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.

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.

Ground testing is of paramount importance to verify and validate space operations and the associated control algorithms before on-orbit deployment. Although state-of-the-art facilities are capable of reproducing zero-G environment with high degree of fidelity, these infrastructures can be complemented with multi-rotors emulating free flying or free floating conditions, exploiting the similarities and analogies between both domains in terms of floating nature, attitude dynamics, and thrust-wrench relation through the mixer matrix. Furthermore, the effective workspace of the testbed can be extended to the dimensions of the flight area and the coverage of the positioning system. Therefore, this papers introduces a new way to recreate orbital motion within an indoor facility, considering the case study of trajectories derived from the Clohessy–Wiltshire equations. This advancement opens up avenues for replicating close-proximity operations between chaser and target satellites employing fully-actuated multi-rotors that allow decoupling translational and attitude dynamics.

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.

This paper presents a bilateral teleoperation system based on smart servos for the realization of dexterous manipulation tasks with aerial robots or in ground service applications, facilitating the transferability of cognitive capabilities of human workers to robots operating remotely or in high altitude workspaces. The system consists of a pair of lightweight and compliant anthropomorphic dual arm manipulators (LiCAS) in leader-follower configuration. The leader dual arm (LDA) captures the movements of the operator’s arms to obtain the desired joint references, sent to the follower dual arm (FDA) to reproduce in a natural and intuitive way the manipulation task. A model of the smart servos is derived, exploiting the feedback from the FDA actuators to provide the kinesthetic feedback to the LDA, using the pulse width modulation signal (PWM) along with the joint speed to estimate the interaction torque. The mechanical joint compliance of the FDA allows the passive accommodation of the arms to the physical interactions with the manipulated objects or the environment, whereas the very low weight of the arms (1.0 kg LDA, 2.5 kg FDA) and the human-size and human-like kinematics facilitate their use in a wide variety of applications. The performance of the system is evaluated using an industrial task board for benchmarking, and in two illustrative bimanual aerial manipulation tasks.

Research in soft manipulators has significantly enhanced object grasping capabilities, thanks to their adaptability to various shapes and sizes. Applying this technology to on-orbit servicing, especially during the capture and containment stages of active space debris removal missions, might offer a secure, adaptable, and cost-effective solution compared to the trend of increasing the degrees of freedom and complexity of the manipulator (e.g. ClearSpace, Astroscale). This work aims to conduct an experimental proof of concept, for which challenges such as radiation, vacuum, and microgravity are significant, but the predominant issue is ensuring effective operation in the extreme temperature swings, where flexible materials may exhibit cryogenic crystallization or drastic shifts in their elasticity. This work addresses this challenge through an initial stage of analytical modeling of the thermal dynamics inside the manipulator in orbit; which is then used for the development of a first multi-layered experimental prototype (leveraging the properties of TPU, silicone, PTFE and aerogel) tested with liquid nitrogen and heat guns. The tendon-actuated servo-driven gripper is tested in the laboratory by varying the shape and size of objects during the grasping. The results, based on servomotor force metrics to assess the flexible manipulator’s adaptability and object capture efficiency across temperature changes, affirm the concept’s viability. Forces increase up to 220% in cryogenic conditions and decrease by no more than 50% at high temperatures.

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.

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.

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 paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

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.

This article analyzes the evolution and current trends in aerial robotic manipulation, comprising helicopters, conventional underactuated multirotors, and multidirectional thrust platforms equipped with a wide variety of robotic manipulators capable of physically interacting with the environment. It also covers cooperative aerial manipulation and interconnected actuated multibody designs. The review is completed with developments in teleoperation, perception, and planning. Finally, a new generation of aerial robotic manipulators is presented with our vision of the future.

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.

Flapping-wing robots (so-called ornithopters) are a promising type of platform to perform efficient winged flight and interaction with the environment. However, the control of such vehicles is challenging due to their under-actuated morphology to meet lightweight requirements. Consequently, the flight control of flapping-wing robots is predominantly handled by the tail. Most ornithopters feature a tail with two degrees of freedom but the configuration choice is often arbitrary and without in-depth study. In this paper, we propose a thorough analysis of the design and in-flight performance for three tails. Their design and manufacturing methods are presented, with an emphasis on low weight, which is critical in ornithopters. The aerodynamics of the tails is analyzed through CFD simulations and their performance compared experimentally. The advantages and performance metrics of each configuration are discussed based on flight data. Two types of 3D flight tests were carried out: aggressive heading maneuvers and level turns. The results show that an inverted V-tail outperforms the others regarding maneuverability and stability. From the three configurations, only the inverted V-Tail can perform an aggressive stable banked level turn with a radius of 3.7 m at a turning rate of 1.6 rad/s. This research work describes the impact of the tail configuration choice on the performance of bird-scale flapping-wing robots.

This work studies the accuracy of a simple but effective analytical model for a flapping-wings UAV in longitudinal gliding flight configuration comparing it with experimental results of a real ornithopter. The aerodynamic forces are modeled following the linearized potential theory for a flat plate in gliding configuration, extended to flapping-wing episodes modeled also by the (now unsteady) linear potential theory, which are studied numerically. In the gliding configuration, the model reaches a steady-state descent at given terminal velocity and pitching and gliding angles, governed by the wings and tail position. In the flapping-wing configuration, it is noticed that the vehicle can increase its flight velocity and perform climbing episodes. A realistic simulation tool based on Unreal Engine 4 was developed to visualize the effect of the tail position and flapping frequencies and amplitudes on the ornithopter flight in real time. The paper also includes the experimental validation of the gliding flight and the data has been released for the community.

