Risk-aware autonomous avoidance for eVTOL

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.