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When a high-voltage charged tether is put into streaming space plasma, the tether’s electric field disturbs the flow of plasma ions and thereby taps momentum from the plasma flow [1-4]. The effect is called electrostatic Coulomb drag. One application is the electric solar wind sail which uses the solar wind to generate interplanetary propulsion [1, 2]. Another application is the Plasma Brake [3, 4] which uses the ionospheric ram flow to generate Coulomb drag that slowly de-orbits the satellite. Both positive and negative tether polarities work. The plasma physics is different, but the net effect is a transfer of momentum in both cases. The reasons are somewhat complicated, but there is good motivation to select positive polarity in the solar wind case and negative polarity in the ionospheric Plasma Brake case. Measurement of Coulomb drag in Low Earth Orbit and testing deployment of tether is to be carried out by ESTCube-2 cubesat [5] which is scheduled for launch in spring 2023, and forthcoming Foresail cubesat scheduled for launch later in 2023-2024.Project DragLiner is ongoing and funded by ESA to define requirements and a preliminary design of a passive Coulomb Drag based deorbit system capable of bringing down LEO spacecrafts in an order of magnitude shorter time than the current regulations of re-enter time for the spacecraft (25 years). Other main requirements for the deorbiting system are low mass and independence from the spacecraft resources. The project will also create a TRL 4 prototype of a Plasma Brake module that can be used to deorbit a few hundred kilogram satellite or launcher upper stage from Low Earth Orbit. The module deploys ~5 km long tether that is made of four 25-50 micrometre diameter conductive wires. In addition to aluminium wires used previously in Cubesat projects we will also evaluate more advanced carbon fibre composite wires. The redundant multi-wire tether structure is used so that the tether does not break even when micrometeoroids cut some of its wires. The tether is deployed from a storage reel. The tether is kept at -1 kV voltage by an onboard high-voltage source. A ~100 m long metal-coated tape tether is used as an electron-gathering surface that closes the current loop. Alternatively, conducting parts of the debris satellite could be used for electron gathering. The power consumption is a few watts. Project Dragliner uses basic Space Plasma Physics to solve a practical and important problem of keeping satellite orbits clean for future generations and preventing a catastrophic Kessler syndrome scenario.[1] Janhunen, P., Electric sail for spacecraft propulsion, J. Prop. Power, 20, 763-764, 2004.[2] Janhunen, P. and A. Sandroos, Simulation study of solar wind push on a charged wire: basis of solar wind electric sail propulsion, Ann. Geophys., 25, 755-767, 2007.[3] Janhunen, P., Electrostatic plasma brake for deorbiting a satellite, J. Prop. Power, 26, 370-372, 2010.[4] Janhunen, P., Simulation study of the plasma-brake effect, Ann. Geophys., 32, 1207-1216, 2014.[5] Iakubivskyi, I., et al., Coulomb drag propulsion experiment of ESTCube-2 and FORESAIL-1, Acta Astronautica, 177, 771-783, 2020.

Extraterrestrial rovers with a general-purpose robotic arm have many potential applications in lunar and planetary exploration. Introducing autonomy into such systems is desirable for increasing the time that rovers can spend gathering scientific data and collecting samples. This work investigates the applicability of deep reinforcement learning for vision-based robotic grasping of objects on the Moon. A novel simulation environment with procedurally-generated datasets is created to train agents under challenging conditions in unstructured scenes with uneven terrain and harsh illumination. A model-free off-policy actor-critic algorithm is then employed for end-to-end learning of a policy that directly maps compact octree observations to continuous actions in Cartesian space. Experimental evaluation indicates that 3D data representations enable more effective learning of manipulation skills when compared to traditionally used image-based observations. Domain randomization improves the generalization of learned policies to novel scenes with previously unseen objects and different illumination conditions. To this end, we demonstrate zero-shot sim-to-real transfer by evaluating trained agents on a real robot in a Moon-analogue facility., Comment: Accepted for publication at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | The source code is available at https://github.com/AndrejOrsula/drl_grasping | The supplementary video is available at https://youtube.com/watch?v=FZSoOkK6VFc

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety., Accepted for publication at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

CubeSats are being deployed for a number of activities including Earth observation, telecommunications, scientific experiments, and due to their low cost and flexibility, more often than not, they are even being considered for use in On-Orbit Servicing (OOS) and debris removal missions. This investigation focuses on using the CubeSat technology to perform autonomous proximity operations with passive target bodies including satellites or space debris. The nonlinear coupled attitude and orbit dynamics for the chaser and the target bodies are modelled and simulated. A nonlinear optimal controller identifies an appropriate rendezvous path. A vision-based navigation system on the chaser satellite records the pose of the target body. The pose observations with stochastic uncertainties are processed using a Kalman filter, and offer state feedback along the satellite path. Such observations in conjunction with the postulated linear control algorithm anchor the chaser to approach the target by maintaining appropriate relative configuration. The linear controller delivers regular maneuvers to compensate for any deviations from the identified reference path. A close-range rendezvous operation is illustrated in a Mission Design Simulator (MDS) tool.

