Sensor Fusion and Conflict Resolution Strategies for Safe Multi-UAV Operations: A Comprehensive Review
Article Sidebar
Main Article Content
Abstract: The widespread adoption of unmanned aerial vehicle (UAV) across diverse sectors has increased the risk of collisions and heightened the urgency for robust collision avoidance mechanisms, especially in multi-UAV operations. This comprehensive review synthesizes recent advancements in conflict resolution and sensor fusion techniques for multi-UAV systems, highlighting their principles, applications, advantages, and limitations. An analysis of fifteen peer-reviewed papers published between 2020 and 2024 reveals the increasing adoption of decentralized architectures, model predictive control, geometric methods, and deep learning-integrated sensor fusion for dynamic obstacle detection and avoidance. These approaches enable UAVs to operate autonomously in uncertain and complex environments by improving conditional awareness and response times. Furthermore, the review highlights application domains such as precision agriculture, disaster response, and urban navigation. Eventually, limitations regarding sensor calibration, computational demands, and environmental variability are also discussed. Future research directions emphasize the need for hybrid decision frameworks, real-time processing, and AI-enhanced multi-sensor integration to support fully autonomous and cooperative UAV operations.
Downloads
References
H. Liu et al., “A survey of sensors based autonomous unmanned aerial vehicle (UAV) localization techniques,” Complex & Intelligent Systems, vol. 11, Art. no. 371, Jul. 2025.
M. Goarin, G. Li, A. Saviolo, and G. Loianno, “Decentralized nonlinear model predictive control for safe collision avoidance in quadrotor teams with limited detection range,” arXiv preprint arXiv:2409.17379, Sept. 2024.
H. Liu, H. Liu, X. Li, and Y. Liu, “A survey of sensors-based autonomous unmanned aerial vehicle (UAV) localization techniques,” Complex & Intelligent Systems, vol. 11, Art. no. 371, Jul. 2025. doi: 10.1007/s40747-025-01961-2
X. Wang et al., “Learning based UAV path planning for data collection with integrated collision avoidance,” arXiv preprint, Dec. 2023.
J. Yan, J. Feng, and F. Pan, “A distributed task allocation method for multi UAV systems in communication constrained environments,” Drones, vol. 8, Art. no. 342, Jul. 2024.
C. Li et al., “Advances and emerging trends in UAV sensor data fusion technology,” 2025. [Online].
D. Debnath et al., “A review of UAV path planning algorithms and obstacle avoidance methods for remote sensing applications,” Remote Sensing, vol. 16, Art. no. 4019, Oct. 2024.
A. James et al., “Sensor fusion for autonomous indoor UAV navigation in confined spaces,” in Proc. IEEE ICST, 2023.
K. Yue, “Multi-sensor data fusion for autonomous flight of unmanned aerial vehicles in complex flight environments,” Drone Systems and Applications, vol. 12, pp. 1–12, 2024. doi: 10.1139/dsa-2024-0005
D. Wanner, H. A. Hashim, S. Srivastava, and A. Steinhauer, “UAV avionics safety, certification, accidents, redundancy, integrity, and reliability: A comprehensive review and future trends,” Drone Systems and Applications, vol. 12, pp. 1–23, Jun. 2024, doi: 10.1139/dsa-2023-0091.
N. Alhousani, M. Saveriano, I. Sevinc, T. Abdulkuddus, H. Kose, and F. J. Abu-Dakka, “Geometric Reinforcement Learning for Robotic Manipulation,” IEEE Access, vol. 11, pp. 111492–111505, 2023, doi: 10.1109/ACCESS.2023.3322654.
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review. Sensors, 21(6), 2140. https://doi.org/10.3390/s21062140
M. Harun et al., “Recent developments and future prospects in collision avoidance control for unmanned aerial vehicles (UAVs): A review,” Int. J. Robotics and Control Systems, vol. 4, no. 3, pp. 1207–1242, Aug. 2024.
S. Du et al., “Safety risk modelling and assessment of civil unmanned aircraft system operations: A comprehensive review,” Drones, vol. 8, no. 8, 2024.
S. A. et al., “Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture,” Sensors, vol. 24, no. 3, Art. no. 981, Feb. 2024.
