Collision Avoidance for Autonomous Unmanned Aerial Vehicles with Dynamic and Stationary Obstacles
Yıl 2024,
ERKEN GÖRÜNÜM, 1 - 1
Elif Ece Elmas
,
Mustafa Alkan
Öz
The progress of UAV autonomous navigation is steadily progressing at a much faster pace, especially in the field of real-time collision avoidance. In this study, an all-in-one solution proposed that uses a LiDAR data analysis for voxel-based environmental model creation and an optical flow (OF) for predicting moving object trajectories. By fusing LiDAR’s high-resolution spatial data with the relative motion detection capability of the OF, proposed system enables detection and avoidance of both static and dynamic objects on-the-fly. In this regard, based on a holistic perception system, the UAV can adapt to any changes in the environment, and its navigational autonomy can be enhanced. Due to these multi-tiered components, this collision avoidance algorithm is able to efficiently provide safety for UAV operations in a vast number of various conditions. Experiment and simulation results indicate that the system is capable to keep the UAV’s flight path steady through various situations where low-light or high-speed components are involved.
Kaynakça
- [1] Ayamga M, Akaba S, Nyaaba AA., " Multifaceted applicability of drones: A review ”, Technological Forecasting and Social Change ,167, (2021).
- [2] Guo K., Tang P., Wang H., et al “Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control” Aerospace 9:, (2022). https://doi.org/10.3390/aerospace9010034.
- [3] Wang X, Yadav V, Balakrishnan S.N ,“Cooperative UAV formation flying with obstacle/collision avoidance”, IEEE Transactions on Control Systems Technology, 15, (2007). https://doi.org/10.1109/TCST.2007.899191.
- [4] Jenie YI, van Kampen E-J, Remes B., “Cooperative Autonomous Collision Avoidance System for Unmanned Aerial Vehicle”, Advances in Aerospace Guidance, Navigation and Control, (2013).
- [5] Tan CY, Huang S., Tan KK, Teo RSH (2020) “Three Dimensional Collision Avoidance for Multi Unmanned Aerial Vehicles Using Velocity Obstacle”, Journal of Intelligent and Robotic Systems: Theory and Applications, 97. https://doi.org/10.1007/s10846-019-01055-5.
- [6] Torens, C., Nikodem, F., Dauer, J.C.et al. “Geofencing requirements for onboard safe operation monitoring”, CEAS Aeronaut J 11, 767–779 (2020). https://doi.org/10.1007/s13272-020-00451-0.
- [7] Kim J., Atkins E., “Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems”, Applied Sciences (Switzerland) 12, (2022). https://doi.org/10.3390/app12020576.
- [8] Elmas, E. E., & Alkan, M., “Bir İnsansız Hava Aracı Sisteminin Tasarımı, Benzetimi ve Gerçekleştirilmesi”, Politeknik Dergisi, 26(2), 929-940, (2023). https://doi.org/10.2339/politeknik.1037319.
- [9] Günay, M. B., & Korkut, İ., “Dron için Hareketli Kol Tasarımında Sistematik İnovasyon Geliştirme”, Politeknik Dergisi 1-1, (2023).. https://doi.org/10.2339/politeknik.1202113.
- [10] Canpolat Tosun, D., “Bir Quadrotorun Yörünge Takibinde Doğrusal Ve Doğrusal Olmayan Kontrol Yöntemlerinin Performans Değerlendirmesi”, Politeknik Dergisi 1-1, (2024). https://doi.org/10.2339/politeknik.1219648.
- [11] Wu, X., Li, W., Hong, D., Tao, R., & Du, Q., “Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey” , IEEE Geoscience and Remote Sensing Magazine, 10, 91-124, (2021). https://doi.org/10.1109/MGRS.2021.3115137.
- [12] Teixeira, K., Miguel, G., Silva, H. S., & Madeiro, F., “A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning”, IEEE Access, 11, 117582-117621,(2023).https://doi.org/10.1109/ACCESS.2023.3326101.
- [13] Huang, C., Lan, Y., Liu, Y., Zhou, W., Pei, H., Yang, L., Cheng, Y., Hao, Y., & Peng, Y., “A new dynamic path planning approach for unmanned aerial vehicles”, Complexity, 8420294, 17 pages, (2018). https://doi.org/10.1155/2018/8420294.
