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An Efficient Path Planning Algorithm for 2D Ground Area Coverage Using Multi-UAV

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Abstract

Unmanned aerial vehicle (UAV) equipped with visual sensors are extensively used in area coverage applications. As a UAV would only cover a fraction of the region of interest, the entire region needs to be covered by several UAVs where each UAV accomplishes its own tasks. For the covering of the target region, a working method consisting of three levels has been developed. The initial step employs the Voronoi partition technique to create a number of convex sub-polygonal areas inside the target area. In the second level, each sub-polygonal area is partitioned to provide a near-optimal collection of waypoints. At the third and final level, we find a path that visits each of the waypoints without colliding with anything and is as short as feasible. Collision due to both static and dynamic obstacles is also considered for avoidance. The first and second-level partitioning processes are carried out offline, whereas path planning is handled in real-time. Traditional methods like Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Cuckoo Optimisation Algorithm (COA) are used to evaluate the proposed work. The evaluated result of the proposed work is compared with the existing work on area coverage in terms of the percentage of inside and outside area coverage. Also, the performance of the proposed dynamic path planning method is compared with TSP and the Improved Follow the Gap Method (FGM-I). The outcome demonstrates that the proposed work is far more effective than the existing result and is suitable for the application in issue, which involves area coverage with the presence of obstacles.

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References

  1. Gurumoorthy, K. B., Allwin Devaraj, S., Gopinath, S., & Tanweer, A. (2021). A novel clustering method for fault recovery and routing in mobile ad-hoc networks. International Journal of Communication Systems, 34(6), e4937.

    Google Scholar 

  2. Senapati, B. R., Khilar, P. M., & Swain, R. R. (2020). Fire controlling under uncertainty in urban region using smart vehicular ad hoc network. Wireless Personal Communications, 116, 2049–2069.

    Article  Google Scholar 

  3. Senapati, B. R., Khilar, P. M., Dash, T., & Swain, R. R. (2023). Ai-assisted emergency healthcare using vehicular network and support vector machine. Wireless Personal Communications, 130(3), 1929–1962.

    Article  Google Scholar 

  4. Senapati, B. R., Swain, S., Swain, R. R., & Khilar, P. M. (2023). A heterogeneous fault diagnosis approach to enhance performance of connected vehicles. International Journal of Communication Systems, 36(4), e5414.

    Article  Google Scholar 

  5. Chriki, Amira, Touati, Haifa, Snoussi, Hichem, & Kamoun, Farouk. (2019). Fanet: Communication, mobility models and security issues. Computer Networks, 163, 106877.

    Article  Google Scholar 

  6. Srivastava, Ashish, & Prakash, Jay. (2021). Future fanet with application and enabling techniques: Anatomization and sustainability issues. Computer Science Review, 39, 100359.

    Article  MathSciNet  Google Scholar 

  7. Zhang, J., Chen, T., Zhong, S., Wang, J., Zhang, W., Zuo, X., Maunder, R. G., & Hanzo, L. (2019). Aeronautical adhoc networking for the internet-above-the-clouds. Proceedings of the IEEE, 107(5), 868–911.

    Article  Google Scholar 

  8. Swain, S., Senapati, B.R., Khilar, P.M. Evolution of vehicular ad hoc network and flying ad hoc network for real-life applications: Role of vanet and fanet. In Modelling and Simulation of Fast-Moving Ad-Hoc Networks (FANETs and VANETs), pages 43–73. IGI Global, 2023

  9. Sharma, Vishal, You, Ilsun, Kumar, Rajesh, & Chauhan, Varun. (2018). Offrp: Optimised fruit fly based routing protocol with congestion control for uavs guided ad hoc networks. International Journal of Ad Hoc and Ubiquitous Computing, 27(4), 233–255.

    Article  Google Scholar 

  10. Sharma, Vishal, & Kumar, Rajesh. (2019). Uavs assisted queue scheduling in ground ad hoc networks. International Journal of Ad Hoc and Ubiquitous Computing, 30(1), 1–10.

