Members of the order Diptera, the true flies, are among the most maneuverable flying animals. These aerial capabilities are partially attributed to flies\' possession of halteres, tiny club-shaped structures that evolved from the hindwings and play a crucial role in flight control. Halteres are renowned for acting as biological gyroscopes that rapidly detect rotational perturbations and help flies maintain a stable flight posture. Additionally, halteres provide rhythmic input to the wing steering system that can be indirectly modulated by the visual system. The multifunctional capacity of the haltere is thought to depend on arrays of embedded mechanosensors called campaniform sensilla that are arranged in distinct groups on the haltere\'s dorsal and ventral surfaces. Although longstanding hypotheses suggest that each array provides different information relevant to the flight control circuitry, we know little about how the haltere campaniforms are functionally organized. Here, we use in vivo calcium imaging during tethered flight to obtain population-level recordings of the haltere sensory afferents in specific fields of sensilla. We find that haltere feedback from both dorsal fields is continuously active, modulated under closed-loop flight conditions, and recruited during saccades to help flies actively maneuver. We also find that the haltere\'s multifaceted role may arise from the steering muscles of the haltere itself, regulating haltere stroke amplitude to modulate campaniform activity. Taken together, our results underscore the crucial role of efferent control in regulating sensor activity and provide insight into how the sensory and motor systems of flies coevolved.

The ability of \"target tracking,\" such as keeping a target object in sight, is crucial for various activities. However, most sensing systems experience a certain degree of delay due to information processing, which challenges accurate target tracking. The long history of studies on animal behavior has revealed several tactics for it, although a systematic understanding of how individual tactics are combined into a strategy has not been reached. This study demonstrates a multifaceted tracking strategy in animals, which mitigates the adverse delay effects with small implementation costs. Using an active-sensing bat to measure their sensing state while chasing natural prey, we found that bats use a tracking strategy by combining multiple echolocation and flight tactics. The three echolocation tactics, namely the predictive control of sensing direction accompanied by adjusting the sensing rate and angular range, produce a direct compensation effect. Simultaneously, the flight tactic, the counter maneuver, assists echolocation by stabilizing the target direction. Our simulation results demonstrate that these combined tactics improve tracking accuracy over a wide range of delay constraints. In addition, a concise rule based on the angular velocity between bats and targets explains how bats control these tactics, suggesting that bats successfully reduce the burden of multitasking management. Our findings reveal the sophisticated strategy in animals\' tracking systems and provide insights into understanding and developing efficiently integrated strategies in target tracking across various disciplines.

The diversity in butterfly morphology has attracted many people around the world since ancient times. Despite morphological diversity, the wing and body kinematics of butterflies have several common features. In the present study, we constructed a bottom-up butterfly model, whose morphology and kinematics are simplified while preserving the important features of butterflies. The present bottom-up butterfly model is composed of two trapezoidal wings and a rod-shaped body with a thorax and abdomen. Its wings are flapped downward in the downstroke and backward in the upstroke by changing the geometric angle of attack (AOA). The geometric AOA is determined by the thorax-pitch and wing-pitch angles. The thorax-pitch angle is actively controlled by abdominal undulation, and the wing-pitch angle is passively determined because of a rotary spring representing the basalar and subalar muscles connecting the wings and thorax. We investigated the effectiveness of abdominal undulation for thorax-pitch control and how wing-pitch flexibility affects aerodynamic-force generation and thorax-pitch control, through numerical simulations using the immersed boundary-lattice Boltzmann method. As a result, the thorax-pitch angle perfectly follows the desired angle through abdominal undulation. In addition, there is an optimal wing-pitch flexibility that maximizes the flying speed in both the forward and upward directions, but the effect of wing-pitch flexibility on thorax-pitch control is not significant. Finally, we compared the flight behavior of the present bottom-up butterfly model with that of an actual butterfly. It was found that the present model does not reproduce reasonable body kinematics but can provide reasonable aerodynamics in butterfly flights.

Recent exploration in insect-inspired robotics has generated considerable interest. Among insects navigating at low Reynolds numbers, mosquitoes exhibit distinct flight characteristics, including higher wingbeat frequencies, reduced stroke amplitudes, and slender wings. This leads to unique aerodynamic traits such as trailing edge vortices via wake capture, diminished reliance on leading vortices, and rotational drag. This paper shows the energetic analysis of a mosquito-inspired flapping-wing Pico aerial vehicle during hovering, contributing insights to its future design and fabrication. The investigation relies on kinematic and quasi-steady aerodynamic modeling of a symmetric flapping-wing model with a wingspan of approximately 26 mm, considering translational, rotational, and wake capture force components. The control strategy adapts existing bird flapping wing approaches to accommodate insect wing kinematics and aerodynamic features. Flight controller design is grounded in understanding the impact of kinematics on wing forces. Additionally, a thorough analysis of the dynamic stability of the mosquito-inspired PAV model is conducted, revealing favorable controller response and maneuverability at a small scale. The modified model, incorporating rigid body dynamics and non-averaged aerodynamics, exhibits weak stability without a controller or sufficient power density. However, the controller effectively stabilizes the PAV model, addressing attitude and maneuverability. These preliminary findings offer valuable insights for the mechanical design, aerodynamics, and fabrication of RoboMos, an insect-inspired flapping wing pico aerial vehicle developed at UPM Malaysia.

