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Enhancing UAV navigation with dynamic programming and hybrid probabilistic route mapping: an improved dynamic window approach

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Abstract

The Dynamic Window Approach (DWA) is a popular method for Unmanned Aerial Vehicle (UAV) navigation and localization in unknown environments. It combines Dynamic Programming (DP) with a Probabilistic Route Mapping (PRM) algorithm to provide efficient path planning and obstacle avoidance. DWA can handle a wide range of obstacles, including dynamic and uncertain ones, making it highly reliable. The approach utilizes dynamic programming to compute the optimal path based on the UAV's current state and the known environment. It also employs a hybrid probabilistic route mapping algorithm to estimate the location and movement of unknown obstacles. By combining these techniques, DWA enables the UAV to navigate through complex environments efficiently. One of DWA's key strengths is its ability to handle non-holonomic constraints, such as the limited turning radius of a mobile UAV. It achieves this by defining a dynamic window that determines the feasible set of motions for the UAV at any given time and adjusts the path accordingly. Compared to other popular methods like the Rapidly Exploring Random Trees (RRT) algorithm, DWA outperforms in terms of path planning and obstacle avoidance. It overcomes the limitations imposed by the size of autonomous mobile UAVs by considering the relationship between the robot's dimensions and obstacles in the open space. To enhance sensing and prediction of the surroundings, a laser range finder is utilized in DWA, particularly to handle curved structures or box-canyon formations. This, along with the Dynamic Programming (DP) algorithm, optimizes the path by considering the gathered information. The proposed approach addresses the local minima problem through a strategy to identify the effective path region. Theoretical studies and simulations demonstrate the efficiency and superiority of DWA. In summary, the Dynamic Window Approach is an efficient method for UAV navigation and localization in unknown environments. By combining dynamic programming, probabilistic route mapping, and considering non-holonomic constraints, it provides reliable path planning and obstacle avoidance. Its ability to handle various obstacles, including dynamic ones, sets it apart from other methods, making it highly valuable for UAV applications.

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Using the DWA and IDWA approaches, significant differences in route lengths and times are discovered. Potential complexity is indicated by Scenario III, which has a DWA route length of 544 cm and a duration of 68 s. On the other hand, Scenario II, with a DWA route length of 486 cm and a period of 46 s, shows better efficiency. Shorter path lengths and times are consistently produced by IDWA; Scenario II has the best results, measuring 430 cm and 37 s.

References

  1. Wang X, Gursoy MC (2022) Learning-based UAV trajectory optimization with collision avoidance and connectivity constraints. IEEE Trans Wirel Commun 21(6):4350–4363. https://doi.org/10.1109/TWC.2021.3129226

    Article  Google Scholar 

  2. Wang X, Gursoy MC, Erpek T, Sagduyu YE (2022) Learning-based UAV path planning for data collection with integrated collision avoidance. IEEE Internet Things J 9(17):16663–16676. https://doi.org/10.1109/JIOT.2022.3153585

    Article  Google Scholar 

  3. Li T, Liu W, Zeng Z, Xiong NN (2022) DRLR: a deep-reinforcement-learning-based recruitment scheme for massive data collections in 6G-based IoT networks. IEEE Internet of Things J 9(16):14595–14609. https://doi.org/10.1109/JIOT.2021.3067904

    Article  Google Scholar 

  4. Wang W, Wang L, Wu J, Tao X, Wu H (2022) Oracle-guided deep reinforcement learning for large-scale multi-UAVs flocking and navigation. IEEE Trans Veh Technol 71(10):10280–10292. https://doi.org/10.1109/TVT.2022.3184043

    Article  Google Scholar 

  5. Wang L, Wang K, Pan C, Xu W, Aslam N, Hanzo L (2021) Multi-agent deep reinforcement learning-based trajectory planning for multi-UAV assisted mobile edge computing. IEEE Trans Cogn Commun Network 7(1):73–84. https://doi.org/10.1109/TCCN.2020.3027695