- [14] Chen, X., Zhao, M. & Yin, L., “Dynamic Path Planning of the UAV Avoiding Static and Moving Obstacles”, J Intell Robot Syst 99, 909–931 (2020). https://doi.org/10.1007/s10846-020-01151-x.
- [15] Christiansen MP, Laursen MS, Jørgensen RN, et al., “Designing and testing a UAV mapping system for agricultural field surveying”, Sensors (Switzerland) 17: (2017). https://doi.org/10.3390/s17122703.
- [16] Youn W, Ko H, Choi H, et al., “Collision-free Autonomous Navigation of A Small UAV Using Low-cost Sensors in GPS-denied Environments”, Int J Control Autom Syst 19: (2021) https://doi.org/10.1007/s12555-019-0797-7.
- [17] Droeschel D, Schwarz M, Behnke S., “Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner”,Rob Auton Syst 88: (2017). https://doi.org/10.1016/j.robot.2016.10.017.
- [18] Qian J, Chen K, Chen Q, et al.,“Robust Visual-LiDAR Simultaneous Localization and Mapping System for UAV”, IEEE Geoscience and Remote Sensing Letters 19: (2022). https://doi.org/10.1109/LGRS.2021.3099166.
- [19] Park J., Cho N., “Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment”, Remote Sensing, 12(6):975, 2020;. https://doi.org/10.3390/rs12060975.
- [20] Ramasamy, S., Sabatini, R., Gardi, A., & Liu, J. , “LiDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid”, Aerospace Science and Technology, 55, Article number, (2016). https://doi.org/10.1016/j.ast.2016.05.020.
- [21] Petrlik M., Krajnik T., Saska M., “LiDAR-based Stabilization, Navigation and Localization for UAVs Operating in Dark Indoor Environments”, In: 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021.
- [22] Behroozpour B., Sandborn PAM, Wu MC, Boser BE. , “LiDAR System Architectures and Circuits”, IEEE Communications Magazine 55: (2017). https://doi.org/10.1109/MCOM.2017.1700030.
- [23] Alonso-Mora J., Naegeli T., Siegwart R., Beardsley P., “Collision avoidance for aerial vehicles in multi-agent scenarios”, Auton Robots 39:. (2015) https://doi.org/10.1007/s10514-015-9429-0.
- [24] Hrabar S., Sukhatme GS., Corke P., et al., “Combined optic-flow and stereo-based navigation of urban canyons for a UAV”, In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, (2005).
- [25] Deng H., Arif U., Yang K., et al., “Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments”, Optik (Stuttg) 219: (2020). https://doi.org/10.1016/j.ijleo.2020.164923.
- [26] Gandhi D., Pinto L., Gupta A., “Learning to fly by crashing”, In: IEEE International Conference on Intelligent Robots and Systems, (2017).
- [27] Kalidas AP, Joshua CJ, Md AQ, et al., “Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles”, Drones,7:(2023). https://doi.org/10.3390/drones7040245
- [28] Cho G., Kim J, Oh H., “Vision-based obstacle avoidance strategies for MAVs using optical flows in 3-D textured environments”, Sensors (Switzerland) 19: (2019). https://doi.org/10.3390/s19112523.
- [29] Florence P., Carter J., Tedrake R., “Integrated Perception and Control at High Speed: Evaluating Collision Avoidance Manoeuvres Without Maps”, In: Springer Proceedings in Advanced Robotics, (2020).
- [30] Mokri, S. S., Ibrahim, N., Hussain, A., & Mustafa, M. M., “Motion detection using Horn Schunck algorithm and implementation”, In 2009 International Conference on Electrical Engineering and Informatics (Vol. 1, pp. 83-87). IEEE, (2009). doi: 10.1109/ICEEI.2009.5254812.
