Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful integration of UAVs in populated environments. Nonetheless, validating autonomous landing in real scenarios is a challenging task with a high risk of injuring people. In this work, we propose a framework for safe real-time and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments and physics-based simulation. The proposed evaluation pipeline includes the use of Unreal graphics engine coupled with AirSim for realistic drone simulation to evaluate landing strategies. Then, Software-/Hardware-In-The-Loop can be used to test beforehand the performance of the algorithms. The final validation stage consists in a Robot-In-The-Loop evaluation strategy where a real drone must perform autonomous landing maneuvers in real-time, with an avatar drone in a virtual environment mimicking its behavior, while the detection algorithms run in the virtual environment (virtual reality to the robot). This method determines the safe landing areas based on computer vision and convolutional neural networks to avoid colliding with people in static and dynamic scenarios. To test the robustness of the algorithms in adversary conditions, different urban-like environments were implemented, including moving agents and different weather conditions. We also propose different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works on this challenging task, and analyze them through several randomized iterations. The proposed approach allowed us to safely validate the autonomous landing strategies, providing an evaluation pipeline, and a benchmark for comparison. An extensive evaluation showed a 99% success rate in static scenarios and 87% in dynamic cases, demonstrating that the use of autonomous landing algorithms considerably prevents accidents involving humans, facilitating the integration of drones in human-populated spaces, which may help to unleash the full potential of drones in urban environments. Besides, this type of development helps to increase the safety of drone operations, which would advance drone flight regulations and allow their use in closer proximity to humans.

DC motors play a fundamental role in robotic and mechatronic systems applied to the manufacturing industry; but broadly speaking, they are necessary in any system where motion is required. In these types of applications, precise control of position and speed is essential. To achieve this, accurate estimation of dynamic parameters such as inertia, viscous friction, and Coulomb friction is necessary to design efficient and sustainable control strategies. This study presents two methodologies for parameter identification based on the analysis of angular position data from a DC motor. The first method uses a constant (step) torque input, while the second is based on ramp excitation. The proposed method is entirely analytical, that is, it is based on the behavior of the system’s responses to the inputs; this makes the procedure practical and does not require computational cost. The experimental platform integrates a hardware-in-loop (HIL) system that allows for real-time acquisition and actuation, with responses processed in MATLAB/Simulink R2022a to provide the basis for estimating the inertia and friction parameters. To validate the values of the physical parameters, a closed-loop proportional-integral (PI) speed control system was implemented. The results confirm the accuracy and consistency of the identified parameters, highlighting their applicability for improving motor control performance in a wide range of robotic applications.

This paper presents an experimental validation of the aerodynamic sectional modelling approach as a general methodology to model the aerodynamic effects on Aerial Vehicles. The introduced modelling scheme also takes into consideration important effects such as engine load torque and landing gear contact. The dynamic model is derived after Kirchhoff’s formulation expressed as a function of the extended momentum vector. The effective forces and torques are included as the addition of independent sources. The aerodynamic effects are computed as the summation of forces and torques over each body section on the airplane, and the engine thrust and load are included in simulation by means of experimental transfer functions. The experimental validation is conducted using a RC-controlled fixed-wing airplane PT- 40 instrumented with the proper avionics to measure its pose (position and attitude) for trajectory tracking purposes. A comparative analysis shows that the analytical model and the experimental setup describe similar qualitative behavior in an open-loop maneuver and reveals that the propeller load torque produces significant aerodynamic effects on both longitudinal and lateral-directional stability of the vehicle.

This article presents the design and implementation of a speed control system for a direct-current (DC) motor that improves the performance of an earlier design previously studied in the literature by incorporating an integral component that enhances control accuracy and robustness, as well as by including a smoother hyperbolic function in the control law to obtain a continuous, smooth, and bounded control signal. The novelty of the proposed approach lies in the integration of the integral action within a bounded hyperbolic control framework, which simultaneously ensures actuator constraint satisfaction, reduces chattering effects, and improves steady-state accuracy, making it particularly suitable for real-time implementation. A detailed description of the dynamic model of the motor is provided in both frequency-domain and state-space representations to support the control synthesis development. The control strategy was initially developed and validated using Matlab/Simulink simulations and subsequently implemented on an experimental platform, allowing a direct comparison between simulation results and experimental data for both the original and proposed controllers. A series of tests was designed to evaluate the effectiveness of the proposed controller in various operating scenarios, including constant speed regulation, disturbance rejection, stepwise speed changes, and predefined trajectory tracking. The results demonstrate that the proposed controller achieves smooth bounded control signals, improved transient response, and enhanced robustness under diverse operating conditions, confirming its effectiveness for practical DC motor speed control applications.