Rotor-Failure-Aware Quadrotors Flight in Unknown Environments

2025-10-13

AI Summary

To address instability, high-speed rotation, and severe vibration caused by single-rotor failure of quadcopters in unknown environments, this paper proposes the first autonomous navigation framework integrating online fault detection and diagnosis (FDD), spatiotemporal trajectory planning, and fault-tolerant control. Our method innovatively unifies FDD with nonlinear model predictive control (NMPC) via co-optimization, yielding a composite NMPC controller that fuses real-time LiDAR perception, motor dynamic modeling, and hardware-based anti-torque plate compensation. The framework ensures robust perception and dynamic obstacle avoidance under high-speed rotational disturbances. Extensive experiments in complex, unstructured environments—including dense indoor spaces and unknown forests—demonstrate stable flight following rotor detachment or motor stall, significantly outperforming state-of-the-art fault-tolerant control approaches.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Rotor failures in quadrotors may result in high-speed rotation and vibration due to rotor imbalance, which introduces significant challenges for autonomous flight in unknown environments. The mainstream approaches against rotor failures rely on fault-tolerant control (FTC) and predefined trajectory tracking. To the best of our knowledge, online failure detection and diagnosis (FDD), trajectory planning, and FTC of the post-failure quadrotors in unknown and complex environments have not yet been achieved. This paper presents a rotor-failure-aware quadrotor navigation system designed to mitigate the impacts of rotor imbalance. First, a composite FDD-based nonlinear model predictive controller (NMPC), incorporating motor dynamics, is designed to ensure fast failure detection and flight stability. Second, a rotor-failure-aware planner is designed to leverage FDD results and spatial-temporal joint optimization, while a LiDAR-based quadrotor platform with four anti-torque plates is designed to enable reliable perception under high-speed rotation. Lastly, extensive benchmarks against state-of-the-art methods highlight the superior performance of the proposed approach in addressing rotor failures, including propeller unloading and motor stoppage. The experimental results demonstrate, for the first time, that our approach enables autonomous quadrotor flight with rotor failures in challenging environments, including cluttered rooms and unknown forests.

Problem

• Addressing rotor failure impacts on quadrotor flight stability
• Enabling autonomous navigation in unknown complex environments post-failure
• Integrating online failure detection with real-time trajectory planning

Innovation

• Composite FDD-based NMPC for fast failure detection
• Rotor-failure-aware planner with spatiotemporal joint optimization
• LiDAR platform with anti-torque plates for reliable perception