Otonom İnsansız Hava Araçları için Dinamik ve Sabit Engellerle Çarpışmayı Önleme
Yıl 2024,
ERKEN GÖRÜNÜM, 1 - 1
Elif Ece Elmas
,
Mustafa Alkan
Öz
İHA otonom navigasyonunun ilerlemesi, özellikle gerçek zamanlı çarpışma önleme alanında, hızlı ve istikrarlı olarak ilerlemektedir. Bu çalışmada, voksel tabanlı çevresel model oluşturmak için LiDAR veri analizini ve hareketli nesne yörüngelerini tahmin etmek için optik akışı (OF) kullanan bir çözüm önerildi. Önerilen sistem ,LiDAR'ın yüksek çözünürlüklü mekansal verilerini OF'nin göreceli hareket algılama yeteneğiyle birleştirerek, hareket halindeyken hem statik hem de dinamik nesnelerin algılanmasını ve bunlardan kaçınılmasını sağlar. Bu sayede bütünsel bir algılama sistemi esas alınarak İHA'nın ortamdaki her türlü değişime uyum sağlayabilmesi ve seyir otonomisinin artırılması sağlanabilecektir.Çok katmanlı bileşenler sayesinde, önerilen çarpışma önleme algoritması, çok sayıda farklı koşulda İHA operasyonları için etkili bir şekilde güvenlik sağlayabilmektedir. Deney ve simülasyon sonuçları, sistemin az-ışıklı veya yüksek hızlı bileşenlerin dahil olduğu durumlarda İHA'nın uçuş yolunu sabit tutabildiğini göstermektedir.
Kaynakça
- [1] Ayamga M, Akaba S, Nyaaba AA., " Multifaceted applicability of drones: A review ”, Technological Forecasting and Social Change ,167, (2021).
- [2] Guo K., Tang P., Wang H., et al “Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control” Aerospace 9:, (2022). https://doi.org/10.3390/aerospace9010034.
- [3] Wang X, Yadav V, Balakrishnan S.N ,“Cooperative UAV formation flying with obstacle/collision avoidance”, IEEE Transactions on Control Systems Technology, 15, (2007). https://doi.org/10.1109/TCST.2007.899191.
- [4] Jenie YI, van Kampen E-J, Remes B., “Cooperative Autonomous Collision Avoidance System for Unmanned Aerial Vehicle”, Advances in Aerospace Guidance, Navigation and Control, (2013).
- [5] Tan CY, Huang S., Tan KK, Teo RSH (2020) “Three Dimensional Collision Avoidance for Multi Unmanned Aerial Vehicles Using Velocity Obstacle”, Journal of Intelligent and Robotic Systems: Theory and Applications, 97. https://doi.org/10.1007/s10846-019-01055-5.
- [6] Torens, C., Nikodem, F., Dauer, J.C.et al. “Geofencing requirements for onboard safe operation monitoring”, CEAS Aeronaut J 11, 767–779 (2020). https://doi.org/10.1007/s13272-020-00451-0.
- [7] Kim J., Atkins E., “Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems”, Applied Sciences (Switzerland) 12, (2022). https://doi.org/10.3390/app12020576.
- [8] Elmas, E. E., & Alkan, M., “Bir İnsansız Hava Aracı Sisteminin Tasarımı, Benzetimi ve Gerçekleştirilmesi”, Politeknik Dergisi, 26(2), 929-940, (2023). https://doi.org/10.2339/politeknik.1037319.
- [9] Günay, M. B., & Korkut, İ., “Dron için Hareketli Kol Tasarımında Sistematik İnovasyon Geliştirme”, Politeknik Dergisi 1-1, (2023).. https://doi.org/10.2339/politeknik.1202113.
- [10] Canpolat Tosun, D., “Bir Quadrotorun Yörünge Takibinde Doğrusal Ve Doğrusal Olmayan Kontrol Yöntemlerinin Performans Değerlendirmesi”, Politeknik Dergisi 1-1, (2024). https://doi.org/10.2339/politeknik.1219648.
- [11] Wu, X., Li, W., Hong, D., Tao, R., & Du, Q., “Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey” , IEEE Geoscience and Remote Sensing Magazine, 10, 91-124, (2021). https://doi.org/10.1109/MGRS.2021.3115137.
- [12] Teixeira, K., Miguel, G., Silva, H. S., & Madeiro, F., “A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning”, IEEE Access, 11, 117582-117621,(2023).https://doi.org/10.1109/ACCESS.2023.3326101.
