-- Flapping-wing robots offer great versatility; however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. T o validate T3's effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot's and controller's outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications.

Accurate and robust localization plays a key role for autonomous aerial robots. Today, LiDAR-based and camerabased (indoors and outdoors) and GNSS-based (outdoors) solutions are widely used. However, the emergence of flapping-wing robots [1-3] has motivated a paradigm change. First, the limited payload and the resource-constrained computation impose a limitation on the number and type of sensors to be mounted [4]. Second, ornithopters' flapping strokes entail several challenges for perception (e.g., motion blur in cameras) [5]. Those restrictions are even more critical in the case of flapping-wing micro air vehicles (FWMAV) [6].

Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions.

With the emergence of new flapping-wing micro aerial vehicle (FWMAV) designs, a need for extensive and advanced mission capabilities arises. FWMAVs try to adapt and emulate the flight features of birds and flying insects. While current designs already achieve high manoeuvrability, they still almost entirely lack perching and take-off abilities. These capabilities could, for instance, enable long-term monitoring and surveillance missions, and operations in cluttered environments or in proximity to humans and animals. We present the development and testing of a framework that enables repeatable perching and take-off for small to medium-sized FWMAVs, utilising soft, non-damaging grippers. Thanks to its novel active-passive actuation system, an energy-conserving state can be achieved and indefinitely maintained while the vehicle is perched. A prototype of the proposed system weighing under 39 g was manufactured and extensively tested on a 110 g flapping-wing robot. Successful free-flight tests demonstrated the full mission cycle of landing, perching and subsequent take-off. The telemetry data recorded during the flights yields extensive insight into the system's behaviour and is a valuable step towards full automation and optimisation of the entire take-off and landing cycle.

Birds, bats and many insects can tuck their wings against their bodies at rest and deploy them to power flight. Whereas birds and bats use well-developed pectoral and wing muscles and tendons, how insects control these movements remains unclear, as mechanisms of wing deployment and retraction vary among insect species. Beetles (Coleoptera) display one of the most complex wing mechanisms. For example, in rhinoceros beetles, the wing deployment initiates by fully opening the elytra and partially releasing the hindwings from the abdomen. Subsequently, the beetle starts flapping, elevates the hindwings at the bases, and unfolds the wingtips in an origami-like fashion. Whilst the origami-like fold have been extensively explored, limited attention has been given to the hindwing base deployment and retraction, which are believed to be driven by thoracic muscles. Using high-speed cameras and robotic flapping-wing models, here we demonstrate that rhinoceros beetles can effortlessly elevate the hindwings to flight position without the need for muscular activity. We show that opening the elytra triggers a spring-like partial release of the hindwings from the body, allowing the clearance needed for subsequent flapping motion that brings the hindwings into flight position. The results also show that after flight, beetles can leverage the elytra to push the hindwings back into the resting position, further strengthening the hypothesis of a passive deployment mechanism. Finally, we validate the hypothesis with a flapping microrobot that passively deploys its wings for stable controlled flight and retracts them neatly upon landing, which offers a simple yet effective approach to the design of insect-like flying micromachines.

Insect-scale micro-aerial vehicles, especially, lightweight, flapping-wing robots, are becoming increasingly important for safe motion sensing in spatially constrained environments such as living spaces. However, yaw control using flapping wings is fundamentally more difficult than using rotating wings. In this study, an insect-scale, tailless robot with four paired tilted flapping wings (weighing 1.52 g) to enable yaw control was fabricated. It benefits from the simplicity of a directly driven wing actuator with no transmission and a lift control signal; however, it still has an offset in the lift force. Therefore, an adaptive controller was designed to alleviate the offset. Numerical experiments confirm that the proposed controller outperforms the linear quadratic integral controller. Finally, in a tethered and controlled demonstration flight, the yaw drift was suppressed by the wing-tilting arrangement and the proposed controller. The simple structure drive system demonstrates the potential for future controlled flights of battery-powered, tailless, flapping-wing robots weighing less than 10 grams.

The mechanical complexity of flapping wings, their unsteady aerodynamic flow, and challenge of making measurements at the scale of a sub-gram flapping-wing flying insect robot (FIR) make its behavior hard to predict. Knowing the precise mapping from voltage input to torque output, however, can be used to improve their mechanical and flight controller design. To address this challenge, we created a sensitive force-torque sensor based on a flexured gimbal that only requires a standard motion capture system or accelerometer for readout. Our device precisely and accurately measures pitch and roll torques simultaneously, as well as thrust, on a tethered flapping-wing FIR in response to changing voltage input signals. With it, we were able to measure cross-axis coupling of both torque and thrust input commands on a 180 mg FIR, the UW Robofly. We validated these measurements using free-flight experiments. Our results showed that roll and pitch have maximum cross-axis coupling errors of 8.58% and 17.24%, respectively, relative to the range of torque that is possible. Similarly, varying the pitch and roll commands resulted in up to a 5.78% deviation from the commanded thrust, across the entire commanded torque range. Our system, the first to measure two torque axes simultaneously, shows that torque commands have a negligible cross-axis coupling on both torque and thrust.

Perception systems for ornithopters face severe challenges. The harsh vibrations and abrupt movements caused during flapping are prone to produce motion blur and strong lighting condition changes. Their strict restrictions in weight, size, and energy consumption also limit the type and number of sensors to mount onboard. Lightweight traditional cameras have become a standard off-the-shelf solution in many flapping-wing designs. However, bioinspired event cameras are a promising solution for ornithopter perception due to their microsecond temporal resolution, high dynamic range, and low power consumption. This paper presents an experimental comparison between frame-based and an event-based camera. Both technologies are analyzed considering the particular flapping-wing robot specifications and also experimentally analyzing the performance of well-known vision algorithms with data recorded onboard a flapping-wing robot. Our results suggest event cameras as the most suitable sensors for ornithopters. Nevertheless, they also evidence the open challenges for event-based vision on board flapping-wing robots.

In this work, we present an experimental setup and guide to enable the perching of large flapping-wing robots. The combination of forward flight, limited payload, and flight oscillations imposes challenging conditions for localized perching. The described method details the different operations that are concurrently performed within the 4 second perching flight. We validate this experiment with a 700 g ornithopter and demonstrate the first autonomous perching flight of a flapping-wing robot on a branch. This work paves the way towards the application of flapping-wing robots for long-range missions, bird observation, manipulation, and outdoor flight.

One of the motivations for exploring flapping-wing aerial robotic systems is to seek energy reduction, by maintaining manoeuvrability, compared to conventional unmanned aerial systems. A Flapping Wing Flying Robot (FWFR) can glide in favourable wind conditions, decreasing energy consumption significantly. In addition, it is also necessary to investigate the power consumption of the components in the flapping-wing robot. In this work, two sets of the FWFR components are analyzed in terms of power consumption: a) motor/electronics components and b) a vision system for monitoring the environment during the flight. A measurement device is used to record the power utilization of the motors in the launching and ascending phases of the flight and also in cruising flight around the desired height. Additionally, an analysis of event cameras and stereo vision systems in terms of energy consumption has been performed. The results provide a first step towards decreasing battery usage and, consequently, providing additional flight time.