Skip to main content

Advertisement

SpringerLink
Log in

Helix-HPSO approach for UAV path planning in a multi-building environment

  • Original Article
  • Published:
Journal of Reliable Intelligent Environments Aims and scope Submit manuscript

Abstract

Regular inspection of historic buildings is essential, while path planning of the building inspection is challenging because it requires comprehensive coverage at a low cost. Most of the previous research does not consider the multiple buildings’ environment. In this paper, a three-dimensional path planning approach is proposed to provide the inspection for multiple buildings. The proposed Helix-HPSO approach generates the helix-shaped path for each building and uses HPSO for path planning between buildings. The computational experiment validates the proposed approach. The helix-shaped path costs less than the traditional back-and-forth path for building inspection. HPSO is compared with other bio-inspired algorithms for optimization problems and PSO for path planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability Statement

Not applicable.

References

  1. Mader D, Blaskow R, Westfeld P, Weller C (2016) Potential of uav-based laser scanner and multispectral camera data in building inspection. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI-B1, pp. 1135–1142 . https://doi.org/10.5194/isprsarchives-XLI-B1-1135-2016

  2. Phung MD, Quach CH, Dinh TH, Ha Q (2017) Enhanced discrete particle swarm optimization path planning for uav vision-based surface inspection. Autom Constr 81:25–33. https://doi.org/10.1016/j.autcon.2017.04.013

    Article  Google Scholar 

  3. Bai X, Jiang H, Cui J, Lu K, Chen P, Zhang M (2021) Uav path planning based on improved a and dwa algorithms. International journal of aerospace engineering 2021:1–12. https://doi.org/10.1155/2021/4511252

    Article  Google Scholar 

  4. Wu X, Xu L, Zhen R, Wu X (2019) Biased sampling potentially guided intelligent bidirectional rrt algorithm for uav path planning in 3d environment. Math Probl Eng 2019:1–12. https://doi.org/10.1155/2019/5157403

    Article  Google Scholar 

  5. Zhang Z, Li J, Wang J (2018) Sequential convex programming for nonlinear optimal control problems in uav path planning. Aerosp Sci Technol 76:280–290. https://doi.org/10.1016/j.ast.2018.01.040

    Article  Google Scholar 

  6. Bolourian N, Hammad A (2020) Lidar-equipped uav path planning considering potential locations of defects for bridge inspection. Automation in Construction 117. https://doi.org/10.1016/j.autcon.2020.103250

  7. Tian R, Cao M, Ma F, Ji P (2020) Agricultural uav path planning based on improved a and gravity search mixed algorithm. J Phys: Conf Ser 1631(1):12082. https://doi.org/10.1088/1742-6596/1631/1/012082

    Article  Google Scholar 

  8. Luo G-c, Yu J-q, Mei Y-s, Zhang S-y (2015) Uav path planning in mixed-obstacle environment via artificial potential field method improved by additional control force. Asian journal of control 17(5), 1600–1610. https://doi.org/10.1002/asjc.960.istex:D5FC09FA138A45E026C35637F5C022F6E58D4272

  9. Lin N, Tang J, Li X, Zhao L (2019) A novel improved bat algorithm in uav path planning. Computers, materials & continua 61(1), 323–344. https://doi.org/10.32604/cmc.2019.05674

  10. Wang J, Wang G, Hu X, Luo H, Xu H (2020) Cooperative transmission tower inspection with a vehicle and a uav in urban areas. Energies 13(2). https://doi.org/10.3390/en13020326

  11. Chen Y, Yu J, Mei Y, Wang Y, Su X (2016) Modified central force optimization (mcfo) algorithm for 3d uav path planning. Neurocomputing (Amsterdam) 171:878–888. https://doi.org/10.1016/j.neucom.2015.07.044

    Article  Google Scholar 

  12. Yang Q, Yang Z, Zhang T, Hu G (2019) A random chemical reaction optimization algorithm based on dual containers strategy for multi-rotor uav path planning in transmission line inspection. Concurrency and computation 31(12). https://doi.org/10.1002/cpe.4658

  13. Fu Z, Yu J, Xie G, Chen Y, Mao Y (2018) A heuristic evolutionary algorithm of uav path planning. Wirel Commun Mob Comput 2018:1–11. https://doi.org/10.1155/2018/2851964

