This research endeavors to design the perching maneuver and control in ornithopter robots. By analyzing the dynamic interplay between the robot's flight dynamics, feedback loops, and the environmental constraints, we aim to advance our understanding of the perching maneuver, drawing parallels to biological systems. Inspired by the elegant control strategies observed in avian flight, we develop an optimal maneuver and a corresponding controller to achieve stable perching. The maneuver consists of a deceleration and a rapid pitch-up (vertical turn), which arises from analytically solving the optimization problem of minimal velocity at perch, subject to kinematic and dynamic constraints. The controller for the flapping frequency and tail symmetric deflection is nonlinear and adaptive, ensuring robustly stable perching. Indeed, such adaptive behavior in a sense incorporates homeostatic principles of cybernetics into the control system, enhancing the robot's ability to adapt to unexpected disturbances and maintain a stable posture during the perching maneuver. The resulting autonomous perching maneuvers-closed-loop descent and turn-have been verified and validated, demonstrating excellent agreement with real bird perching trajectories reported in the literature. These findings lay the theoretical groundwork for the development of future prototypes that better imitate the skillful perching maneuvers of birds.

In this work, the application of an optimization algorithm is investigated to optimize static and dynamic engineering problems. The methodology of the approach is to generate random solutions and find a zone for the initial answer and keep reducing the zones. The generated solution in each loop is independent of the previous answer that creates a powerful method. Simplicity as its main advantage and the interlaced use of intensification and diversification mechanisms--to refine the solution and avoid local minima/maxima--enable the users to apply that for a variety of problems. The proposed approach has been validated by several previously solved examples in structural optimization and scored good results. The method is also employed for dynamic problems in vibration and control. A modification has also been done on the method for high-dimensional test functions (functions with very large search domains) to converge fast to the global minimum or maximum; simulated for several well-known benchmarks successfully. For validation, a number of 9 static and 4 dynamic constrained optimization benchmark applications and 32 benchmark test functions are solved and provided, 45 in total. All the codes of this work are available as supplementary material in the online version of the paper on the journal website.

The development of perception and control methods that allow bird-scale flapping-wing robots (a.k.a. ornithopters) to perform autonomously is an under-researched area. This paper presents a fully onboard event-based method for ornithopter robot visual guidance. The method uses event cameras to exploit their fast response and robustness against motion blur in order to feed the ornithopter control loop at high rates (100 Hz). The proposed scheme visually guides the robot using line features extracted in the event image plane and controls the flight by actuating over the horizontal and vertical tail deflections. It has been validated on board a real ornithopter robot with real-time computation in low-cost hardware. The experimental evaluation includes sets of experiments with different maneuvers indoors and outdoors.

Flapping-wing robots (so-called ornithopters) are a promising type of platform to perform efficient winged flight and interaction with the environment. However, the control of such vehicles is challenging due to their under-actuated morphology to meet lightweight requirements. Consequently, the flight control of flapping-wing robots is predominantly handled by the tail. Most ornithopters feature a tail with two degrees of freedom but the configuration choice is often arbitrary and without in-depth study. In this paper, we propose a thorough analysis of the design and in-flight performance for three tails. Their design and manufacturing methods are presented, with an emphasis on low weight, which is critical in ornithopters. The aerodynamics of the tails is analyzed through CFD simulations and their performance compared experimentally. The advantages and performance metrics of each configuration are discussed based on flight data. Two types of 3D flight tests were carried out: aggressive heading maneuvers and level turns. The results show that an inverted V-tail outperforms the others regarding maneuverability and stability. From the three configurations, only the inverted V-Tail can perform an aggressive stable banked level turn with a radius of 3.7 m at a turning rate of 1.6 rad/s. This research work describes the impact of the tail configuration choice on the performance of bird-scale flapping-wing robots.

This work presents a model-free nonlinear controller for an ornithopter prototype with bioinspired wings and tail. The size and power requirements have been thought to allocate a customized autopilot on board. To assess the functionality and performance of the full mechatronic design, a controller has been designed and implemented to execute a prescribed perching 2D trajectory. Although functional, its 'handmade' nature forces many imperfections that cause uncertainty that hinder its control. Therefore, the controller is based on adaptive backstepping and does not require any knowledge of the aerodynamics. The controller is able to follow a given reference in flight path angle by actuating only on the tail deflection. A novel space-dependent nonlinear guidance law is also provided to prescribe the perching trajectory. Mechatronics, guidance and control system performance is validated by conducting indoor flight tests.

The aim of this work is to design, analyze and test the behaviour of first-order CLIK (Closed-Loop Inverse Kinematics) manipulator algorithms under the influence of Cartesian integral error feedback. Although CLIK algorithms has been widely and successfully applied in robot manipulators with structured workspaces, in aerial manipulators the lack of structured workspaces is highly demanding for the control algorithm and some extra requirements are needed. Thus, in this work the standard proportional action in first-order CLIK algorithms is enhanced adding integral actions. Among others benefits, it provides a smoother behaviour needed for smart manipulation, zero steady-state tracking error, rejection to constant disturbances (like those of the numerical algorithm errors) and zero sensitivity to dc-gain uncertainties in stable systems. The theoretical achievement is corroborated with both simulations and experiments on a 7-DoF lightweight aerial manipulator. Experiments also include the integration with a SNS (Saturation in the Null Space) algorithm to deal with physical constraints, demonstrating a successful implementation and performance on a RTOS (Real-Time Operating System).

This paper presents the design and control of a multirotor-based aerial manipulator developed for outdoor operation. The multi-rotor has eight rotors and large payload to integrate a 7-degrees of freedom arm and to carry sensors and processing hardware needed for outdoor positioning. The arm can also carry an end-effector and sensors to perform different missions. The paper focuses on the control design and implementation aspects. A stable backstepping-based controller for the multirotor that uses the coupled full dynamic model is proposed, and an admittance controller for the manipulator arm is outlined. Several experimental tests with the aerial manipulator are also presented. In one of the experiments, the performance of the pitch attitude controller is compared to a PID controller. Other experiments of the arm controller following an object with the camera are also presented.