Autonomous Landing Techniques for Unmanned Aerial Vehicles

Autonomous landing of unmanned aerial vehicles (UAVs) has emerged as a critical capability for extended missions, rapid deployment and safe recovery in civilian, commercial and defence contexts. Techniques span from classical control-based approaches, such as model predictive control, to vision-based methods that exploit fiducial markers, monocular cameras and stereo vision systems. Recent advances in sensor fusion integrate inertial measurements, optical flow and global navigation satellite system (GNSS) data—or operate entirely in GPS-denied environments—by relying on simultaneous localisation and mapping (SLAM) algorithms. Dynamic platform landings on moving ground or marine vessels demand combined guidance and estimation schemes, frequently employing Kalman filtering to compensate for motion uncertainties and environmental disturbances. Simpler solutions use patterned ground markers to guide descent with centimetre-level precision, while more computationally intensive frameworks fuse visual-inertial data on the vehicle or offload processing to ground stations. The global significance of these developments lies in enabling UAVs to operate reliably under adverse weather, contested signal conditions and complex terrains, thereby broadening applications from infrastructure inspection and package delivery to emergency response and scientific observation.

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Autonomous Landing Techniques for Unmanned Aerial Vehicles publication trend

The graph below shows the total number of articles in autonomous landing techniques for unmanned aerial vehicles across all publications each year (not limited to Nature Index journals).

Technical terms

Model Predictive Control (MPC): A control strategy that optimises future control actions over a finite horizon subject to dynamic constraints and disturbance models.

Simultaneous Localisation and Mapping (SLAM): A computational method by which a vehicle constructs a map of an unknown environment while tracking its own position within it.

GPS-denied environment: Operational context in which GNSS signals are unavailable or unreliable, requiring alternative localisation methods.

Fiducial marker (ArUco marker): A predesigned visual pattern placed in the environment to enable precise pose estimation from camera images.

Extended Kalman Filter (EKF): A recursive estimator that linearises nonlinear system and measurement models to fuse sensor data and reduce state uncertainty.

References

1. Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control. Drones (2018).
2. Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments. Electronics (2018).
3. Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition. Electronics (2019).
4. Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach. Sensors (2022).