让无人机在强风中安全飞行，实时感知并应对扰动
Meta-Learning Augmented MPC for Disturbance-Aware Motion Planning and Control of Quadrotors
- 用在线学习的扰动模型预测环境干扰，指导规划
- 结合收缩控制实现飞行器对扰动的稳定跟踪与避障
- 适合复杂环境下的无人机自主飞行系统开发
自主飞行中的主要挑战是未知扰动，可能危及安全并导致碰撞，尤其在障碍物密集环境中。本文提出一种面向自主空中飞行的扰动感知运动规划与控制框架。该框架包含两个核心组件：扰动感知运动规划器和跟踪控制器。扰动感知运动规划器由预测控制策略和在线自适应学习的扰动模型组成。跟踪控制器采用收缩控制方法，确保飞行器在障碍物附近的行为相对于扰动感知轨迹具有安全边界。算法在模拟场景中进行了测试，涉及遭遇强侧风和地面诱导扰动的四旋翼飞行器。
原文摘要 · Abstract (English)
A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and lead to collisions, especially in obstacle-rich environments. This paper presents a disturbance-aware motion planning and control framework designed for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The disturbance-aware motion planner consists of a predictive control scheme and a learned model of disturbances that is adapted online. The tracking controller is designed using contraction control methods to provide safety bounds on the quadrotor behaviour in the vicinity of the obstacles with respect to the disturbance-aware motion plan. Finally, the algorithm is tested in simulation scenarios with a quadrotor facing strong crosswind and ground-induced disturbances.
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