城市空中交通等低空经济概念的提出，描绘了城市超低空无人机高密度运行的愿景，形成了相关无人机高密度运行的技术浪潮。其中，避撞技术作为保障无人机高密度运行安全的关键核心技术之一，直接影响着高密度运行的可靠性。在局部高密度空域的多无人机避撞场景中，现有避撞决策方法无法提供实时可靠的避撞策略。针对上述问题，提出了基于D3QN（dueling double DQN）算法的高密度避撞决策方法。具体地，对状态空间进行重构，基于固定扇区，在能够处理高密度避撞输入的同时，保证了神经网络的稳定性。其次，借鉴现有民航空管体系管制避撞规则，基于同伦理论，设计了由规则引导及自由避撞构成的两阶段高密度避撞决策方法。仿真实验表明，相比于传统方法，本文提出的高密度避撞决策方法在单位密度>30架次／km²的高密度空域中，仍能够维持避撞成功率在99.66%以上，为城市低空无人机的高密度运行提供了切实可行的避撞策略。

The vision of ultra-low-altitude urban drone operations at high density is depicted by the introduction of concepts such as urban air mobility and the low-altitude economy, and a technological wave in high-density drone operations is generated. Collision avoidance technology is recognized as one of the key core technologies for ensuring the safety of high-density drone operations, and its impact on operational reliability is directly acknowledged. In multi-drone collision avoidance scenarios within localized high-density airspace, real-time and reliable collision avoidance strategies are found to be beyond the capabilities of existing decision-making methods. To address this issue, a high-density collision avoidance decision-making method based on the D3QN (dueling double DQN) algorithm was proposed. Specifically, the state space was reconstructed using fixed sectors, thereby enabling high-density collision avoidance inputs to be processed while neural network stability was ensured. Furthermore, existing civil aviation air traffic control collision avoidance rules were referenced, and a two-stage collision avoidance decision-making method was designed, which comprises both rule-guided and free collision avoidance components, based on homotopy theory. Simulation experiments have demonstrated that, compared to conventional methods, a collision avoidance success rate exceeding 99.66% is maintained in airspace densities greater than 30 flights per square kilometer, thus providing a feasible collision avoidance strategy for high-density urban drone operations.