W. Zhang, G. Zelinsky, and D. Samaras, “Real-time accurate object detection using multiple resolutions,” in Proc. IEEE Int. Conf. Computer Vision, Oct. 2007, pp. 1–8.
S. N. Shinde and S. Chorage, “Unmanned ground vehicle,” Int. J. Adv. Eng. Manage. Sci., vol. 2, no. 10, 2016.
H. Chao, Y. Cao, and Y. Chen, “Autopilots for small unmanned aerial vehicles: A survey,” Int. J. Control, Autom. Syst., vol. 8, no. 1, pp. 36–44, Feb. 2010. [Online]. Available: https://doi.org/10.1007/s12555-010-0105-z
C. Zhuge, Y. Cai, and Z. Tang, “A novel dynamic obstacle avoidance algorithm based on collision time histogram,” Chin. J. Electron., vol. 26, no. 3, pp. 522–529, 2017.
D. H. Shim, H. Chung, H. J. Kim, and S. Sastry, “Autonomous exploration in unknown urban environments for unmanned aerial vehicles,” in Proc. AIAA GNC Conf., 2005.
PlaneCrashInfo.com, “Accident statistics.” [Online]. Available: http://www.planecrashinfo.com/cause.htm
D. H. Shim, H. Chung, H. J. Kim, and S. Sastry, “Autonomous exploration in unknown urban environments for unmanned aerial vehicles,” in Proc. AIAA GNC Conf., 2005.
R. J. Kiefer, D. K. Grimm, B. B. Litkouhi, and V. Sadekar, “Collision avoidance system,” U.S. Patent 7,245,231, Jul. 17, 2007.
G. Ladd and G. Bland, “Non-military applications for small UAS platforms,” in AIAA Infotech@Aerospace Conf. and AIAA Unmanned... Unlimited Conf., 2009, p. 2046.
L. He, P. Bai, X. Liang, J. Zhang, and W. Wang, “Feedback formation control of UAV swarm with multiple implicit leaders,” Aerosp. Sci. Technol., vol. 72, pp. 327–334, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1270963816309816
S. S. Esfahlani, “Mixed reality and remote sensing application of unmanned aerial vehicle in fire and smoke detection,” J. Ind. Inf. Integr., 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2452414X18300773
H. Shakhatreh et al., “Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges,” IEEE Access, vol. 7, pp. 48572–48634, 2019.
C. A. Wargo, G. C. Church, J. Glaneueski, and M. Strout, “Unmanned aircraft systems (UAS) research and future analysis,” in Proc. IEEE Aerosp. Conf., 2014, pp. 1–16.
Z. Xu, Y. Zhang, Y. Zhang, and F. Gao, “Vision-aided UAV navigation using gradient-based B-spline trajectory optimization,” arXiv preprint arXiv:2209.07003, 2022. [Online]. Available: https://arxiv.org/abs/2209.07003
X. Wang, V. Yadav, and S. N. Balakrishnan, “Cooperative UAV formation flying with obstacle/collision avoidance,” IEEE Trans. Control Syst. Technol., vol. 15, no. 4, pp. 672–679, Jul. 2007.
G. Recchia, G. Fasano, D. Accardo, A. Moccia, and L. Paparone, “An optical flow based electro-optical see-and-avoid system for UAVs,” in Proc. IEEE Aerosp. Conf., Mar. 2007, pp. 1–9.
F. Kóta, T. Zsedrovits, and Z. Nagy, “Sense-and-avoid system development on an FPGA,” in Proc. Int. Conf. Unmanned Aircraft Syst. (ICUAS), Jun. 2019, pp. 575–579.
A. McFadyen, A. Durand-Petiteville, and L. Mejias, “Decision strategies for automated visual collision avoidance,” in Proc. Int. Conf. Unmanned Aircraft Syst. (ICUAS), 2014, pp. 715–725.
R. Beard and J. Saunders, “Reactive vision based obstacle avoidance with camera field of view constraints,” in AIAA Guidance, Navigation and Control Conf., 2008, p. 7250.
H.-J. Cho and M.-T. Tseng, “A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation,” Math. Comput. Model., vol. 58, no. 1, pp. 438–448, 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0895717712003020
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.