    Article  Google Scholar 

  14. Qu C, Gai W, Zhong M, Zhang J (2020) A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (uavs) path planning. Appl Soft Comput 89:106099. https://doi.org/10.1016/j.asoc.2020.106099

    Article  Google Scholar 

  15. Huo L, Zhu J, Li Z, Ma M (2021) A hybrid differential symbiotic organisms search algorithm for uav path planning. Sensors (Basel, Switzerland) 21(9):3037. https://doi.org/10.3390/s21093037

    Article  Google Scholar 

  16. Wang X, Pan J-S, Yang Q, Kong L, Snášel V, Chu S-C (2022) Modified mayfly algorithm for uav path planning. Drones (Basel) 6(5):134. https://doi.org/10.3390/drones6050134

    Article  Google Scholar 

  17. Jordan S, Moore J, Hovet S, Box J, Perry J, Kirsche K, Lewis D, Tse ZTH (2018) State-of-the-art technologies for uav inspections. IET Radar, Sonar & Navigation 12(2):151–164. https://doi.org/10.1049/iet-rsn.2017.0251

    Article  Google Scholar 

  18. Roca D, Lagüela S, Díaz-Vilariño L, Armesto J, Arias P (2013) Low-cost aerial unit for outdoor inspection of building façades. Autom Constr 36:128–135. https://doi.org/10.1016/j.autcon.2013.08.020

    Article  Google Scholar 

  19. Zainorizuan MJ, Kaamin M, Idris NA, Mohd Bukari S, Ali Z, Samion N, Anjang Ahmad M, Yee Yong L, Alvin John Meng Siang L, Mohamad Hanifi O, Siti Nazahiyah R, Mohd Shalahuddin A (2017) Visual inspection of historical buildings using micro uav. In: MATEC Web of Conferences, vol. 103. https://doi.org/10.1051/matecconf/201710307003

  20. Ham Y, Han KK, Lin JJ, Golparvar-Fard M (2016) Visual monitoring of civil infrastructure systems via camera-equipped unmanned aerial vehicles (uavs): a review of related works. Visualization in Engineering 4(1). https://doi.org/10.1186/s40327-015-0029-z

  21. Rachele G, Umberto M, Giuseppe M, Francesco P, Manuela R (2020) Collecting built environment information using uavs: Time and applicability in building inspection activities. Sustainability (Basel, Switzerland) 12(4731):4731. https://doi.org/10.3390/su12114731

    Article  Google Scholar 

  22. Chen K, Reichard G, Akanmu A, Xu X (2021) Geo-registering uav-captured close-range images to gis-based spatial model for building façade inspections. Automation in Construction 122. https://doi.org/10.1016/j.autcon.2020.103503

  23. Murtiyoso A, Grussenmeyer P (2017) Documentation of heritage buildings using close-range uav images: dense matching issues, comparison and case studies. Photogram Rec 32(159):206–229. https://doi.org/10.1111/phor.12197

    Article  Google Scholar 

  24. Seo J, Duque L, Wacker J (2018) Drone-enabled bridge inspection methodology and application. Autom Constr 94:112–126. https://doi.org/10.1016/j.autcon.2018.06.006

    Article  Google Scholar 

  25. Markova M, Kravchenko D (2018) 3d photogrammetry application for building inspection of cultural heritage objects. Bulletin of Prydniprovs’ka State Academy of Civil Engineering and Architecture 1, 91–96. https://doi.org/10.30838/j.Bpsacea.2312.170118.82.44

  26. Buffi G, Manciola P, Gambi A, Montanari G (2018) Unmanned aerial vehicle (uav) and building information modelling (bim) technologies in concrete dam management: The case of ridracoli. In bo 9(13):36–43

    Google Scholar 

  27. Gonzalez de Santos LM, Frias Nores E, Martinez Sanchez J, Gonzalez Jorge H (2021) Indoor path-planning algorithm for uav-based contact inspection. Sensors (Basel) 21(2). https://doi.org/10.3390/s21020642

  28. González-deSantos LM, Martínez-Sánchez J, González-Jorge H, Navarro-Medina F, Arias P (2020) Uav payload with collision mitigation for contact inspection. Automation in Construction 115. https://doi.org/10.1016/j.autcon.2020.103200

