@article{ZHANG2026, 
author = {Kang ZHANG and Xinmin TANG and Junwei GU},
title = {Risk-aware autonomous avoidance for eVTOL},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {2},
keywords = {urban air traffic, eVTOL, collision avoidance, chance constraints, motion planning},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2025.32083},
doi = {10.7527/S1000-6893.2025.32083},
abstract = {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.}
}