在城市空中交通背景下，目前针对动态、不确定环境下电动垂直起降（eVTOL）飞机的实时避让还没有一个成熟的解决方案，对此提出了一种具有风险意识的高效运动规划方法，用于实现eVTOL的实时避让。同时考虑eVTOL的动态碰撞规避约束和静态地理围栏约束，建立了机会约束的模型预测控制问题（CC-MPC）作为运动规划的子问题；利用Big-M方法和置信椭球重构机会约束，将CC-MPC转换为混合整数规划问题（MIP），接着通过迭代凸化的优化方法来快速求解MIP，并在优化过程中加入全局搜索算法作为前端以提供高质量的初始方案；最后通过滚动时域框架整合所有过程，实现了eVTOL动态轨迹的快速生成，在仿真实验中通过实现不同场景下的规划过程，验证了所提方法的有效性。

In the context of urban air traffic， there is currently no mature solution for real-time avoidance of electric Vertical Take-Off and Landing （eVTOL） aircraft in dynamic and uncertain environments. To address this challenge， we propose a risk-aware and efficient motion planning method for real-time eVTOL avoidance. The problem is formulated as a Model Predictive Control （MPC） framework with Chance Constraints MPC （CC-MPC）， incorporating both collision avoidance and geo-fencing constraints. To efficiently handle the chance constraints， we reformulate them using Big-M and confidence ellipsoids， transforming the CC-MPC problem into a Mixed-Integer Programming （MIP） problem. To efficiently solve the MIP， we employ an iterative convexification-based optimization method， complemented by a global search algorithm that serves as a front-end warm-start mechanism. Finally， all components are integrated within a receding horizon control framework to enable fast and dynamic trajectory generation for eVTOLs. Simulation experiments across various flight scenarios demonstrate the effectiveness of the proposed approach.