    Article  Google Scholar 

  6. Zhao C, Liu J, Sheng M, Teng W, Zheng Y, Li J (2021) Multi-UAV trajectory planning for energy-efficient content coverage: a decentralized learning-based approach. IEEE J Sel Areas Commun 39(10):3193–3207. https://doi.org/10.1109/JSAC.2021.3088669

    Article  Google Scholar 

  7. Khodaparast SS, Lu X, Wang P, Nguyen UT (2021) Deep reinforcement learning based energy efficient multi-UAV data collection for IoT networks. IEEE Open J Veh Technol 2:249–260. https://doi.org/10.1109/OJVT.2021.3085421

    Article  Google Scholar 

  8. Hsu Y-H, Gau R-H (2022) Reinforcement learning-based collision avoidance and optimal trajectory planning in UAV communication networks. IEEE Trans Mob Comput 21(1):306–320. https://doi.org/10.1109/TMC.2020.3003639

    Article  Google Scholar 

  9. Mei C, Fan Z, Zhu Q, Yang P, Hou Z, Jin H (2023) A Novel scene matching navigation system for UAVs based on vision/inertial fusion. IEEE Sens J 23(6):6192–6203. https://doi.org/10.1109/JSEN.2023.3241330

    Article  ADS  Google Scholar 

  10. Khalife J, Kassas ZM (2022) On the achievability of submeter-accurate UAV navigation with cellular signals exploiting loose network synchronization. IEEE Trans Aerosp Electron Syst 58(5):4261–4278. https://doi.org/10.1109/TAES.2022.3162770

    Article  ADS  Google Scholar 

  11. Or B, Klein I (2022) Adaptive step size learning with applications to velocity aided inertial navigation system. IEEE Access 10:85818–85830. https://doi.org/10.1109/ACCESS.2022.3198672

    Article  Google Scholar 

  12. Savkin AV, Huang H (2022) Navigation of a UAV network for optimal surveillance of a group of ground targets moving along a road. IEEE Trans Intell Transp Syst 23(7):9281–9285. https://doi.org/10.1109/TITS.2021.3077880

    Article  Google Scholar 

  13. Yang Y, Liu X, Zhang W, Liu X, Guo Y (2022) Multilayer low-cost sensor local-global filtering fusion integrated navigation of small UAV. IEEE Sens J 22(18):17550–17564. https://doi.org/10.1109/JSEN.2021.3091687

    Article  ADS  Google Scholar 

  14. Chen H, Xian-Bo W, Liu J, Wang J, Ye W (2020) Collaborative multiple UAVs navigation with GPS/INS/UWB jammers using sigma point belief propagation. IEEE Access 8:193695–193707. https://doi.org/10.1109/ACCESS.2020.3031605

    Article  Google Scholar 

  15. Huang H, Savkin AV, Huang C (2021) Decentralized autonomous navigation of a UAV network for road traffic monitoring. IEEE Trans Aerosp Electron Syst 57(4):2558–2564. https://doi.org/10.1109/TAES.2021.3053115

    Article  ADS  Google Scholar 

  16. Savkin AV, Huang H (2020) Navigation of a network of aerial drones for monitoring a frontier of a moving environmental disaster area. IEEE Syst J 14(4):4746–4749. https://doi.org/10.1109/JSYST.2020.2966779

    Article  ADS  Google Scholar 

  17. Lindqvist B, Mansouri SS, Haluška J, Nikolakopoulos G (2022) Reactive navigation of an unmanned aerial vehicle with perception-based obstacle avoidance constraints. IEEE Trans Control Syst Technol 30(5):1847–1862. https://doi.org/10.1109/TCST.2021.3124820

    Article  Google Scholar 

  18. Chen S, Chen H, Chang C-W, Wen C-Y (2021) Multilayer mapping kit for autonomous UAV navigation. IEEE Access 9:31493–31503. https://doi.org/10.1109/ACCESS.2021.3055066

