Sergio A. Araujo-Estrada

This research focuses on a novel convertible unmanned aerial vehicle (CUAV) featuring four rotors with tilting capabilities combined with a tapered form. This paper studies the transition motion between multirotor and fixed-wing modes based on the mechanical and aerodynamics design as well as the control strategy. The proposed CUAV involves information about design, manufacturing, operation, modeling, control strategy, and real-time experiments. The CUAV design considers a fixed-wing with tiltrotors and provides the maneuverability to perform take-off, hover flight, cruise flight, and landing, having the characteristics of a helicopter in hover flight and an aircraft in horizontal flight. The manufacturing is based on additive manufacturing, which facilitates the creation of a lattice structure within the wing. The modeling is obtained using the Newton–Euler equations, and the control strategy is a PID controller based on a geometric approach on SE(3). Finally, the real-time experiments validate the proposed design for the complete regime of flight, and the research meticulously evaluates the feasibility of the prototype and its potential to significantly enhance the mission versatility.

Drones, unmanned aircraft controlled remotely and equipped with cameras, have seen widespread deployment across military, industrial, and commercial domains. The commercial sector, in particular, has experienced rapid growth, outpacing regulatory developments due to substantial financial incentives. The UK construction sector exemplifies a case where the regulatory framework for drones remains unclear. This article investigates the state of UK legislation on commercial drone use in construction through a thematic analysis of peer-reviewed literature. Four main themes, including opportunities, safety risks, privacy risks, and the regulatory context, were identified along with twenty-one sub-themes such as noise and falling materials. Findings reveal a fragmented regulatory landscape, combining byelaws, national laws, and EU regulations, creating business uncertainty. Our study recommends the establishment of specific national guidelines for commercial drone use, addressing uncertainties and building public trust, especially in anticipation of the integration of “autonomous” drones. This research contributes to the responsible computing domain by uncovering regulatory gaps and issues in UK drone law, particularly within the often-overlooked context of the construction sector. The insights provided aim to inform future responsible computing practices and policy development in the evolving landscape of commercial drone technology.

Nonlinear flight controllers for fixed-wing unmanned aerial vehicles (UAVs) can potentially be developed using deep reinforcement learning. However, there is often a reality gap between the simulation models used to train these controllers and the real world. This study experimentally investigated the application of deep reinforcement learning to the pitch control of a UAV in wind tunnel tests, with a particular focus of investigating the effect of time delays on flight controller performance. Multiple neural networks were trained in simulation with different assumed time delays and then wind tunnel tested. The neural networks trained with shorter delays tended to be susceptible to delay in the real tests and produce fluctuating behaviour. The neural networks trained with longer delays behaved more conservatively and did not produce oscillations but suffered steady state errors under some conditions due to unmodeled frictional effects. These results highlight the importance of performing physical experiments to validate controller performance and how the training approach used with reinforcement learning needs to be robust to reality gaps between simulation and the real world.

Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumenta...

Deep reinforcement learning is a promising method for training a nonlinear attitude controller for fixed-wing unmanned aerial vehicles. Until now, proof-of-concept studies have demonstrated successful attitude control in simulation. However, detailed experimental investigations have not yet been conducted. This study applied deep reinforcement learning for one-degree-of-freedom pitch control in wind tunnel tests with the aim of gaining practical understandings of attitude control application. Three controllers with different discrete action choices, that is, elevator angles, were designed. The controllers with larger action rates exhibited better performance in terms of following angle-of-attack commands. The root mean square errors for tracking angle-of-attack commands decreased from 3.42° to 1.99° as the maximum action rate increased from 10°/s to 50°/s. The comparison between experimental and simulation results showed that the controller with a smaller action rate experienced the friction effect, and the controllers with larger action rates experienced fluctuating behaviors in elevator maneuvers owing to delay. The investigation of the effect of friction and delay on pitch control highlighted the importance of conducting experiments to understand actual control performances, specifically when the controllers were trained with a low-fidelity model.