The vast majority of pterosaurs are characterized by relatively large, elongate heads that are often adorned with large, elaborate crests. Projecting out in front of the body, these large heads and any crests must have had an aerodynamic effect. The working hypothesis of the present study is that these oversized heads were used to control the left-right motions of the body during flight. Using digital models of eight non-pterodactyloids (\"rhamphorhyncoids\") and ten pterodactyloids, the turning moments associated with the head + neck show a close and consistent correspondence with the rotational inertia of the whole body about a vertical axis in both groups, supporting the idea of a functional relationship. Turning moments come from calculating the lateral area of the head (plus any crests) and determining the associated lift (aerodynamic force) as a function of flight speed, with flight speeds being based on body mass. Rotational inertias were calculated from the three-dimensional mass distribution of the axial body, the limbs, and the flight membranes. The close correlation between turning moment and rotational inertia was used to revise the life restorations of two pterosaurs and to infer relatively lower flight speeds in another two.

Avian wing morphing allows dynamic, active control of complex flight manoeuvres. Previous linear time-invariant (LTI) models have quantified the effect of varying fixed wing configurations but the time-dependent effects of morphing between different configurations is not well understood. To fill this gap, I implemented a linear parameter-varying (LPV) model for morphing wing gull flight. This approach models the wing joint angles as scheduled parameters and accounts for nonlinear kinematic and gravitational effects while interpolating between LTI models at discrete trim points. With the resulting model, I investigated the longitudinal response associated with various joint extension trajectories. By optimizing the extension trajectory for four independent objectives (speed and pitch angle overshoot, speed rise time and pitch angle settling time), I found that the extension trajectory inherent to the gull wing does not guarantee an optimal response but may provide a sufficient response with a simpler mechanical implementation. Furthermore, the results indicated that gulls likely require extension speed feedback. This morphing LPV model provides insights into underlying control mechanisms, which may allow for avian-like flight in future highly manoeuvrable uncrewed aerial vehicles.

The detection of optic flow is important for generating optomotor responses to mediate retinal image stabilization, and it can also be used during ongoing locomotion for centring and velocity control. Previous work in hummingbirds has separately examined the roles of optic flow during hovering and when centring through a narrow passage during forward flight. To develop a hypothesis for the visual control of forward flight velocity, we examined the behaviour of hummingbirds in a flight tunnel where optic flow could be systematically manipulated. In all treatments, the animals exhibited periods of forward flight interspersed with bouts of spontaneous hovering. Hummingbirds flew fastest when they had a reliable signal of optic flow. All optic flow manipulations caused slower flight, suggesting that hummingbirds had an expected optic flow magnitude that was disrupted. In addition, upward and downward optic flow drove optomotor responses for maintaining altitude during forward flight. When hummingbirds made voluntary transitions to hovering, optomotor responses were observed to all directions. Collectively, these results are consistent with hummingbirds controlling flight speed via mechanisms that use an internal forward model to predict expected optic flow whereas flight altitude and hovering position are controlled more directly by sensory feedback from the environment.

The Bird-like Flapping-wing Air Vehicle (BFAV) is a robotic innovation that emulates the flight patterns of birds. In comparison to fixed-wing and rotary-wing air vehicles, the BFAV offers superior attributes such as stealth, enhanced maneuverability, strong adaptability, and low noise, which render the BFAV a promising prospect for numerous applications. Consequently, it represents a crucial direction of research in the field of air vehicles for the foreseeable future. However, the flapping-wing vehicle is a nonlinear and unsteady system, posing significant challenges for BFAV to achieve autonomous flying since it is difficult to analyze and characterize using traditional methods and aerodynamics. Hence, flight control as a major key for flapping-wing air vehicles to achieve autonomous flight garners considerable attention from scholars. This paper presents an exposition of the flight principles of BFAV, followed by a comprehensive analysis of various significant factors that impact bird flight. Subsequently, a review of the existing literature on flight control in BFAV is conducted, and the flight control of BFAV is categorized into three distinct components: position control, trajectory tracking control, and formation control. Additionally, the latest advancements in control algorithms for each component are deliberated and analyzed. Ultimately, a projection on forthcoming directions of research is presented.

The flapping-wing technology has emerged recently in the application of unmanned aerial robotics for autonomous flight, control, inspection, monitoring, and manipulation. Despite the advances in applications and outdoor manual flights (open-loop control), closed-loop control is yet to be investigated. This work presents a nonlinear optimal closed-loop control design via the state-dependent Riccati equation (SDRE) for a flapping-wing flying robot (FWFR). Considering that the dynamic modeling of the flapping-wing robot is complex, a proper model for the implementation of nonlinear control methods is demanded. This work proposes an alternative approach to deliver an equivalent dynamic for the translation of the system and a simplified model for orientation, to find equivalent dynamics for the whole system. The objective is to see the effect of flapping (periodic oscillation) on behavior through a simple model in simulation. Then the SDRE controller is applied to the derived model and implemented in simulations and experiments. The robot bird is a 1.6 m wingspan flapping-wing system (six-degree-of-freedom robot) with four actuators, three in the tail, and one as the flapping input. The underactuated system has been controlled successfully in position and orientation. The control loop is closed by the motion capture system in the indoor test bed where the experiments of flight have been successfully done.

The unmanned aerial vehicle(UAV) used in agricultural fields usually has a load, which affects stability of flight trajectory. This problem is a great technical challenge and critical issue which has fascinated many researchers in this area. This research paper presents a flight dynamics model for agricultural UAV under load condition. The robust T-S fuzzy control method is applied in the attitude angle control part, and the traditional PID controller is used in the position loop control part. Results of simulation depict that the flight trajectory of agricultural UAV can reach certain stability when the load is relatively small.