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.

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.

Although a good variety of successful LiDAR-based mapping schemes have been developed, these methods present shortcomings when mapping geometrically poor environments. In these scenarios, the chosen map structure and the consideration of map uncertainty are particularly relevant for providing a robust robot motion estimation, critically affecting the quality of the resulting map. This paper introduces the use of probabilistic 3D triangle meshes in LiDAR-based mapping. Our approach combines: i) meshes, which consistently represent planar surfaces and enable the use of decimation techniques to reduce the influence of the measurement noise in the map and improve map fidelity, while strongly reducing the map size; with ii) a probabilistic on-manifold formulation of planar objects, which naturally reflects the measurement uncertainty in the mesh map avoiding inconsistencies in state estimation. The proposed methods are experimentally evaluated both individually and jointly integrated in a generic mapping scheme in different scenarios, showing the improvement in robustness and accuracy in geometrically poor environments and providing strong reductions in map size over existing schemes. We release the used datasets and C++ implementations of the proposed methods.

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.

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.

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

This article presentsSensor Network Security using Robots(SNSR), a novel, open, and flexible architecture that improves security in static sensor networks by benefiting from robot–sensor network cooperation. In SNSR, the robot performs sensor node authentication and radio-based localization (enabling centralized topology computation and route establishment) and directly interacts with nodes to send them configurations or receive status and anomaly reports without intermediaries. SNSR operation is divided into stages set in a feedback iterative structure, which enables repeating the execution of stages to adapt to changes, respond to attacks, or detect and correct errors. By exploiting the robot capabilities, SNSR provides high security levels and adaptability without requiring complex mechanisms. This article presents SNSR, analyzes its security against common attacks, and experimentally validates its performance.

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.

Automatic surveillance and monitoring using Unmanned Aerial Systems (UAS) require the development of perception systems that robustly work under different illumination conditions. Event cameras are neuromorphic sensors that capture the illumination changes in the scene with very low latency and high dynamic range. Although recent advances in eventbased vision have explored the use of event cameras onboard UAS, most techniques group events in frames and, therefore, do not fully exploit the sequential and asynchronous nature of the event stream. This paper proposes a fully asynchronous scheme for intruder monitoring using UAS. It employs efficient event clustering and feature tracking modules and includes a sampling mechanism to cope with the computational cost of event-by-event processing adapting to on-board hardware computational constraints. The proposed scheme was tested on a real multirotor in challenging scenarios showing significant accuracy and robustness to lighting conditions.

This paper presents an bio-inspired event-based perception scheme for agile aerial robot maneuvering. It tries to mimic birds, which perform purposeful maneuvers by closing the separation in the retinal image (w.r.t. the goal) to follow time-to-contact trajectories. The proposed approach is based on event cameras, also called artificial retinas, which provide fast response and robustness against motion blur and lighting conditions. Our scheme guides the robot by only adjusting the position of features extracted in the event image plane to their goal positions at a predefined time using smooth time-to-contact trajectories. The proposed scheme is robust, efficient and can be added on top of commonly-used aerial robot velocity controllers. It has been validated on-board a UAV with real-time computation in low-cost hardware during sets of experiments with different descent maneuvers and lighting conditions.

The need for robots autonomously navigating in more and more complex environments has motivated intense R& D efforts in making robot pose estimation more accurate and reliable. This paper presents a multi-sensor multi-hypothesis method for robust 6-DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multi-hypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multi-core implementation. The proposed method has been extensively validated for closed-loop aerial robot navigation in different urban and industrial scenarios and has shown advantages over well-known single-sensor techniques.

There is a high demand in advanced fault detection methods suitable for sensor networks monitoring complex dynamic systems such as industrial plants or large infrastructure units. This paper proposes a robust and efficient decentralized sensor fault detection method with in-place sensor self-recalibration capability that extracts and uses complex context information referred to the full monitored process. The method includes three main components, all decentralized and sharing the same statistical framework: 1) a consensus-based modeling step based on decentralized RANSAC; 2) a statistical analysis based on Bayesian networks and Hidden Markov Models in which each sensor identifies inconsistencies with the consensus model and determines if it is correctly calibrated, uncalibrated or faulty and; 3) a final step in which each uncalibrated sensor self-recalibrates using the consensus model. The proposed method is efficient in the use of computational and communicational resources, it is scalable and robust against outliers, transmission errors, sensor failures and network topology changes. It has been extensively validated in an experimental industrial setting.

