The flapping-wing robotic birds were inspired by nature to present an alternative way of thrust and lift generation instead of conventional high-speed rotary propellers in unmanned aerial platforms. The advances in flapping technology recently led to the prototyping of leg-claw mechanisms for perching and occasionally very lightweight arms for sampling or tiny object aerial manipulation. A dual-arm manipulator on top of a robotic bird might not be bio-inspired and safe in case of a collision with the environment or human-robot interaction. Here in this work, the previously designed dual-arm scissors-type manipulator has been improved in terms of workspace, mechanism, vision system, and blade placement to present a more natural way of sampling. The new dual-arm, with 100.2(g) weight, is redesigned inside a beak to have protection against possible collisions and also secure the cutting blades within a protected shield. During the flight, the dual-arm system is inside the cover and invisible; the lower beak is opened before manipulation and sets out the arm in a proper place for sampling. This new safety cover (beak) along with the new blade mechanism enhanced the cutting power and the safety of the operation. The experimental results show the successful cutting of a series of plant samples.

A model-based approach for lateral maneuvering of flapping wing UAVs in closed spaces is presented. Bird-size ornithopters do not have asymmetric actuation in the wing due to mechanical complexity, so they rely upon the tail for lateral maneuvering. The prototype E-Flap can deflect the vertical tail to make maneuvers out of the longitudinal plane. This work defines simplified equations for the steady turning maneuver based on the body roll angle. The relation between the velocity of the prototype and the turning radius is also stated. Then, an approach to the attitude is proposed, defining the relation between the deflection of the vertical tail and the roll angle. We prove that, even though this deflection causes a yaw moment, the coupling between yaw and roll dynamics generates also a roll rate. To validate this simplified model, a simple control is presented for continuous circular trajectory tracking inside an indoor flight zone. The objective is to track circular trajectories of a radius 2 times greater than the wingspan at a constant height. Results show a very good agreement between the theoretical and experimental turning radius. In addition, the direct relation between the vertical tail deflection and the roll rate of the ornithopter is identified. Even though the desired radius is not reached, the FWUAV is capable of maintaining a closed turning maneuver for several laps. Therefore, the insight provided by the model proves to be an appropriate approach for aggressive lateral maneuvers of bird-size ornithopters.

The state-dependent Riccati equation (SDRE) is a nonlinear optimal controller with a flexible structure which is one of the main advantages of this method. Here in this work, this flexibility is used to present a novel design for handling a soft constraint for state variables (trajectories). The concept is applied to a large-scale swarm control system, with more than 1000 agents. The control of the swarm satellite system is devoted to two modes of orbit-to-orbit and in-same-orbit cases. Keeping the satellites in one orbit in regulation (point-to-point motion) requires additional constraints while they are moving in Cartesian coordinates. For a small number of agents trajectory design could be done for each satellite individually, though, for a swarm with many agents, that is not practical. The constraint has been incorporated into the cost function of optimal control and resulted in a modified SDRE control law. The proposed method successfully controlled a swarm case of 1024 agents in leader-follower mode for orbit-to-orbit and in-same-orbit simulations. The soft constraint presented a percentage of 0.05 in the error of the satellites with respect to travel distance, in in-same-orbit regulation. The presented approach is systematic and could be performed for larger swarm systems with different agents and dynamics.

This paper presents the modeling and closed loop control of the shape-memory-alloy (SMA)-actuated hip joint of a flapping-wing flying robot (FWFR). Despite the lightweight legs/claw mechanism, a strong force of grasping is needed. The SMAs show high force delivery; however, it is difficult to control (position and temperature) the actuation due to the necessity of high currents for warming up, and time for cooling down process. This paper presents a state-dependent differential Riccati equation (SDDRE) controller taking into account the SMA dynamic and the actuator limits to control the leg/claw system. The use of nonlinear optimal control, specifically, the SDDRE, has been reported for the first time for bio-inspired leg/claw control of FWFR. The dynamics of the SMA actuators and on-off switching of the MOSFETs to provide current for the system demands switching in the design of the controller as a constraint for inputs which was considered in the design. Simulation and experimental results and analysis of different phases of heating of SMAs were discussed and resulted in satisfactory control performance.

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.