Rotor-Failure-Aware Quadrotors Flight in Unknown Environments

TL;DR

This work tackles the challenge of autonomous quadrotor flight after rotor failures in unknown environments by integrating a composite fault-detection and nonlinear model predictive control (NMPC) framework with a rotor-failure-aware planner and a perception-enhanced platform. A composite FDD mechanism rapidly detects motor and propeller faults across flight stages, triggering a reconfigurable NMPC that sacrifices yaw controllability and emphasizes tilt-prioritized attitude control to maintain stability. The rotor-failure-aware planner generates dynamically feasible, collision-free trajectories under degraded actuation using a front-end dynamic A* and a back-end unconstrained nonlinear optimization with post-failure constraints, including updated thrust and acceleration limits. Realistic hardware, including four anti-torque plates and a LiDAR-based perception system, enables reliable operation under high-speed rotation, with extensive simulations and indoor/outdoor experiments demonstrating robust performance and safe takeoff, hover, and navigation despite rotor faults. This approach advances practical, autonomous post-failure quadrotor operation in cluttered and unknown environments, reducing reliance on global sensors and enabling resilient mission execution.

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.

Figures (12)

• Figure 1: Autonomous waypoint navigation of a quadrotor with rotor failure in the forest. A(1) is the Bird’s-eye view of the flight environment. A(2) and A(3) show snapshots of third-person view from the real-world experiment, where the quadrotor navigates through narrow passages. B(1)-B(3) present the 3D point cloud map and detailed views of the experiment. The planned and flight trajectories are depicted as red and blue paths, respectively. The start and destination points are marked with orange squares, while four waypoints are highlighted with light blue stars. The video is available at https://sojustfish.github.io/Rotor-Failure-Aware/.
• Figure 2: The illustration of thrust index, inertial coordinates, body coordinates, and flight trajectory for the quadrotor with rotor failures.
• Figure 3: The proposed system consists of three main components: perception, planning, and control.
• Figure 4: The platform and components description of the designed quadrotor.
• Figure 5: Four groups of tests are arranged to verify the robustness of the proposed method under both motor and propeller failure. In Test #1 and Test #2, the rotor failure is injected during trajectory tracking. In Test #3 and Test #4, the rotor failure is involved during quadrotor takeoff.

Theorems & Definitions (2)

• Remark 1
• Remark 2