Due to their simplicity and agility, nowadays drones especially quadcopters have become popular in many applications. However, they still have a velocity limitation on fast descent maneuvers, which is known as Vortex Ring State (VRS) constraint in the literature. In this paper, first, we provide a brief description of VRS, and formalize a constraint on the quadcopter's velocity regarding the VRS. Then a novel control strategy using the yaw rate has been suggested to avoid the VRS velocity constraint. Finally, the motion of the quadcopter and the proposed controller are examined in both simulation and experiment, and the performance of the system in avoiding the unstable VRS region in a fast descent maneuver is demonstrated. The quadcopter's equations of motion and the Yaw Rate Controller (YRC) are simulated in the Matlab/Simulink software. The experiments show that the quadcopter can descend about three times faster than the regular descent maneuvers, using the YRC algorithm without any instability. This is due to the creation of horizontal airflow on the blade disk by the yaw rate maneuver. By using this algorithm, quadcopters can descend faster to save their flight endurance for their main mission goal. Moreover, since this method guarantees the stability of quadcopters in pure descents, the drone can be used in chimneys inspections.

The conventional quadrotor, characterized by four control inputs to control six degrees of freedom, is inherently under-actuated, resulting in limited independent control over pitch and roll angles. In this study, we present a configuration that incorporates four additional control inputs for manipulating the angles of the propeller axes. This addresses the previously mentioned limitations and offers additional advantages. Also, it can exhibit enhanced stability during altitude reduction by mitigating the vortex ring state. We derive the dynamic model for this system using Newthon-Euler's method. To address unmodeled dynamics, uncertainties, disturbances, and the inherent complexity of these systems, we utilize a nonlinear robust sliding mode controller. An affining process is performed to account for the non-affine nature of the dynamic equations. We introduce an optimization criteria aimed at maximizing battery life, leveraging the over-actuation of the system. This controller is applied to a dynamic system, infused with various uncertainties, such as inaccurate actuators, high-delayed servo motors, uncertain physical parameters, and wind disturbances. These factors collectively challenge the robustness of the control strategy. Additionally, the controller is implemented on a MATLAB Simscape Multibody model. Given that the controller is model-based, the consistency of the obtained results validates the accuracy of the derived dynamic equations. This research aligns with the project SAC-1, one of the objectives of our lab, Sharif AgRoLab.

Conventional multi-rotors are under-actuated systems, hindering them from independently controlling attitude from position. In this study, we present several distinct configurations that incorporate additional control inputs for manipulating the angles of the propeller axes. This addresses the mentioned limitations, making the systems "omniorientational". We comprehensively derived detailed dynamic models for all introduced configurations and validated by a methodology using Simscape Multibody simulations. Two controllers are designed: a sliding mode controller for robust handling of disturbances and a novel PID-based controller with gravity compensation integrating linear and non-linear allocators, designed for computational efficiency. A custom control allocation strategy is implemented to manage the input-non-affine nature of these systems, seeking to maximize battery life by minimizing the "Power Consumption Factor" defined in this study. Moreover, the controllers effectively managed harsh disturbances and uncertainties. Simulations compare and analyze the proposed configurations and controllers, majorly considering their power consumption. Furthermore, we conduct a qualitative comparison to evaluate the impact of different types of uncertainties on the control system, highlighting areas for potential model or hardware improvements. The analysis in this study provides a roadmap for future researchers to design omniorientational drones based on their design objectives, offering practical insights into configuration selection and controller design. This research aligns with the project SAC-1, one of the objectives of Sharif AgRoLab.

Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a fully onboard real-time framework that integrates Unmanned Aerial Vehivle (UAV) imagery, the YOLOv12 deep learning model, and a camera projection technique to detect, geolocate, and count palm trees directly during flight. The lightweight YOLOv12n variant, deployed on an NVIDIA Jetson Nano edge device, achieved a detection precision of 92.4%, an average geolocation error of 2.14 m, and a counting error of only 0.2% across 915 trees. Unlike many existing methods that rely on offline processing or offboard computation, the proposed system performs all computations in real time, enabling immediate decision-making for tasks such as plantation density analysis, replanting planning, and yield forecasting. Experimental results demonstrate that the proposed approach provides a scalable, cost-effective, and autonomous solution for modern precision agriculture.