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 proposes a planning algorithm for autonomous media production with multiple Unmanned Aerial Vehicles (UAVs) in outdoor events. Given filming tasks specified by a media Director, we formulate an optimization problem to maximize the filming time considering battery constraints. As we conjecture that the problem is NP-hard, we consider a discretization version, and propose a graph-based algorithm that can find an optimal solution of the discrete problem for a single UAV in polynomial time. Then, a greedy strategy is applied to solve the problem sequentially for multiple UAVs. We demonstrate that our algorithm is efficient for small teams (3-5 UAVs) and that its performance is close to the optimum. We showcase our system in field experiments carrying out actual media production in an outdoor scenario with multiple UAVs.

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.

Taking inspiration from nature, the work presented in this paper aims to develop bio-inspired claws to be used for grasping and perching in flapping-wing aerial systems. These claws can be 3D printed out of two different materials and will be capable of adapt to any shape. Also, they will be soft for avoiding undesired damages on the objects when performing manipulation. These claws will be actuated by shape memory alloys (SMA) springs to get rid of the weight of traditional servos. The design of all the components will be explained in this work. Also, the challenges of being able to control SMA using only a LiPo battery on an aerial vehicle will be exposed. The solutions applied and electronics used will be also described. Lastly, experiments made both in test bench as on flight will be summarized.

In this paper, we propose a new concept of robot which is hybrid, including aerial and crawling subsystems and an arm, and also modular with interchangeable crawling subsystems for different pipe configurations, since it has been designed to cover most industrial oil & gas end-users' requirements. The robot has the same ability than aerial robots to reach otherwise inaccessible locations, but makes the inspection more efficient, increasing operation time since crawling requires less energy than flying, and achieving better accuracy in the inspection. It also integrates safety-related characteristics for operating in the potentially explosive atmosphere of a refinery, being able to immediately interrupt the inspection if a hazardous situation is detected and carry the sensible parts such as batteries and electronic devices away as soon as possible. The paper presents the design of this platform in detail and shows the feasibility of the whole system performing indoor experiments.

This work presents a model-free nonlinear controller for an ornithopter prototype with bioinspired wings and tail. The size and power requirements have been thought to allocate a customized autopilot on board. To assess the functionality and performance of the full mechatronic design, a controller has been designed and implemented to execute a prescribed perching 2D trajectory. Although functional, its 'handmade' nature forces many imperfections that cause uncertainty that hinder its control. Therefore, the controller is based on adaptive backstepping and does not require any knowledge of the aerodynamics. The controller is able to follow a given reference in flight path angle by actuating only on the tail deflection. A novel space-dependent nonlinear guidance law is also provided to prescribe the perching trajectory. Mechatronics, guidance and control system performance is validated by conducting indoor flight tests.

The paper presents a multiple task-priority inverse kinematics algorithm for a dual-arm aerial manipulator. Both tasks defined as equality constraints and inequality constraints are handled by means of a singularity robust method based on the Null-Space based Behavioral control. The proposed schema is constituted by the inverse kinematics control, that receives the desired behavior of the system and outputs the reference values for the motion variables, i.e. the UAV pose and the arm joints position, and a motion control, that computes the vehicle thrusts and the joint torques. The method has been experimentally validated on a system composed by an underactuated aerial hexarotor vehicle equipped with two lightweight 4-DOF manipulators, involved in operations requiring the coordination of the two arms and the vehicle.

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.

The use of unmanned aerial systems for industrial applications has evolved considerably in recent years. This paper presents an aerial system capable of perching autonomously on pipes for inspection and maintenance in industrial environments. The target pipe to perch on is detected using a visual algorithm based on a semantic convolutional neuronal network. The information from a color camera is used to segment the image. Then, the segmentation information is fused with a depth image to estimate the pipe's pose, so that the pose of the robot can be controlled relative to it. The aerial robot is equipped with a soft landing system that robustly attaches it to the pipe. The article presents the complete development of the system. Experimental results performed in outdoor environments are shown.

Winged aerial robots represent an evolution of aerial manipulation robots, replacing the multirotor vehicles by fixed or flapping wing platforms. The development of this morphology is motivated in terms of efficiency, endurance and safety in some inspection operations where multirotor platforms may not be suitable. This paper presents a first prototype of compliant dual arm as preliminary step towards the realization of a winged aerial robot capable of perching and manipulating with the wings folded. The dual arm provides 6 DOF (degrees of freedom) for end effector positioning in a human-like kinematic configuration, with a reach of 25 cm (half-scale w.r.t. the human arm), and 0.2 kg weight. The prototype is built with micro metal gear motors, measuring the joint angles and the deflection with small potentiometers. The paper covers the design, electronics, modeling and control of the arms. Experimental results in test-bench validate the developed prototype and its functionalities, including joint position and torque control, bimanual grasping, the dynamic equilibrium with the tail, and the generation of 3D maps with laser sensors attached at the arms.

This paper presents the motion planning of a novel aerial robotic system with a long-bar extension and two arms for long-reach manipulation in cluttered environments. The novel aerial long-reach manipulator includes a passive revolute joint between the aerial platform and the dual arm. This feature minimises the torque induced to the aerial system in case of unexpected collisions of the manipulator. The motion planning problem is addressed considering jointly the complete set of configuration variables for the aerial platform and the dual arm. Furthermore, the planner has been built over the fundamentals of RRT* algorithms in order to optimise the performance of the trajectories in terms of energy and time. The proposed planning method has been experimentally validated in a realistic industrial scenario, the transportation of a long bar through a cluttered environment consisting of several pipe structures.

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.

The proximity between the multirotor blades and the environmental obstacles restricts the application of aerial manipulators in inspection tasks due to the risk of impacts, the limitation in the reach of the arm, and the physical interactions. This paper presents a long reach aerial manipulator consisting of a hexarotor platform equipped with a 2-DOF compliant joint arm attached at the tip of a one-meter-length link in passive pendulum configuration. The arm integrates magnetic encoders for force/torque estimation-control based on joint deflection, a range sensor in the forearm link for measuring the distance to the contact point, and a camera for visual inspection. A 2-DOF wearable exoskeleton interface has been developed, allowing the teleoperation of the arm with visual feedback in a more intuitive way. The paper also covers the kinematics and dynamics of the aerial manipulator, including the dynamics of the flexible long reach link. The developed system has been evaluated in test-bench and in outdoor flight tests.