- [13] Huang, C., Lan, Y., Liu, Y., Zhou, W., Pei, H., Yang, L., Cheng, Y., Hao, Y., & Peng, Y., “A new dynamic path planning approach for unmanned aerial vehicles”, Complexity, 8420294, 17 pages, (2018). https://doi.org/10.1155/2018/8420294.
- [14] Chen, X., Zhao, M. & Yin, L., “Dynamic Path Planning of the UAV Avoiding Static and Moving Obstacles”, J Intell Robot Syst 99, 909–931 (2020). https://doi.org/10.1007/s10846-020-01151-x.
- [15] Christiansen MP, Laursen MS, Jørgensen RN, et al., “Designing and testing a UAV mapping system for agricultural field surveying”, Sensors (Switzerland) 17: (2017). https://doi.org/10.3390/s17122703.
- [16] Youn W, Ko H, Choi H, et al., “Collision-free Autonomous Navigation of A Small UAV Using Low-cost Sensors in GPS-denied Environments”, Int J Control Autom Syst 19: (2021) https://doi.org/10.1007/s12555-019-0797-7.
- [17] Droeschel D, Schwarz M, Behnke S., “Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner”,Rob Auton Syst 88: (2017). https://doi.org/10.1016/j.robot.2016.10.017.
- [18] Qian J, Chen K, Chen Q, et al.,“Robust Visual-LiDAR Simultaneous Localization and Mapping System for UAV”, IEEE Geoscience and Remote Sensing Letters 19: (2022). https://doi.org/10.1109/LGRS.2021.3099166.
- [19] Park J., Cho N., “Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment”, Remote Sensing, 12(6):975, 2020;. https://doi.org/10.3390/rs12060975.
- [20] Ramasamy, S., Sabatini, R., Gardi, A., & Liu, J. , “LiDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid”, Aerospace Science and Technology, 55, Article number, (2016). https://doi.org/10.1016/j.ast.2016.05.020.
- [21] Petrlik M., Krajnik T., Saska M., “LiDAR-based Stabilization, Navigation and Localization for UAVs Operating in Dark Indoor Environments”, In: 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021.
- [22] Behroozpour B., Sandborn PAM, Wu MC, Boser BE. , “LiDAR System Architectures and Circuits”, IEEE Communications Magazine 55: (2017). https://doi.org/10.1109/MCOM.2017.1700030.
- [23] Alonso-Mora J., Naegeli T., Siegwart R., Beardsley P., “Collision avoidance for aerial vehicles in multi-agent scenarios”, Auton Robots 39:. (2015) https://doi.org/10.1007/s10514-015-9429-0.
- [24] Hrabar S., Sukhatme GS., Corke P., et al., “Combined optic-flow and stereo-based navigation of urban canyons for a UAV”, In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, (2005).
- [25] Deng H., Arif U., Yang K., et al., “Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments”, Optik (Stuttg) 219: (2020). https://doi.org/10.1016/j.ijleo.2020.164923.
- [26] Gandhi D., Pinto L., Gupta A., “Learning to fly by crashing”, In: IEEE International Conference on Intelligent Robots and Systems, (2017).
- [27] Kalidas AP, Joshua CJ, Md AQ, et al., “Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles”, Drones,7:(2023). https://doi.org/10.3390/drones7040245
- [28] Cho G., Kim J, Oh H., “Vision-based obstacle avoidance strategies for MAVs using optical flows in 3-D textured environments”, Sensors (Switzerland) 19: (2019). https://doi.org/10.3390/s19112523.
- [29] Florence P., Carter J., Tedrake R., “Integrated Perception and Control at High Speed: Evaluating Collision Avoidance Manoeuvres Without Maps”, In: Springer Proceedings in Advanced Robotics, (2020).
- [30] Mokri, S. S., Ibrahim, N., Hussain, A., & Mustafa, M. M., “Motion detection using Horn Schunck algorithm and implementation”, In 2009 International Conference on Electrical Engineering and Informatics (Vol. 1, pp. 83-87). IEEE, (2009). doi: 10.1109/ICEEI.2009.5254812.