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

2025-05-15

AI Summary

Quadrotors often suffer from instability-induced drift and crash under actuator saturation due to inherent nonlinear dynamics. Method: This paper proposes a hierarchical control decomposition framework that decouples trajectory planning from low-level control, and—novelty—integrates simulation-driven penalty learning: leveraging multi-cost trajectory data to explicitly model saturation-induced instability mechanisms and adaptively learn tracking penalty terms compliant with actuator constraints. The method combines optimization-based trajectory planning with embedded implementation, validated on the Crazyflie platform. Results: Simulation crash rate is reduced by 49%; real-world experiments demonstrate successful high-dynamic flight. Lightweight open-source code and hardware-abstracted interfaces enable rapid deployment across platforms. The core contribution is the first synergistic integration of control decomposition and data-driven penalty learning, significantly enhancing robustness and hardware feasibility during aggressive maneuvers.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search
• Intelligent Robots: Behavior Learning & Control

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Address motor saturation in quadrotor trajectory planning
• Prevent uncontrolled drift and crashes during aggressive maneuvers
• Enable feasible hardware execution via control decomposition

Innovation

• Control decomposition handles motor saturation constraints
• Tracking penalty learned from simulation trajectory data
• Open-source lightweight code for hardware abstraction