This paper presents a method for trajectory generation with Bézier curves, by considering the wind field and dynamic constraints for small Unmanned Aerial Systems (UAS). Atmospheric phenomena affect UAS trajectories, so the wind presence should be considered in order efficiently perform a mission. These missions require precise area decomposition, efficient waypoint sequencing, and a smooth trajectory generation to fulfill the required level of safety and reliability. In this context, a path planning algorithm is presented which incorporates the use of an area decomposition scheme which considers complex shapes and restrictions, in order to provide high levels of coverage. A novel trajectory generation algorithm is proposed, which aims to harvest energy from the atmosphere by taking advantage of the previously estimated wind field and the identified wind features, such as shear wind and discrete gusts, by taking into account flight envelope restrictions. Different test cases and scenarios, including Software-In-The-Loop (SITL) simulations and real telemetry data analysis, are presented to assess the energy gain for the implemented algorithms. The results indicate promising energy gains of almost 15% in voltage saving if the mentioned factors are considered to re-plan the path with the energy-efficient trajectories.

This paper presents a low weight (1.3 kg), human size dual arm system with compliant joints designed for aerial manipulation with a multirotor platform. Each arm provides four degrees of freedom (DOF) for end effector positioning in a kinematic configuration close to the human arm: shoulder pitch, roll and yaw, and elbow pitch. The aluminum frame structure of the arms has been designed with a double purpose: protecting the servo actuators against direct impacts and overloads, and allowing the integration of a compliant transmission mechanism with deflection measurement between the servo shaft and the output link Mechanical joint compliance increases safety in the physical interactions with the environment, removing also joint overloads typical in closed kinematic chain configurations. The dual arm system has been integrated in a hexarotor platform with a visual servoing system for object grasping, evaluating its performance first in a fixed base test bench and later in outdoor flight tests.

This paper addresses a synchronization problem that arises when a team of robots needs to communicate while repeatedly performing assigned tasks in a cooperative scenario. Each robot has a limited communication range and moves along a previously defined closed trajectory. When two robots are close enough, a communication link may be established, allowing the robots to exchange information. The goal is to schedule the motions such that the entire system can be synchronized for maximum information exchange; that is, every pair of neighbors always visit the feasible communication link at the same time. An algorithm for scheduling the team of robots in this scenario is proposed and a robust framework that assures the synchronization of a large team of robots is presented. Simulations, experiments, and computational results demonstrate the applicability of the algorithm. The approach allows the design of fault-tolerant systems that can be used for multiple tasks, such as surveillance, area exploration, and searching for targets in hazardous environments, among others.

In this paper a three layer control architecture for multiple aerial robotic manipulators is presented. The top layer, on the basis of the desired mission, determines the end-effector desired trajectory for each manipulator, while the middle layer is in charge of computing the motion references in order to track such end-effectors trajectories coming from the upper layer. Finally the bottom layer is a low level motion controller, which tracks the motion references. The overall mission is decomposed in a set of elementary behaviors which are combined together, through the Null Space-based Behavioral (NSB) approach, into more complex compounds behaviors. The proposed framework has been tested conducting an experimental campaign.

The development of solar-powered high-altitude UAV has gained increasing attention in the recent years. Several aircraft have had successful flights in the stratosphere, but despite advances in lightweight design they can only carry small payloads compared to the total takeoff mass. This paper suggests to eliminate the need for a landing gear by landing on a mobile ground vehicle. This would not only increase the payload capacity, but also simplify landings in crosswind conditions and thus increase the operational availability. A system with a small UAV and a car-mounted landing platform is prepared as a technology demonstrator. Different aspects of the landing problem are studied in simulations and real experiments and algorithms for the cooperative control of both vehicles are proposed. Simulations as well as experiments with the real car and a simulated UAV show the feasibility of such landings.

This paper presents the design and experimental validation of a Fault Detection, Identification and Recovery (FDIR) system intended for multi-UAV applications. The system exploits the information provided by internal position, attitude and visual sensors onboard the UAVs of the fleet for detecting faults in the measurements of the position and attitude sensors of any of the member vehicles. Considering the observations provided by two or more UAVs in a cooperative way, it is possible to identify the source of the fault, but also implement a Cooperative Virtual Sensor (CVS) which provides a redundant position and velocity estimation of the faulty UAV that can be used for replacing its internal sensor. The vision-based FDIR system has been validated experimentally with quadrotors in an indoor testbed. In particular, fault detection and identification has been evaluated injecting a fault pattern offline on the position measurements, while the CVS has been applied in real time for the recovery phase.

In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.

This paper presents a new method for estimation and identification of shear wind and discrete gusts of a previously unknown wind field by using an Unmanned Aircraft System (UAS). Wind estimation and identification is key in energy-efficient trajectory planning and dynamic soaring applications. The research proposes an approach for mapping a complete wind field from the collected data. Therefore, the generated map also describes areas where UAS has not passed through. The proposed method consists of the next steps: 1) the wind vector is estimated in each UAS position; 2) wind data are fitted into a Weibull probability density function and meeting the Prandtl's power law relationship; 3) scale factor of the Weibull distribution and the power law coefficient are computed; 4) wind feature detection such as shear layer and gusts is performed from the relation wind magnitude vs. altitude obtained; and finally 5), data could be extrapolated to generate the complete wind field. Novel aspects and advantages include the optimization of the scale factor from the estimated wind data by using a genetic algorithm, the identification of wind features separately, and the possibility to apply the method online. Real data of flight have been used to validate the method and many simulations and studies have been performed to test and analyze the proposed method in different scenarios.

