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.

This paper presents the design, development, and validation of a fully autonomous dual-arm aerial robot capable of mapping, localizing, planning, and grasping parcels in an intra-logistics scenario. The aerial robot is intended to operate in a scenario comprising several supply points, delivery points, parcels with tags, and obstacles, generating the mission plan from the voice commands given by the user. The paper derives a transferability model of the scenario, the robot, and the task, so that the proposed system design can be generalized to different scenarios (environment transfer) and platforms (embodiment transfer). The proposed transferable system architecture allows the integration of software modules managed by the Aerial Delivery Robot Operations Manager (ADROM) through the Module Interface Instances (MII) that handle the requests and the signals involved during the execution of the operation. The performance of the developed system was evaluated as part of the euROBIN Nancy Competition, conducting more than 50 flight tests. The software modules are open source, making the flight dataset also publicly available.

Flapping-wing aerial robots offer significant advantages over conventional multirotors, including lower noise signatures, higher energy efficiency, and enhanced maneuverability. Despite these benefits, their application in surveillance, particularly person detection and tracking, remains largely underexplored. This paper proposes a bioinspired framework for person detection and tracking, specifically designed for flapping-wing aerial robots. Drawing inspiration from the dual pathways in biological vision, our method integrates an event-by-event blob tracker with a more accurate but slower frame-based detector. The event-based tracker leverages the high temporal resolution and robustness to motion blur of event cameras, effectively compensating for the strong vibrations caused by the flapping strokes of these robots. The frame-based detection (implemented using a deep neural network) periodically corrects and enhances the event-based tracking estimates, globally achieving a balanced trade-off between accuracy, responsiveness, and computational cost. Evaluation with both multirotor and flapping-wing aerial robots validates the effectiveness and efficiency of the approach.

We introduce eFFT, an efficient method for the calculation of the exact Fourier transform of an asynchronous event stream. It is based on keeping the matrices involved in the Radix-2 FFT algorithm in a tree data structure and updating them with the new events, extensively reusing computations, and avoiding unnecessary calculations while preserving exactness. eFFT can operate event-by-event, requiring for each event only a partial recalculation of the tree since most of the stored data are reused. It can also operate with event packets, using the tree structure to detect and avoid unnecessary and repeated calculations when integrating the different events within each packet to further reduce the number of operations. eFFT has been extensively evaluated with public datasets and experiments, validating its exactness, low processing time, and feasibility for online execution on resource-constrained hardware. We release a C++ implementation of eFFT to the community.

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.

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.

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.