This paper proposes mechanisms to efficiently address critical tasks in the operation of cluster-based target tracking, namely: (1) measurement integration, (2) inclusion/exclusion in the cluster, and (3) cluster head rotation. They all employ distributed probabilistic tools designed to take into account wireless camera networks (WCNs) capabilities and constraints. They use efficient and distribution-friendly representations and metrics in which each node contributes to the computation in each mechanism without requiring any prior knowledge of the rest of the nodes. These mechanisms are integrated in two different distributed schemes so that they can be implemented in constant time regardless of the cluster size. Their experimental validation showed that the proposed mechanisms and schemes significantly reduce energy consumption (>55 percent) and computational burden with respect to existing methods.

This paper is motivated by schemes of robotsensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computing and communication capabilities they are actually endowed with. This paper proposes a Range-Only scheme based on Sparse Extended Information Filters (SEIF) that efficiently exploits their capabilities. The robot computes the SLAM prediction stage and distributes the update stage among beacons within its sensing area. The proposed scheme naturally integrates robot-beacon and inter-beacon measurements, significantly improving map and also robot estimations. Our scheme inherits from SEIF its efficiency and scalability and further reduces robot computational burden by exploiting the beacons computing capability. As a result, it has lower error and lower computer requirements than traditional methods. This paper presents the scheme, evaluates and compares its performance in simulations and real experiments.

This paper proposes a scheme that exploits synergies between RSSI and camera measurements in object localization and tracking using Wireless Camera Networks (WCN). It is based on three main mechanisms: a training method that accurately adapts RSSI-range models to the particular environment; a sensor activation/deactivation method that balances the different information contribution and energy consumptions of camera and RSSI measurements; and a distributed Information Filter to integrate the available measurements. The joint use of these mechanisms drastically reduces energy consumption -40% with no significant degradation w.r.t. existing schemes based on only cameras and shows better robustness to target occlusions. The scheme has been implemented and validated in the indoor CONET Integrated Testbed.

The paper presents entropy-based mechanisms to improve energy efficiency and robustness to transmission errors in camera-based object tracking systems. The main mechanisms are: an entropy-based algorithm that dynamically activates/deactivates nodes; a method that dynamically selects the cluster head using entropies and transmission error rates between the cluster nodes; and a distributed Extended Information Filter (EIF) that integrates measurements gathered within the cluster. These mechanisms have been integrated and implemented in a camera-based Wireless Sensor Network, being each camera node comprised of a TelosB WSN node connected to a CMUcam3 module. The proposed mechanisms and tracking system have been experimented and validated in the CONET Robot-WSN Integrated Testbed.

This paper and video are devoted to the last experiments and demonstration of the AWARE project (European Commission, FP6) carried out in Utrera, near Seville (Spain) May, 2009. The project has developed and validated in field experiments a platform providing the functionalities required for the cooperation of aerial robots with ground sensor-actuator wireless networks, including static and mobile nodes carried by people and vehicles. The project demonstrated the self-deployment, self-configuration and self-repairing of the network by using autonomous helicopters that transported and deployed sensor nodes and loads. These features are highly relevant in natural and urban environments without pre-existing infrastructure or where the infrastructure has been damaged or destroyed. Two validation scenarios have been considered: Disaster Management/Civil Security and Filming.

This paper describes a testbed for general experimentation involving Cooperating Objects (COs). Its architecture considers all COs at the same level. It allows a multiple schemes including multi-robot, WSNs experiments and robot-WSN collaboration working as peers. Currently comprised of 6 mobile robots (5 Pioneer 3AT and one outdoor robot) and 40 static WSN nodes equipped with cameras (IEEE1394 for the robots and embedded cameras for the WSN nodes), laser rangers, and other sensors, it can be easily extended with other hardware due to the use of a modular architecture and standard software tools and interfaces. The testbed allows testing centralized and distributed techniques, is suitable for indoors and outdoors and can be accessed through the Internet for online remote monitoring and visualization. The main experiments already carried out, some of which are described in the paper, focused on cooperative perception and robot-WSN collaboration for network repairing.

The paper presents a framework for cooperative fire detection by means of a fleet of heterogeneous UAVs. Computer vision techniques are used to detect and localize fires from infrared and visual images and other data provided by the cameras and other sensors on-board the UAVs. The paper deals with the techniques used to decrease the uncertainty in fire detection and increase the accuracy in fire localisation by means of the cooperation of the information provided by several UAVs. The presented methods have been developed in the COMETS multi-UAV project.