This paper presents the design and experimental validation of a compliant and lightweight 3-DOF robotic arm - shoulder yaw, shoulder pitch and elbow pitch joints - equipped with a compliant finger module intended for aerial inspection and manipulation in contact with the environment. A simple transmission mechanism consisting in a pair of compression springs and a flange bearing is integrated in the shoulder pitch and elbow pitch joints between the servo shaft and the output frame. Joint deflection measurement with potentiometer allows joint torque but also contact force estimation and control. The low stiffness of the compliant finger has been exploited for soft collision detection and obstacle localization, in such a way that the contact forces do not significantly disturb the UAV. Fixed-base experiments have been performed with the arm, including the characterization of the compliant joints and the control of the contact force at wrist point.

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

This paper proposes a method called Segmentation-adaptive Pseudospectral collocation to address the problem of safe trajectory generation in missions with cooperating multiple aerial vehicles. Pseudospectral collocation can generate optimized collision-free trajectories, but for multiple aerial vehicles it cannot guarantee that the safety separation distance is maintained in the whole trajectories, since the constraints are only enforced in discrete points in the trajectory (collocation points). Hp-adaptive pseudospectral collocation increases iteratively the number of collocation points and the degree of the approximating polynomial, but this may lead to an exponential increase of the computational load. The proposed method solves the problem by selectively adding new collocation points where they are needed, only in the segments with conflicts in each iteration, thus effectively reducing the number of collocation points and the computation time with respect to other pseudospectral collocation formulations. The proposed method allows both changes of speed and changes of heading for each aerial vehicle to guarantee the safety distance between them. Its computational load and scalability are studied in randomly generated scenarios. Moreover, a comparison with other method is presented. Several experiments to test the validity of the approach have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies.

This paper addresses a synchronization problem that arises when a team of aerial robots (ARs) need to communicate while performing assigned tasks in a cooperative scenario. Each robot has a limited communication range and flies within a previously assigned closed path. When two robots are close enough, a communication link may be established allowing the robots to share information. The goal is to schedule the flights such that the entire system can be synchronized for maximum information exchange, that is, every pair of neighbors are on the feasible communication link at the same time. We propose an algorithm for scheduling a team of robots in this scenario and propose a robust framework where the synchronization of a large team of robots is assured. The approach allows us to design a fault-tolerant system that can be used for multiple tasks such as surveillance, area exploration, searching for targets in a hazardous environment, and assembly and structure construction, to name a few.

This paper presents an approach for decentralized range-only simultaneous localization and mapping (RO-SLAM) of a network of aerial vehicles and a set of static range-only sensors deployed in the environment. The paper makes use of a multi-hypothesis framework developed by the authors [1] in order to deal with the multiple hypotheses that are present in the early stages of the undelayed RO-SLAM, this paper extends the approach to consider the integration of landmark estimations provided by other aerial vehicles nearby the robot. The method will enable a significant reduction in the convergence time needed to remove wrong localization hypotheses for every range-only landmark and, as a results, a map with improved accuracy. The proposed approach is validated first in simulations and later on with real experiments involving real range-only sensors and two unmanned aerial vehicles.

A multilayer architecture to control multirotor UAVs equipped with a servo robot arm is proposed in this paper. The main purpose is to control the aerial platform taking into account the presence of the moving manipulator. Three layers are considered in this work. First, a novel mechanism is proposed considering a moving battery to counterweight the statics of the robotic arm. Then, in order to overcome the mechanical limitations of the previous layer, the residual of the arm static effects on the UAV is computed and compensated through the given control thrust and torques. Finally, an estimator of external forces and moments acting on the aerial vehicle is considered and the estimations are fed back to the controller to compensate neglected aerodynamic effects and the arm dynamics. The performance of the proposed architecture has been experimentally evaluated.

The aim of this work is to design, analyze and test the behaviour of first-order CLIK (Closed-Loop Inverse Kinematics) manipulator algorithms under the influence of Cartesian integral error feedback. Although CLIK algorithms has been widely and successfully applied in robot manipulators with structured workspaces, in aerial manipulators the lack of structured workspaces is highly demanding for the control algorithm and some extra requirements are needed. Thus, in this work the standard proportional action in first-order CLIK algorithms is enhanced adding integral actions. Among others benefits, it provides a smoother behaviour needed for smart manipulation, zero steady-state tracking error, rejection to constant disturbances (like those of the numerical algorithm errors) and zero sensitivity to dc-gain uncertainties in stable systems. The theoretical achievement is corroborated with both simulations and experiments on a 7-DoF lightweight aerial manipulator. Experiments also include the integration with a SNS (Saturation in the Null Space) algorithm to deal with physical constraints, demonstrating a successful implementation and performance on a RTOS (Real-Time Operating System).

This paper presents an aerial manipulator consisting of a multirotor equipped with a robotic multi-link arm attached to the top of the multirotor body. This setup has strong potentialities for inspection of structures, since the arm is able to safely touching the structure surface with a sensor while flying, taking measurements from the underside for example in bridges. The paper presents the dynamic model of the system and the derivation of a nonlinear controller, which is tested in simulation. First flight experiments with prototype of the system are also presented.

This paper presents the design and experimental validation of a low weight compliant arm for aerial manipulation, which uses a linear actuator to move the elbow joint. The proposed arm design allows for estimating payload mass, as well as detecting collisions against obstacles in the forearm. The compliant arm design is intended to be applied in aerial manipulation and transportation, being mounted on the base of an Unmanned Aerial Vehicle (UAV) like a multirotor or a small autonomous helicopter. The payload mass estimation can be useful in the adaptation and stabilization of UAV altitude and attitude controllers when objects of unknown weight are grasped by the arm, while compliance reduces the effect of physical interaction when lifting an object on UAV stability. The paper addresses the mechanical design and construction of a three DoF arm prototype (elbow, wrist roll and pitch), deriving the equations for estimating the payload mass. A set of experiments for payload mass estimation and collision detection and reaction have been performed to test the validity of the approach.