  29. Murtiyoso A, Koehl M, Grussenmeyer P, Freville T (2017) Acquisition and processing protocols for uav images: 3d modeling of historical buildings using photogrammetry. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W2, 163–170 . https://doi.org/10.5194/isprs-annals-IV-2-W2-163-2017

  30. Pan N-H, Tsai C-H, Chen K-Y, Sung J (2020) Enhancement of external wall decoration material for the building in safety inspection method. J Civ Eng Manag 26(3):216–226

    Article  Google Scholar 

  31. Vacca G, Furfaro G, Dessì A (2018) The use of the uav images for the building 3d model generation. Remote Sensing and Spatial Information Sciences XLII-4/W8, 217–223

  32. Biçici S, Zeybek M (2021) An approach for the automated extraction of road surface distress from a uav-derived point cloud. Automation in Construction 122. https://doi.org/10.1016/j.autcon.2020.103475

  33. Freimuth H, König M (2018) Planning and executing construction inspections with unmanned aerial vehicles. Autom Constr 96:540–553. https://doi.org/10.1016/j.autcon.2018.10.016

    Article  Google Scholar 

  34. Liu D, Xia X, Chen J, Li S (2021) Integrating building information model and augmented reality for drone-based building inspection. Journal of Computing in Civil Engineering 35(2). https://doi.org/10.1061/(asce)cp.1943-5487.0000958

  35. Asadi K, Kalkunte Suresh A, Ender A, Gotad S, Maniyar S, Anand S, Noghabaei M, Han K, Lobaton E, Wu T (2020) An integrated ugv-uav system for construction site data collection. Automation in Construction 112. https://doi.org/10.1016/j.autcon.2019.103068

  36. Nex F, Duarte D, Steenbeek A, Kerle N (2019) Towards real-time building damage mapping with low-cost uav solutions. Remote Sensing 11(3)

  37. Kang D, Cha Y-J (2018) Autonomous uavs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Computer-Aided Civil and Infrastructure Engineering 33(10):885–902. https://doi.org/10.1111/mice.12375

    Article  Google Scholar 

  38. Kucuksubasi F, Sorguc A (2018) Transfer learning-based crack detection by autonomous uavs. In: 35th International Symposium on Automation and Robotics in Construction (ISARC 2018)

  39. Lin S, Kong X, Wang J, Liu A, Fang G, Han Y (2021) Development of a uav path planning approach for multi-building inspection with minimal cost. In: Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 82–93

  40. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory, pp. 39–43. https://doi.org/10.1109/MHS.1995.494215

  41. Zong Woo G, Joong Hoon K, Loganathan GV (2001) A new heuristic optimization algorithm: Harmony search. Simulation (San Diego, Calif.) 76(2), 60–68. https://doi.org/10.1177/003754970107600201

  42. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99. https://doi.org/10.1023/A:1022602019183

    Article  Google Scholar 

  43. Yang X-S Firefly Algorithm, Lévy Flights and Global Optimization, pp. 209–218. Springer, London (2009). https://doi.org/10.1007/978-1-84882-983-1_15

Download references

Funding

No funds or grants were received for this manuscript.

Author information

Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Sydney, NSW, 2007, Australia

    Shiwei Lin, Jianguo Wang & Ang Liu

  2. School of IT and Engineering, Melbourne Institute of Technology, Sydney Campus, Sydney, NSW, 2000, Australia

    Xiaoying Kong

Authors
  1. Shiwei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoying Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this research. Shiwei Lin performed model design, Data collection, experiment design and analysis. The draft of the manuscript was written by Shiwei Lin and Ang Liu and commented on by Xiaoying Kong and Jianguo Wang. All authors approved the final manuscript.

Corresponding author

Correspondence to Shiwei Lin.

Ethics declarations

Conflict of interest

No relevant interests to disclose.

Ethics approval

This research involves simulation, and there is not required ethics approval.

Consent to participate

Not applicable.

Consent for publication

All authors agreed to publish the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, S., Kong, X., Wang, J. et al. Helix-HPSO approach for UAV path planning in a multi-building environment. J Reliable Intell Environ 9, 371–384 (2023). https://doi.org/10.1007/s40860-022-00196-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40860-022-00196-z

Keywords

Search

Navigation