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.
Aliyu et al. (Mon,) studied this question.