Meta-Learning Augmented MPC for Disturbance-Aware Motion Planning and Control of Quadrotors

TL;DR

This paper introduces a disturbance-aware motion planning and control framework for quadrotors that uses meta-learning and predictive control to handle unknown disturbances, enhancing safety in obstacle-rich environments.

Contribution

It proposes a novel disturbance-aware motion planning and control framework combining meta-learning, predictive control, and contraction control for quadrotors.

Findings

Effective disturbance adaptation in simulation with strong crosswinds

Enhanced safety bounds near obstacles under disturbances

Robustness of the framework demonstrated in simulation scenarios

Abstract

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.