. Meng, X. Chen, H. Sun, F. Lin, and M. H. Ang, “Autonomous exploration and mapping system using heterogeneous uavs and ugvs in gps-denied environments,” IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1339–1350, 2019

    2019

  122. [128]

    Cooperative forest monitoring and fire detection using a team of uavs-ugvs,

    K. A. Ghamry, M. A. Kamel, and Y . Zhang, “Cooperative forest monitoring and fire detection using a team of uavs-ugvs,” in Proc. Int. Conf. Unmanned Aircr. Syst. , 2016, pp. 1206–1211

    2016

  123. [129]

    A cooperative uav/ugv platform for wildfire detection and fighting,

    C. Phan and H. H. Liu, “A cooperative uav/ugv platform for wildfire detection and fighting,” in Proc. Asia Simulat. Conf. 7th Int. Conf. Syst. Simulat. Sci. Comput. , 2008, pp. 494–498

    2008

  124. [130]

    Radiation search operations using scene understand- ing with autonomous uav and ugv,

    G. Christie, A. Shoemaker, K. Kochersberger, P. Tokekar, L. McLean, and A. Leonessa, “Radiation search operations using scene understand- ing with autonomous uav and ugv,” J. Field Robot., vol. 34, no. 8, pp. 1450–1468, 2017

    2017

  125. [131]

    A robust uav- ugv collaborative framework for persistent surveillance in disaster management applications,

    M. S. Mondal, S. Ramasamy, J. D. Humann, J. M. Dotterweich, J.- P. F. Reddinger, M. A. Childers, and P. Bhounsule, “A robust uav- ugv collaborative framework for persistent surveillance in disaster management applications,” in Proc. Int. Conf. Unmanned Aircr. Syst. , 2024, pp....

    2024

  126. [132]

    Deep reinforcement learning and ant colony optimization supporting multi-ugv path planning and task assignment in 3d environments,

    B. Jin, Y . Sun, W. Wu, Q. Gao, and P. Si, “Deep reinforcement learning and ant colony optimization supporting multi-ugv path planning and task assignment in 3d environments,” IET Intell. Transp. Syst., vol. 18, no. 9, pp. 1652–1664, 2024

    2024

  127. [133]

    A formation cooperative reconnaissance strategy for multi-ugvs in partially unknown environment,

    H. Zhang, T. Yang, and Z. Su, “A formation cooperative reconnaissance strategy for multi-ugvs in partially unknown environment,” J. Chin. Inst. Engineers, vol. 46, no. 6, pp. 551–562, 2023

    2023

  128. [134]

    A coordinated behavior planning and trajectory plan- ning framework for multi-ugvs in unstructured narrow interaction scenarios,

    Z. Zang, X. Zhang, J. Song, Y . Lu, Z. Li, H. Dong, Y . Li, Z. Ju, and J. Gong, “A coordinated behavior planning and trajectory plan- ning framework for multi-ugvs in unstructured narrow interaction scenarios,” IEEE Trans. Intell. Vehicles , 2024, to be published. doi: 10.1109...

    2024

  129. [135]

    Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in gps-denied environ- ments,

    D. Scaramuzza, M. C. Achtelik, L. Doitsidis, F. Friedrich, E. Kos- matopoulos, A. Martinelli, M. W. Achtelik, M. Chli, S. Chatzichristofis, L. Kneip et al. , “Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in gps-denied environ- ...

    2014

  130. [138]

    An optimal uav and ugv cooperative network navigation algorithm for bushfire surveillance and disaster relief,

    J. Wei and Z. Fang, “An optimal uav and ugv cooperative network navigation algorithm for bushfire surveillance and disaster relief,” in Proc. Int. Conf. Comput. Autom. Eng. , 2024, pp. 636–641

    2024

  131. [139]

    Uav and ugv autonomous cooperation for wildfire hotspot surveillance,

    D. Pasini, C. Jiang, and M.-P. Jolly, “Uav and ugv autonomous cooperation for wildfire hotspot surveillance,” in Proc. IEEE MIT Undergraduate Res. Technol. Conf. , 2022, pp. 1–5

    2022

  132. [140]

