Quad-LCD: Layered Control Decomposition Enables Actuator-Feasible Quadrotor Trajectory Planning

TL;DR

This paper introduces Quad-LCD, a layered control decomposition method that improves quadrotor trajectory planning by addressing motor saturation issues, significantly reducing crash rates and enabling successful real-world flights.

Contribution

The paper presents a novel layered control decomposition approach that learns a tracking penalty to handle motor saturation, improving trajectory planning for quadrotors.

Findings

Reduces crash rates by 49% in simulation.

Successfully demonstrates real-world flights on Crazyflie.

Provides open-source code for easy implementation.

Abstract

In this work, we specialize contributions from prior work on data-driven trajectory generation for a quadrotor system with motor saturation constraints. When motors saturate in quadrotor systems, there is an ``uncontrolled drift" of the vehicle that results in a crash. To tackle saturation, we apply a control decomposition and learn a tracking penalty from simulation data consisting of low, medium and high-cost reference trajectories. Our approach reduces crash rates by around $49\%$ compared to baselines on aggressive maneuvers in simulation. On the Crazyflie hardware platform, we demonstrate feasibility through experiments that lead to successful flights. Motivated by the growing interest in data-driven methods to quadrotor planning, we provide open-source lightweight code with an easy-to-use abstraction of hardware platforms.