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 addresses the area surveillance problem with a team of multiple aerial robots under communication constraints. In previous work of authors [1], a decentralized modular architecture was proposed for surveillance missions with a team of homogeneous robots. This paper presents a decentralized decision-making algorithm that solves the problem for heterogeneous aerial robots with different sensing and motion capabilities. The proposed frequency-based approach offers a dynamic and robust solution able to adapt to changes in the area size and robot capabilities, and even to total robot failures. The algorithm can run properly under communication constraints and there is no robot which rules the others. Simulations and experimental results validate the proposed system for heterogeneous robots that can be dynamically added/removed during the execution of the mission.

This paper presents a new approach for aerial robots simultaneous localization and mapping (SLAM) oriented to aerial manipulation applications. The approach is based on the integration of range-only measurements and visual markers detected with the on-board camera. A multiple hypotheses framework is applied for range-only together with visual markers SLAM. This approach allows integrating two different types of sensors that are complementary for localization, mixing the stable and continuous estimation provided by range sensors with the precise but infrequent measurements from the visual markers. Real experiments involving an aerial robot and radio/visual markers have been used to validate the approach.

This paper is devoted to a system for aerial manipulation, composed of a helicopter and an industrial manipulator. The usage of an industrial manipulator is motivated by practical applications which were identified in different cooperation projects with the industry. We address the coupling between manipulator and helicopter and show that even in case when we have an ideal controller for manipulator and a highperformance controller for helicopter, an unbounded energy flow can be generated by internal forces between helicopter and manipulator if both controllers are used independently. To solve this problem we propose a new kinematical coupling for control by introducing an additional manipulation DoF realized by helicopter rotation around its yaw axis. The new experimental setup and required modifications in the manipulator controller for this purpose are described. Further, we propose dynamical coupling which is implemented by modification of the helicopter controller feeding the interaction force/torque, measured between manipulator base and fuselage, directly to the actuators of the rotor blades. At the end, we present experimental results for aerial manipulation and their analysis.

Landing of unmanned helicopters is a challenging maneuver which can be seriously affected by GPS sensor reliability. This work explores an alternative approach to GPS sensors to avoid typical drawbacks, such as inaccuracies of standalone sensors or weight/cost for Real Time Kinematic (RTK) setups. To this end, the paper studies a setup consisting of a tether linking the helicopter to the landing point. Appropriate sensing of tether attitude relative to the helicopter together with an altitude measurement, allows estimation of the helicopter's linear position relative to the landing point. Additionally, the tension exerted on the tether provides a stabilizing effect on helicopter translational dynamics. Taking into account all these considerations, a model-based control strategy is developed and implemented in the real platform. The experimental results endorse the validity of the proposed approach.

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 the application of an hp-adaptive pseudospectral for trajectory generation in scenarios with multiple aerial vehicles in order to avoid collisions. This method computes an optimal solution numerically. The method assigns a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial trajectories is minimized. The Estimated Time of Arrival (ETA) of each aerial vehicle is also taken into account to solve the conflicts. Its computational load and scalability depending on the main parameters of the method are studied. Many simulations have been performed to analyze the best parameters of the method. Experiments have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

Exploiting wind currents in the environment allows autonomous gliders to gain altitude and energy and consequently extend flight duration. This paper considers the problem of a persistent monitoring mission using multiple autonomous gliders and exploiting thermal soaring. Communications constraints and non-homogeneous teams of gliders are considered. A distributed method based on coordination variables is proposed to monitor the area in a cooperative manner by following a partitioning patrolling strategy. A distributed one-to-one coordination technique is used to manage the gliders' access to thermals according to their states and known thermal locations. Gliders perform a model-based estimation about their energy losses between thermals to estimate the optimal time to remain in a thermal to maintain persistent surveillance with minimum refresh time. Simulated test results are provided to evaluate how the proposed approach is able to extend the mission while maintaining near-optimal performance.

This paper presents the design and control of a multirotor-based aerial manipulator developed for outdoor operation. The multi-rotor has eight rotors and large payload to integrate a 7-degrees of freedom arm and to carry sensors and processing hardware needed for outdoor positioning. The arm can also carry an end-effector and sensors to perform different missions. The paper focuses on the control design and implementation aspects. A stable backstepping-based controller for the multirotor that uses the coupled full dynamic model is proposed, and an admittance controller for the manipulator arm is outlined. Several experimental tests with the aerial manipulator are also presented. In one of the experiments, the performance of the pitch attitude controller is compared to a PID controller. Other experiments of the arm controller following an object with the camera are also presented.

In this paper, we introduce an approach called FSBS (Forward Search in Belief Space) for online planning in POMDPs. The approach is based on the RTBSS (Real-Time Belief Space Search) algorithm of [1]. The main departure from the algorithm is the introduction of similarity measures in the belief space. By considering statistical divergence measures, the similarity between belief points in the forward search tree can be computed. Therefore, it is possible to determine if a certain belief point (or one very similar) has been already visited. This way, it is possible to reduce the complexity of the search by not expanding similar nodes already visited in the same depth. This reduction of complexity makes possible the real-time implementation of more complex problems in robots. The paper describes the algorithm, and analyzes different divergence measures. Benchmark problems are used to show how the approach can obtain a ten-fold reduction in the computation time for similar obtained rewards when compared to the original RTBSS. The paper also presents experiments with a quadrotor in a search application.