    An aerial/ground robot team for autonomous firefighting in urban gnss- denied scenarios

    S. Martinez-Rozas, R. Rey, D. Alejo, D. Acedo, J. A. Cobano, A. Rodríguez-Ramos, P. Campoy, L. Merino, and F. Caballero, “An aerial/ground robot team for autonomous firefighting in urban gnss- denied scenarios.” Field Robot., vol. 2, no. 1, pp. 241–273, 2022

    2022

  133. [141]

    Semantic grounding for long-term autonomy of mobile robots toward dynamic object search in home environments,

    Y . Zhang, G. Tian, X. Shao, M. Zhang, and S. Liu, “Semantic grounding for long-term autonomy of mobile robots toward dynamic object search in home environments,” IEEE Trans. Ind. Electron. , vol. 70, no. 2, pp. 1655–1665, 2023

    2023

  134. [142]

    An object slam framework for association, mapping, and high-level tasks,

    Y . Wu, Y . Zhang, D. Zhu, Z. Deng, W. Sun, X. Chen, and J. Zhang, “An object slam framework for association, mapping, and high-level tasks,” IEEE Trans. Robot. , vol. 39, no. 4, pp. 2912–2932, 2023

    2023

  135. [143]

    Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments,

    H. Wei, J. Jiao, X. Hu, J. Yu, X. Xie, J. Wu, Y . Zhu, Y . Liu, L. Wang, and M. Liu, “Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments,” Int. J. Robot. Res. , 2024, to be published, DOI: 10.1177/02783649241303525

    2024 doi

  136. [144]

    Msf-slam: Multi-sensor-fusion-based simultaneous localization and mapping for complex dynamic environments,

    X. Lv, Z. He, Y . Yang, J. Nie, Z. Dong, S. Wang, and M. Gao, “Msf-slam: Multi-sensor-fusion-based simultaneous localization and mapping for complex dynamic environments,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 12, pp. 19 699–19 713, 2024

    2024

  137. [145]

    Applying deep learning to real-time uav-based forest monitoring: Leveraging multi-sensor imagery for improved results,

    T. Marques, S. Carreira, R. Miragaia, J. Ramos, and A. Pereira, “Applying deep learning to real-time uav-based forest monitoring: Leveraging multi-sensor imagery for improved results,” Expert Syst. Appl., vol. 245, p. 123107, 2024

    2024

  138. [146]

    Artificial intelligence, machine learning and deep learning in advanced robotics, a review,

    M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cogn. Robot., vol. 3, pp. 54–70, 2023

    2023

  139. [147]

    Human-guided reinforcement learning with sim-to-real transfer for autonomous navi- gation,

    J. Wu, Y . Zhou, H. Yang, Z. Huang, and C. Lv, “Human-guided reinforcement learning with sim-to-real transfer for autonomous navi- gation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 12, pp. 14 745–14 759, 2023

    2023

  140. [148]

    Aligning cyber space with physical world: A comprehensive survey on embodied ai,

    Y . Liu, W. Chen, Y . Bai, X. Liang, G. Li, W. Gao, and L. Lin, “Aligning cyber space with physical world: A comprehensive survey on embodied ai,” arXiv preprint arXiv:2407.06886 , 2024

    2024 arXiv

  141. [149]

    Zisvfm: Zero-shot object instance segmentation in indoor robotic environments with vision foundation models,

    Y . Zhang, M. Yin, W. Bi, H. Yan, S. Bian, C.-H. Zhang, and C. Hua, “Zisvfm: Zero-shot object instance segmentation in indoor robotic environments with vision foundation models,” IEEE Trans. Robot. , to be published, 2025

    2025

  142. [150]

    A survey of object goal navigation,

    J. Sun, J. Wu, Z. Ji, and Y .-K. Lai, “A survey of object goal navigation,” IEEE Trans. Autom. Sci. Eng., 2024, to be published, DOI: 10.1109/TASE.2024.3378010

    2024

  143. [151]

    Llmscenario: Large language model driven scenario generation,

    C. Chang, S. Wang, J. Zhang, J. Ge, and L. Li, “Llmscenario: Large language model driven scenario generation,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 54, no. 11, pp. 6581–6594, 2024

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.