    Article  Google Scholar 

  11. Hassanalian, Mostafa, & Abdelkefi, Abdessattar. (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91, 99–131.

    Article  Google Scholar 

  12. Macrina, Giusy, Di Puglia, Luigi, Pugliese, Francesca Guerriero, & Laporte, Gilbert. (2020). Drone-aided routing: A literature review. Transportation Research Part C: Emerging Technologies, 120, 102762.

    Article  Google Scholar 

  13. Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019). Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges. Ieee Access, 7, 48572–48634.

    Article  Google Scholar 

  14. Swain, S., Khilar, P. M., & Senapati, B. R. (2023). A reinforcement learning-based cluster routing scheme with dynamic path planning for mutli-uav network. Vehicular Communications, 41, 100605.

    Article  Google Scholar 

  15. Swain, S., Khilar, P. M., & Senapati, B. R. (2022). An effective data routing for dynamic area coverage using multidrone network. Transactions on Emerging Telecommunications Technologies, 33(9), e4532.

    Article  Google Scholar 

  16. Galceran, Enric, & Carreras, Marc. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous systems, 61(12), 1258–1276.

    Article  Google Scholar 

  17. Frank Lingelbach. Path planning using probabilistic cell decomposition. In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, volume 1, pages 467–472. IEEE, 2004.

  18. Acar, E. U., Choset, H., Rizzi, A. A., Atkar, P. N., & Hull, D. (2002). Morse decompositions for coverage tasks. The international journal of robotics research, 21(4), 331–344.

    Article  Google Scholar 

  19. Sylvia C Wong and Bruce A MacDonald. A topological coverage algorithm for mobile robots. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453), volume 2, pages 1685–1690. IEEE, 2003.

  20. Zack J Butler, Alfred A Rizzi, and Ralph L Hollis. Contact sensor-based coverage of rectilinear environments. In Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No. 99CH37014), pages 266–271. IEEE, 1999.

  21. Ling Xu. Graph planning for environmental coverage. 2011.

  22. Vikas Shivashankar, Rajiv Jain, Ugur Kuter, and Dana Nau. Real-time planning for covering an initially-unknown spatial environment. In Twenty-Fourth International FLAIRS Conference, 2011.

  23. Ghaddar, Alia, Merei, Ahmad, & Natalizio, Enrico. (2020). Pps: Energy-aware grid-based coverage path planning for uavs using area partitioning in the presence of nfzs. Sensors, 20(13), 3742.

    Article  Google Scholar 

  24. Avellar, G. S. C., Pereira, G. A. S., Pimenta, L. C. A., & Iscold, P. (2015). Multi-uav routing for area coverage and remote sensing with minimum time. Sensors, 15(11), 27783–27803.

    Article  Google Scholar 

  25. J Valente, A Barrientos, J del Cerro, C Rossi, J Colorado, D Sanz, M Garzón. Multi-robot visual coverage path planning: Geometrical metamorphosis of the workspace through raster graphics based approaches. In International Conference on Computational Science and Its Applications, pages 58–73. Springer, 2011.

  26. Sina Sharif Mansouri, George Georgoulas, Thomas Gustafsson, and George Nikolakopoulos. On the covering of a polygonal region with fixed size rectangles with an application towards aerial inspection. In 2017 25th Mediterranean Conference on Control and Automation (MED), pages 1219–1224. IEEE, 2017.

  27. Mansouri, S. S., Kanellakis, C., Georgoulas, G., Kominiak, D., Gustafsson, T., & Nikolakopoulos, G. (2018). 2D visual area coverage and path planning coupled with camera footprints. Control Engineering Practice, 75, 1–16.

    Article  Google Scholar 

  28. Lewis, R. M., Torczon, V., & Trosset, M. W. (2000). Direct search methods: then and now. Journal of computational and Applied Mathematics, 124(1–2), 191–207.

    Article  MathSciNet  MATH  Google Scholar 

  29. Randy L Haupt and Sue Ellen Haupt. Practical genetic algorithms. 2004.

  30. Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. UK: Elsevier.

    Google Scholar 

  31. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied soft computing, 11(8), 5508–5518.