This paper addresses the problem of extending the flight duration of cooperative missions with multiple gliding fixed-wing UAVs by using the energy that comes from static soaring. We consider exploration missions where UAVs should pass through a set of Point of Interest (PoI) with the presence of thermals in the space. These thermals can be exploited to provide energy in terms of altitude for each gliding UAV. The objective of the mission is to extend the flight duration of each UAV to explore the environment without landing and decreasing the time to perform the mission. An algorithm named Bounded Recursive Heuristic Search (BRHS), based on Depth-First search techniques, is applied to the PoIs and to the UAVs. The main advantage is the real time application because of the low computational load. The paper presents a set of simulations and experiments have carried out in the airfield of La Cartuja (Seville, Spain).

This paper deals with aerial manipulators consisting of an unmanned aerial vehicle equipped with a robotic multi-link arm. The paper presents methods for the control of the aerial platform taking into account the motion of the arm. It shows how a Variable Parameter Integral Backstepping controller outperforms the results obtained by using PID controllers. The paper presents a quadrotor with a new arm designed for assembly tasks and the implementation of the proposed control methods. Simulations and outdoor experiments confirm the validity of the proposed approach.

This paper presents the perimeter surveillance problem using a set of cooperative robots with heterogeneous speed capabilities under communication constraints. The problem is solved using a frequency-based approach. In the proposed path-partition strategy, the robots patrol a segment length related to their own capabilities and interchanges information with its neighbors periodically. An efficient decentralized algorithm is applied to coordinate the robots from local information and decisions. Finally, simulation and experimental results are presented to illustrate the convergence and the robustness of the solution.

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.

This paper presents a cooperative system architecture that extends the flight duration of multiple gliding fixed-wing Unmanned Aerial Vehicles (UAVs) for long endurance missions. The missions are defined by a set of Points of Interest (PoI) and UAVs should pass through them. A module to detect and identify thermals is implemented to exploit their energy and extend the flight duration, known as static soaring. A collision-free trajectory planner based on the RRT∗ (Optimal Rapidly-exploring Random Trees) planning algorithm is implemented. The proposed system allows applications in real time because of its low computational needs. Simulations and experiments carried out in the airfield of La Cartuja (Seville, Spain) show the performance and advantages of the proposed system.

This paper presents an undelayed range-only simultaneous localization and mapping (RO-SLAM) based on the Extended Kalman filter. The approach is optimized for working in 3D scenarios, reducing the required computational payload at two levels: first, using a reduced spherical state vector parametrization and, second, proposing a new EKF update scheme. The paper proposes a state vector parametrization based on Gaussian-Mixture to cope with the multi-modal nature of range-only measurements and a reduced spherical parametrization of the range sensor positions that allows to shorten the length of the state vector for a given number of hypotheses. The approach is firstly tested and discussed in simulation, followed by experimental results involving a real robot and radio-based range sensors.

This paper presents an efficient conflict resolution method for multiple aerial vehicles based on speed planning. The problem is assigning a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial planned trajectories is minimized. Also, the arrival time of each aerial vehicle at the end waypoint of the trajectory is taken into account to solve the conflicts. The proposed method involves the use of appropriate airspace discretization. The method consists of two steps: a search tree step, which finds if it exists a solution; and an optimization step by solving a QP-problem, which minimizes a cost function. The paper also presents simulations for several scenarios and experiments that have been carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multiagent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Furthermore, communication models in the multiagent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust cooperation that our techniques can provide.

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 video summarizes the results obtained in the last 10 years in European projects dealing with the cooperation of unmanned aerial systems (UAS), also including the cooperation between UAS and ground sensors and actuator networks. It includes results obtained in the projects COMETS (2002-2005) and AWARE (2006-2009), as well as preliminary results of the projects EC-SAFEMOBILE and ARCAS started respectively July and November 2011.

This paper presents a collision-free path planning method for an aerial vehicle sharing airspace with other aerial vehicles. It is based on grid models and genetic algorithms to find safe trajectories. Monte-Carlo method is used to evaluate the best predicted trajectories considering different sources of uncertainty such as the wind, the inaccuracies in the vehicle model and limitations of on-board sensors and control system.

Radio signal-based localization and mapping is becoming more interesting as applications involving the collaboration between robots and static wireless devices are more common. Under certain assumptions, the problem is basically equivalent to the range-only localization and mapping problem. The paper presents a method for mapping with a mobile robot the position of a set of nodes using radio signal measurements. It uses Gaussian Mixtures for undelayed initialization of the position of the wireless nodes. The paper shows how the approach can be integrated within a Kalman Filter. This way, information can be used in the filter since the first measurement. The paper describes simulations to verify the feasibility of the approach, and presents results obtained with experimental data involving one mobile robot and a wireless sensor network.

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.

Radio signal-based localization and mapping is becoming more interesting in robotics as applications involving the collaboration between robots and static wireless devices are more common. This paper describes a method for mapping with a mobile robot the position of a set of nodes using radio signal measurements. The method employs Gaussian Mixtures Models (GMM) for undelayed initialization of the position of the wireless nodes within a Kalman filter. Moreover, the paper extends the method to consider active sensing strategies in order to map the nodes. Entropy variation is used as a measurement of information gain, and allows to prioritize control actions of the robot. However, as there is no analytical expression for the entropy of a GMM, upper bounds of the entropy, for which close form computation is possible, are used instead. The paper describes simulations that show the feasibility of the approach.

This paper describes a real-time method that obtains a hybrid description of the environment (both metric and semantic) from raw data perceived by a 2D laser scanner. A set of linguistically labelled polylines allows to build a compact geometrical representation of the indoor location where a set of representative points (or features) are semantically described. These features are processed in order to find a list of traversable segments whose middle points are heuristically clustered. Finally, a set of safe paths are calculated from these clusters. Both the environment representation and the safe paths can be used by a controller to carry out navigation and exploration tasks. The method has been successfully tested in simulation and on a real robot.

