Autonomous Landing Control of Aerial Vehicles
Summary
The autonomous landing of aerial vehicles constitutes a critical frontier in unmanned flight operations, demanding the seamless integration of perception, estimation, planning and control. As applications span package delivery, infrastructure inspection, disaster response and maritime operations, robust landing algorithms are essential for safe and reliable descents on static and dynamic platforms. Key challenges include underactuated dynamics, aerodynamic disturbances such as wind gusts and ground effect, and uncertainties in target position and motion. Contemporary solutions often employ a hierarchical architecture in which vision or sensor-based state estimation informs a guidance law that generates a feasible trajectory, followed by a feedback controller—ranging from nonlinear model-based schemes to adaptive and robust methods—that regulates position, attitude and descent rate. Stability guarantees are commonly provided through Lyapunov theory or sliding-mode formulations, while disturbance observers mitigate unmodelled effects. Additionally, trajectory planners ensure collision avoidance and adherence to operational constraints. Recent advances harness machine-vision enhancements, extended state observers and flatness-based designs to achieve centimetre-level landing accuracy even on moving or unstructured surfaces.
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Autonomous Landing Control of Aerial Vehicles publication trend
The graph below shows the total number of articles in autonomous landing control of aerial vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Disturbance observer: An estimation algorithm that reconstructs unmodelled forces or perturbations to augment feedback control.
Ground effect: A near-surface aerodynamic phenomenon that increases lift and alters drag as a rotorcraft approaches a landing surface.
Underactuated system: A vehicle with fewer control inputs than degrees of freedom, complicating full state regulation.
Lyapunov stability: A mathematical criterion ensuring that system errors converge to zero or remain bounded under a designed controller.
Vision-based state estimation: The use of camera sensors and image processing to determine vehicle position and orientation relative to a target.
Flatness property: A system characteristic that allows dynamics to be expressed in terms of a set of output variables and their derivatives, facilitating trajectory planning and control.
References
- Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner. IEEE Access (2022).
- Synthesized Landing Strategy for Quadcopter to Land Precisely on a Vertically Moving Apron. Mathematics (2022).
- Flatness-Based Active Disturbance Rejection Control for a PVTOL Aircraft System with an Inverted Pendular Load. Machines (2022).
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