    Article  Google Scholar 

  19. Tuan HD, Nasir AA, Savkin AV, Poor HV, Dutkiewicz E (2021) MPC-based UAV navigation for simultaneous solar-energy harvesting and two-way communications. IEEE J Sel Areas Commun 39(11):3459–3474. https://doi.org/10.1109/JSAC.2021.3088633

    Article  Google Scholar 

  20. Rezwan S, Choi W (2022) Artificial intelligence approaches for UAV navigation: recent advances and future challenges. IEEE Access 10:26320–26339. https://doi.org/10.1109/ACCESS.2022.3157626

    Article  Google Scholar 

  21. Cohen MR, Forbes JR (2020) Navigation and control of unconventional VTOL UAVs in forward-flight with explicit wind velocity estimation. IEEE Robot Autom Lett 5(2):1151–1158. https://doi.org/10.1109/LRA.2020.2966406

    Article  Google Scholar 

  22. Yang H, Li X, Guo Y, Jia L (2022) Discretization–filtering–reconstruction: railway detection in images for navigation of inspection UAV. IEEE Trans Instrum Measure 71:1–13. https://doi.org/10.1109/TIM.2022.3220295

    Article  Google Scholar 

  23. Sivarambabu PV, Malarvezhi P, Dayana R, Vadivukkarasi K (2021) EEHC approach for latency minimization in 3D network architecture using 5G+with UAVs. Wirel Person Commun. https://doi.org/10.1007/s11277-021-08931-0

    Article  Google Scholar 

  24. Venkatasivarambabu P, Agrawal R (2023) A review on UAV path planning optimization based on motion planning algorithms: collision avoidance and challenges. In: 2023 8th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. 1483–1488. https://doi.org/10.1109/ICCES57224.2023.10192737

  25. Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. In: IEEE robotics & automation magazine, vol 4, no 1. pp 23–33. https://doi.org/10.1109/100.580977

  26. Missura M, Bennewitz M (2019) Predictive Collision Avoidance for the Dynamic Window Approach. International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 8620–8626, https://doi.org/10.1109/ICRA.2019.8794386

  27. Khalife J, Kassas ZM (2020) Opportunistic UAV navigation with carrier phase measurements from asynchronous cellular signals. IEEE Trans Aerosp Electron Syst 56(4):3285–3301. https://doi.org/10.1109/TAES.2019.2948452

    Article  ADS  Google Scholar 

  28. SherlinShobitha G, Prabhakar B (2023) Energy aware African buffalo-based optimized dynamic media access control protocol for mobile Adhoc network environment. Int J Inform Technol. https://doi.org/10.1007/s41870-023-01372-x

    Article  Google Scholar 

  29. Ashwin SH, Naveen Raj R (2023) Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance. Int J Inf Tecnol. https://doi.org/10.1007/s41870-023-01412-6

    Article  Google Scholar 

  30. Jaiswal P, Bhirud S (2023) A cropping algorithm for automatically extracting regions of ınterest from panoramic radiographs based on maxilla and mandible parts. Int J Inf Tecnol. https://doi.org/10.1007/s41870-023-01406-4

    Article  Google Scholar 

  31. Kumar D, Yadav D, Yadav DS (2023) Spatial indexing and searching using parallel wavelet tree. Int J Inf Tecnol. https://doi.org/10.1007/s41870-023-01422-4

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India

    Pamarthi Venkatasivarambabu & Richa Agrawal

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  1. Pamarthi Venkatasivarambabu
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Correspondence to Pamarthi Venkatasivarambabu.

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Venkatasivarambabu, P., Agrawal, R. Enhancing UAV navigation with dynamic programming and hybrid probabilistic route mapping: an improved dynamic window approach. Int. j. inf. tecnol. 16, 1023–1032 (2024). https://doi.org/10.1007/s41870-023-01671-3

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