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. Particularly, a Decentralized Delayed-State Extended Information Filter (DDSEIF) is described, where full state trajectories are considered to fuse the information. This permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications. The sparseness of the information matrix maintains the communications overhead at a reasonable level. The method is applied to cooperative tracking and some results in disaster management scenarios are shown. In this kind of scenarios the target might move in both open field and indoor areas, so fusion of data provided by heterogeneous sensors is beneficial.

This paper presents a new method for the 3D localization of an outdoor wireless sensor network (WSN) by using a single flying beacon-node on-board an autonomous helicopter, which is aware of its position thanks to a GPS device. The technique is based on particle filtering and does not require any prior information about the position of the nodes to be estimated. Its structure and stochastic nature allows a distributed computation of the position of the nodes. The paper shows how the method is very suitable for outdoor applications with robotic data-mule systems. The paper includes a section with experiments.

In this paper, we present a distributed market-based algorithm called S+T, which solves the multi-robot task allocation (MRTA) problem in applications that require the cooperation among the robots to accomplish all the tasks. If a robot cannot execute a task by itself, it asks for help and, if possible, another robot will provide the required service. In the paper, tasks consisting in transmitting data in real-time that could require communication relay services are considered. On the other hand, the parameters of the algorithm can be adapted to give priority to either the execution time or the energy consumption in the mission. The potential generation of deadlocks associated to the relation between tasks and services is studied, and as an original result, a distributed algorithm that prevent them is proposed. The algorithm has been tested in simulations that illustrate the main features of the S+T algorithm.

This paper presents a probabilistic framework where uncertainties can be considered in the mosaic building process. It is shown how can be used as an environment representation for an aerial robot. The inter-image relations are modeled by homographies. The paper shows a robust method to compute them in the case of quasi-planar scenes, and also how to estimate the uncertainties in these local image relations. Moreover, the paper describes how, when a loop is present in the sequence of images, the accumulated drift can be compensated and propagated to the rest of the mosaic. In addition, the relations among images in the mosaic can be used, under certain assumptions, to localize the robot.

The multi-robot task allocation (MRTA) problem has become a key research topic in the field of distributed multirobot coordination in recent years. In this paper, two algorithms for the distributed solution of the MRTA problem are presented. In our market-based approach, robots consider their local plans when bidding and multiple tasks can be allocated to a single robot during the negotiation process. The second algorithm described in the paper is based on the negotiation of subset of tasks and can be considered as a generalization of the first one, which only negotiates single tasks. Both algorithms have been tested in a multirobot simulator with multiple missions consisting in visiting waypoints with promising results.

The paper presents a vision-based position estimation method for UAVs. It assumes a planar scene, approximation that usually holds when a vehicle is flying at a relatively high altitude. Monocular image sequences gathered by the UAV are used to estimate the vehicle motion, but accumulative errors can make diverge the estimated position. The proposed method uses an online-built mosaic to correct the drift associated to the planar motion estimation algorithm. The mosaic allows to use not only the current image but also previously recorded information for localization. Results from actual field experiments are presented

This paper presents a vision-based method to estimate the * real motion of a single camera from views of a planar patch. Projective techniques allow to estimate camera motion from pixel space apparent motion without explicit 3-D reconstruction. In addition, the paper will present the HELINSPEC project, the framework where the proposed method has been tested, and will detail some applications in external building inspection that make use of the proposed techniques.

This paper presents a sensor fault detection and diagnosis system for autonomous helicopters. The system has been tested with the MARVIN autonomous helicopter. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The effectiveness of the proposed approach is demonstrated by means of MARVIN experimental results.

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.

This work presents real time computer vision techniques for autonomous navigation and operation of unmanned aerial vehicles. The proposed techniques are based on image feature matching and projective methods. Particularly, the paper presents the application to helicopter motion compensation and object detection. These techniques have been implemented in the framework of the COMETS multi-UAV systems. Furthermore, The work presents the application of the proposed techniques in a forest fire scenario in which the COMETS system can be demonstrated.

This paper describes the design and implementation of a fuzzy control system for a car-like autonomous vehicle. The problem addressed is the diagonal parking in a constrained space, a typical problem in motion control of nonholonomic robots. The architecture proposed for the fuzzy controller is a hierarchical scheme which combines seven modules working in series and in parallel. The rules of each module employ the adequate fuzzy operators for its task (making a decision or generating a smoothly varying control output), and they have been obtained from heuristic knowledge and numerical data (with geometric information) depending on the module requirements (some of them are constrained to provide paths of near-minimal lengths). The computer-aided design tools of the environment Xfuzzy 3.0 (developed by some of the authors) have been employed to automate the different design stages: 1) translation of heuristic knowledge into fuzzy rules; 2) extraction of fuzzy rules from numerical data and their tuning to give paths of near-minimal lengths; 3) offline verification of the control system behavior; and 4) its synthesis to be implemented in a true robot and be verified on line. Real experiments with the autonomous vehicle ROMEO 4R (designed and built at the Escuela Superior de Ingenieros, University of Seville, Seville, Spain) demonstrate the efficiency of the described controller and of the methodology followed in its design.

This paper addresses the synthesis of a predictive controller for a nonlinear process based on a fuzzy model of the Takagi-Sugeno (T-S) type, resulting in a stable closed-loop control system. Conditions are given that guarantee closed-loop robust asymptotic stability for open-loop bounded-input-bounded-output (BIBO) stable processes with an additive l/sub 1/-norm bounded model uncertainty. The idea is closely related to (small-gain-based) l/sub 1/-control theory, but due to the time-varying approach, the resulting robust stability constraints are less conservative. Therefore the fuzzy model is viewed as a linear time-varying system rather than a nonlinear one. The goal is to obtain constraints on the control signal and its increment that guarantee robust stability. Robust global asymptotic stability and offset-free reference tracking are guaranteed for asymptotically constant reference trajectories and disturbances.