    Article  Google Scholar 

  32. Le, A. V., Arunmozhi, M., Veerajagadheswar, P., Ku, P. C., Minh, T. H. Q., Sivanantham, V., & Mohan, R. E. (2018). Complete path planning for a tetris-inspired self-reconfigurable robot by the genetic algorithm of the traveling salesman problem. Electronics, 7(12), 344.

    Article  Google Scholar 

  33. Brown, T., Wang, Z., Shan, T., Wang, F., Xue, J., On wireless video sensor network deployment for 3d indoor space coverage. In SoutheastCon 2016, pages 1–8. IEEE, 2016.

  34. H Pham, SA Smolka, SD Stoller, D Phan, and J Yang. A survey on unmanned aerial vehicle collision avoidance systems. arxiv. arXiv preprint arXiv:1508.07723, 2015.

  35. Yasin, J. N., Mohamed, S. A. S., Haghbayan, M. H., Sherif, A. S., Jawad, N., Heikkonen, J., Tenhunen, H., & Plosila, J. (2020). Collision avoidance systems and approaches. Unmanned aerial vehicles (uavs). IEEE Access, 8, 105139–105155.

    Article  Google Scholar 

  36. VJ Lumelsky, AA Stepanov. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. In Autonomous robot vehicles, pages 363–390. Springer, 1990.

  37. Min Gyu Park, Jae Hyun Jeon, and Min Cheol Lee. Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570), volume 3, pages 1530–1535. IEEE, 2001.

  38. Kobayashi, Masato, & Motoi, Naoki. (2022). Local path planning: Dynamic window approach with virtual manipulators considering dynamic obstacles. IEEE Access, 10, 17018–17029.

    Article  Google Scholar 

  39. Sezer, Volkan, & Gokasan, Metin. (2012). A novel obstacle avoidance algorithm:"follow the gap method". Robotics and Autonomous Systems, 60(9), 1123–1134.

    Article  Google Scholar 

  40. Özdemir, Aykut, & Sezer, Volkan. (2018). Follow the gap with dynamic window approach. International Journal of Semantic Computing, 12(01), 43–57.

    Article  Google Scholar 

  41. Mustafa Demir and Volkan Sezer. Improved follow the gap method for obstacle avoidance. In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pages 1435–1440. IEEE, 2017.

  42. Chakravarthy, Animesh, & Ghose, Debasish. (1998). Obstacle avoidance in a dynamic environment: A collision cone approach. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 28(5), 562–574.

    Article  Google Scholar 

  43. Jennifer Goss, Rahul Rajvanshi, and Kamesh Subbarao. Aircraft conflict detection and resolution using mixed geometric and collision cone approaches. In AIAA Guidance, Navigation, and Control Conference and Exhibit, page 4879, 2004.

  44. Jun Li, Yifeng Zhou, and Louise Lamont. Communication architectures and protocols for networking unmanned aerial vehicles. In 2013 IEEE Globecom Workshops (GC Wkshps), pages 1415–1420. IEEE, 2013.

  45. Jiang, Jinfang, & Han, Guangjie. (2018). Routing protocols for unmanned aerial vehicles. IEEE Communications Magazine, 56(1), 58–63.

    Article  Google Scholar 

  46. Meng, W., Bo, P., Zhang, X., Hong, J., Xin, S., & Tu, C. (2023). An efficient algorithm for approximate voronoi diagram construction on triangulated surfaces. Computational Visual Media, 9(3), 443–459.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India

    Sipra Swain & Pabitra Mohan Khilar

  2. Department of Computer Science and Engineering, ITER, SOA (Deemed to be) University, Bhubaneswar, India

    Biswa Ranjan Senapati

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  1. Sipra Swain
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  2. Pabitra Mohan Khilar
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  3. Biswa Ranjan Senapati
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Contributions

All authors contributed to the study conception and design. Material preparation and analysis were performed by [SS], [BRS] and [PMK]. The first draft of the manuscript was written by [SS] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sipra Swain.

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Swain, S., Khilar, P.M. & Senapati, B.R. An Efficient Path Planning Algorithm for 2D Ground Area Coverage Using Multi-UAV. Wireless Pers Commun 132, 361–407 (2023). https://doi.org/10.1007/s11277-023-10614-x

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