A Visual Guidance and Control Method for Autonomous Landing of a Quadrotor UAV on a Small USV
Abstract
:1. Introduction
- An improved minimum snap trajectory planning algorithm is proposed to refine the flight trajectory based on given waypoints, ensuring trajectory smoothness while keeping the deviation between the generated trajectory and the target flight path within an acceptable range. This guides the UAV in approaching the USV in narrow river environments while improving flight stability during trajectory tracking and reducing energy consumption caused by frequent velocity changes.
- Based on a small USV adapted for operating in narrow rivers, a landing platform with a vertically placed fiducial marker is designed to separate the UAV landing area from the fiducial marker detection region in order to mitigate the interference from lighting and shadows on the fiducial marker. Additionally, an event-triggered visual guidance and control method is introduced to enhance UAV stability by optimizing heading and position control during the autonomous landing process.
- An autonomous landing system is developed comprising a USV, quadrotor UAV, and wireless ground station. The system design involves both hardware setup and software development. Outdoor experimental results show that the proposed method enables stable and autonomous landing of a UAV on a small USV, demonstrating the feasibility of the PX4 and ROS2 systems.
2. System Modeling
2.1. Coordinate System Definition
2.2. UAV Dynamics Model
2.3. Finite State Machine
- Idle: This is the initial stage of the system. The UAV hovers in the air, waiting for further commands. After receiving the landing command from the ground station, the system transitions to the Approaching stage.
- Approaching: At the beginning of the Approaching stage, the UAV automatically computes an optimized trajectory based on desired waypoints, then initiates trajectory tracking. When the UAV’s front-facing camera detects the fiducial marker on the landing platform, the state automatically switches to the Landing stage.
- Landing: In this stage, the UAV approaches the landing platform based on visual guidance. When the relative pose error between the UAV and the ArUco fiducial marker falls below the threshold value, the motors are shut down and the UAV falls onto the landing platform, completing the landing.
3. Trajectory Generation
3.1. Cost Function and Constraints
3.2. Improved Minimum Snap Algorithm
Algorithm 1: Improved Minimum Snap Algorithm |
1. Initialize: P = {, , , |
2. While |
3. |
4. For to |
5. |
6. |
7. |
8. End For |
9. Get by solving Equation (8) |
10. |
11. For to |
12. |
13. |
14. End For |
15. |
16. |
17. Else |
18. . |
19. |
20. End If |
21. End While |
4. Visual Guidance and Control
4.1. Camera Calibration
4.2. Heading Control
4.3. Position Control
4.4. Failsafe Mechanism
5. System Architecture
5.1. Hardware Setup
5.2. System Communication
6. Experiments
6.1. Stationary Platform
6.1.1. Event-Triggered Mechanism Validation
6.1.2. Yaw Deviation Adjustment
6.1.3. Approaching and Landing
6.2. Moving Platform
6.2.1. Terrestrial Environment
6.2.2. River Environment
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Algorithm | Mean (m) | Standard Deviation (m) |
---|---|---|
Improved minimum snap | 0.4092 | 0.4760 |
Minimum snap with corridor constraint | 1.6840 | 0.9855 |
Minimum snap | 3.3090 | 3.6241 |
Bézier curve | 2.1055 | 1.3185 |
Algorithm | Trajectory Generation Time (s) | Flight Distance (m) | Flight Duration (s) | Energy Consumption (J) |
---|---|---|---|---|
Improved minimum snap | 0.1257 | 185.68 | 87.93 | 27,506.52 |
Minimum snap with corridor constraint | 9.6933 | 173.35 | 101.19 | 31,581.67 |
Minimum snap | 0.0153 | 199.45 | 110.11 | 34,418.62 |
Bézier curve | 0.0095 | 188.72 | 99.15 | 31,009.10 |
Ground Truth (cm) | Stereo Vision (cm) | (cm) |
---|---|---|
50 | 50 | 53 |
100 | 100 | 103 |
150 | 149 | 155 |
200 | 201 | 208 |
250 | 249 | 257 |
300 | 300 | 312 |
350 | 341 | 361 |
400 | 387 | 415 |
450 | 432 | 470 |
500 | 465 | 522 |
550 | 507 | 573 |
600 | 547 | 623 |
650 | 589 | 674 |
700 | 625 | 725 |
750 | 665 | 784 |
800 | 701 | 837 |
850 | 743 | 890 |
Metric | Stereo Vision | |
---|---|---|
Mean Absolute Error | 35.23 cm | 18.35 cm |
Root Mean Square Error | 51.04 cm | 21.58 cm |
Mean Relative Error | 5.38% | 4.00% |
Metric | Stereo Vision | |
---|---|---|
Mean Absolute Error | 4.56 cm | 9.33 cm |
Root Mean Square Error | 8.01 cm | 10.78 cm |
Mean Relative Error | 1.27% | 3.83% |
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Guo, Z.; Wang, J.; Zheng, X.; Zhou, Y.; Zhang, J. A Visual Guidance and Control Method for Autonomous Landing of a Quadrotor UAV on a Small USV. Drones 2025, 9, 364. https://doi.org/10.3390/drones9050364
Guo Z, Wang J, Zheng X, Zhou Y, Zhang J. A Visual Guidance and Control Method for Autonomous Landing of a Quadrotor UAV on a Small USV. Drones. 2025; 9(5):364. https://doi.org/10.3390/drones9050364
Chicago/Turabian StyleGuo, Ziqing, Jianhua Wang, Xiang Zheng, Yuhang Zhou, and Jiaqing Zhang. 2025. "A Visual Guidance and Control Method for Autonomous Landing of a Quadrotor UAV on a Small USV" Drones 9, no. 5: 364. https://doi.org/10.3390/drones9050364
APA StyleGuo, Z., Wang, J., Zheng, X., Zhou, Y., & Zhang, J. (2025). A Visual Guidance and Control Method for Autonomous Landing of a Quadrotor UAV on a Small USV. Drones, 9(5), 364. https://doi.org/10.3390/drones9050364