Presents a method to design fuzzy logic controllers of autonomous articulated vehicles. The method considers manoeuvres in industrial environments, including backing-up manoeuvres where the angle between the longitudinal axis of a tractor and its trailer takes large values. These manoeuvres are particularly dangerous because the vehicle can reach the so-called "jackknife" position, which is a very dangerous situation because it may cause the destruction of the roller. Stability analysis is a crucial issue in the backing-up manoeuvres of articulated vehicles. However, the existing results on the stability of fuzzy control systems of articulated vehicles are based on the assumption of a small value of the above-mentioned angle, which is not usually the case when manoeuvring in factories. The paper shows how a fuzzy control system which, under this assumption, satisfies the Lyapunov stability criterion, can lead the system to the "jackknife" position. The method proposed in the paper is based on the qualitative theory of nonlinear dynamical systems and does not assume a small angle between the tractor and the trailer. The paper shows how the method guarantees absolute stability of the fuzzy control system and can preclude the "jackknife" position.

Points out how the nonlinearities involved in multivariable Takagi-Sugeno (T-S) fuzzy control systems could originate complex behavior phenomena, such as multiple equilibrium points or limit cycles, that cannot be detected using conventional stability analysis techniques. In the paper, the application of MIMO frequency-domain methods to predict the existence of multiple equilibria and of limit cycles are presented. The proposed method is based on the formulation of a Lur'e problem from the original structure of a T-S fuzzy system with a fuzzy controller. Furthermore, this technique makes straightforward the application of input-output stability techniques such as the multivariable circle criterion, also called the conicity criterion, and the harmonic balance method. Moreover, in the paper, the application of the harmonic balance method has been generalized to the case of a MIMO fuzzy system with asymmetric nonlinearities and improved by the decreasing conservatism. A new and more general stability index which could be used to perform a bifurcation analysis of fuzzy control systems is presented. The paper includes a collection of examples where the advantages of the proposed approach are made explicit comparing it to the input-output conicity criterion and the Lyapunov direct method.

This paper describes a method for mobile robot position estimation based on feature tracking through the sequence of images provided by a conventional video camera. The block-based correspondence algorithms selects candidates through grey level matching and local coherence analysis; a DSP-based multiprocessing image processing system is used to achieve real time performance. Point-to-point correspondences are used to estimate the motion parameters of a mobile robot. Motion-based procedures to eliminate false matching have been implemented. The method has been applied to a mobile robot in non-structured and outdoor environments.

This paper presents a new approach that analyzes the stability of a general class of path tracking algorithms taking into account the pure delay in the control loop. The analysis has been done for straight paths and paths of constant curvature. This has sufficient generality since most usual paths can be decomposed in pieces of constant curvature. The method has been applied to the pure pursuit path tracking algorithm, one of the most widely used algorithms. The experiments performed with a computer controlled high mobility multi-purpose wheeled vehicle show good agreement with the theoretical predictions of the proposed method.

This paper describes a method of building a map of the environment for a mobile robot equipped with a radial laser scanner. This sensor radially scans in a plane parallel to the ground providing a two-dimensional description of the environment. From this information, the map builder produces a set of (typically) short line segments which approximate the shape of almost any kind of environment (local map). As the robot moves, the different local maps obtained are integrated into a global map, representing, thus, the whole environment observed by the robot during its navigation. In particular the authors focus their attention on the update process of the global map. The proposed algorithm introduces what the authors call a "viewing sector" as a simple mechanism to reduce the number of local map segments to be checked for correspondence for each particular segment from the global map. A line segment fragmentation process is also used in order to manage partial correspondence between segments from both maps. The authors present experimental results obtained with this system that demonstrate successful map building.>

This paper presents methods for planning mobile robot trajectories by considering the kinematic and dynamic constraints on the vehicle motion. The resulting path is smooth and quasilinear in curvature variations. The maximum value of the curvature can be assured to be smaller than the value given by the constraints. Furthermore, speeds along the path are planned subject to the kinematic and dynamic constraints. The resulting trajectories provide ideal conditions for high precision path tracking and positioning. In the paper we present the application of the proposed methods to RAM-1, a new mobile robot designed and built for indoor and outdoor industrial environment.>

The authors present an iconic approach for estimating the pose of a mobile robot equipped with a radial laser rangefinder that requires minimal structure in the environment. The algorithm uses a connected set of short line segments to approximate the shape of any environment and can easily be constructed by the rangefinder itself. The authors describe techniques for efficiently managing the environment map, matching the sensor data to the map and computing the robot's position. Accuracy and runtime results for the implementation are included.>

The stability of rule-based expert control systems is studied. A control structure with a nonlinear plant and a nonlinear controller, designed on the basis of logical rules incorporating the experience of designers and human operators, is considered. Two classes of indices are presented: one related to the relative stability of the equilibrium point at the origin, and the other to the global stability of the system. These indices can be used for the analysis and design of expert control systems. Some examples are included to illustrate the approach.>

Self-learning classification of data and connectivity analysis using probabilistic and fuzzy concepts are compared with subjective and visual rearrangement of data. Both methodologies are fully described and results are compared on two examples: crisp binary data and normalized real data.

Interactive methods to solve large-scale problems with multicriteria formulation are proposed. The mixed use of techniques which have proved their eMciency in large-scale dynamic optimization, combined with techniques based on fuzzy set theory, is suggested. The latter enables us to consider inaccuracies, inherent in decisionmakers' judgements, without a significant increase in computational effort. Two different procedures are studied based on the hypothesis of local imprecise knowledge of the overall decision function. In the first procedure we assume that the decisionmaker can estimate in an approximate numerical way his local trade-offs between criteria; in the second one we assume that the only disposable items of information are linguistic ones. These methodologies were applied to a problem of optimal scheduling in a hydrothermal power system with water resources constraints. Some computational results are included.