Collision avoidance in UAV swarms: A learning-centric perspective on collaborative intelligence

As UAV swarm deployments become more prevalent in mission critical domains, collision avoidance remains a key challenge in ensuring safety, coordination, and autonomy at scale. This survey investigates the state of the art in learning based collision avoidance strategies enabled through collaborative intelligence in UAV swarms. We introduce a six dimensional taxonomy that classifies approaches across decision making paradigms, swarm coordination models, communication architectures, learning methodologies, execution strategies, and safety assurance mechanisms. The survey places particular emphasis on learning based methodologies, which we categorize into four prominent techniques: reinforcement learning, federated learning, neuro inspired models, and hybrid approaches. For each, we provide a detailed review of training architectures, scalability, robustness, and real-time feasibility. Drawing on peer-reviewed publications (2019 to early 2025), we synthesize comparative insights into their application contexts, including trajectory planning, vision-based navigation, decentralized coordination, and multi-agent conflict resolution, while assessing trade-offs in deployment complexity and operational safety. Beyond method specific analysis, the survey highlights key distinctions, practical challenges, and enabling technologies, concluding with open challenges and future directions for scalable and verifiable UAV swarm intelligence.