Space debris is positioned as a fatal problem for current and future space missions. Many effective space debris removal methods have been proposed in the past decade, and several techniques have been either tested on the ground or in parabolic flight experiments. Nevertheless, no uncooperative debris has been removed from any orbit until this moment. Therefore, to expand this research field and progressing the development of space debris removal technologies, this paper reviews and compares the existing technologies with past, present, and future methods and missions. Moreover, since one of the critical problems when designing space debris removal solutions is how to transfer the energy between the chaser/de-orbiting kit and target during the first interaction, this paper proposes a novel classification approach, named ET-Class (Energy Transfer Class). This classification approach provides an energy-based perspective to the space debris phenomenon by classifying how existing methods dissipate or store energy during the first contact.

Vision is in fact the richest source of information for ourself and also for outdoors Robotics, and can be considered the most complex and challenging problem in signal processing for pattern recognition. The first results using Vision in the control loop have been obtained in indoors and structured environments, in which a line or known patterns are detected and followed by a robot (Feddema & Mitchell (1989), Masutani et al. (1994)). Successful works have demonstrated that visual information can be used in tasks such as servoing and guiding, in robot manipulators and mobile robots (Conticelli et al. (1999), Mariottini et al. (2007), Kragic & Christensen (2002).) Visual Servoing is an open issue with a long way for researching and for obtaining increasingly better and more relevant results in Robotics. It combines image processing and control techniques, in such a way that the visual information is used within the control loop. The bottleneck of Visual Servoing can be considered the fact of obtaining robust and on-line visual interpretation of the environment, which can be usefully treated by control structures and algorithms. The solutions provided in Visual Servoing are typically divided into Image Based Control Techniques and Pose Based Control Techniques, depending on the kind of information provided by the vision system that determine the kind of references that have to be sent to the control structure (Hutchinson et al. (1996), Chaumette & Hutchinson (2006) and Siciliano & Khatib (2008)). Another classical division of the Visual Servoing algorithms considers the physical disposition of the visual system, yielding to eye-in-hand systems and eye-to-hand systems, that in the case of Unmanned Aerial Vehicles (UAV) can be translated as on-board visual systems (Mejias (2006)) and ground visual systems (Martinez et al. (2009)). The challenge of Visual Servoing is to be useful in outdoors and non-structured environments. For this purpose the image processing algorithms have to provide visual information that has to be robust and works in real time. UAV can therefore be considered as a challenging testbed for visual servoing, that combines the difficulties of abrupt changes in the image sequence (i.e. vibrations), outdoors operation (non-structured environments) and 3D information changes (Mejias et al. (2006)). In this chapter we give special relevance to the fact of obtaining robust visual information for the visual servoing task. In section (2).we overview the main algorithms used for visual tracking and we discuss their robustness when they are applied to image sequences taken from the UAV. In sections (3). and (4). we analyze how vision systems can perform 3D pose estimation that can be used for

Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations requires demonstrating compliance with applicable regulations and a comprehensive understanding of the residual risk associated with the UAS operation. To support these activities and enable the safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has established a UAS regulatory framework that mandates the execution of safety risk assessment for UAS operations in order to gain authorization to carry out certain types of operations. Driven by this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment for UAS operations. However, existing work on SORA and its applications focuses mainly on single UAS operations, offering limited support for assuring operations conducted with multiple UAS and with autonomous features. Therefore, the work presented in this paper analyzes the application of SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS with autonomous features inside an airport. We present the decision-making process of each SORA step and its application to a multiple UAS scenario. The results shows that the procedures and safety features included in the Multi-AFI operation such as workspace segmentation, the independent multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can be conducted safely and can receive a positive evaluation from the competent authorities. We also present our key findings from the application of SORA and discuss how it can be extended to better support multi-UAS operations. Unión Europea 101017258

Background and Objective Operating Rooms (ORs) are among the most expensive services in hospitals. A challenge to optimize the OR efficiency is to improve the surgery scheduling task, which requires the estimation of surgical time duration. Surgeons or programming units (based on people's experience) typically do the duration estimation using an experience-based strategy, which may include some bias, such as overestimating the surgery time, increasing ORs' operational cost. Methods This paper analyzes a machine learning-based solution for surgical time predictions. We apply and compare four machine-learning algorithms (Linear Regression, Support Vector Machines, Regression Trees, and Bagged Trees) to predict the surgical time duration at a tertiary referral university hospital in Bogota, Colombia. Historical data from 2004 until 2019 was used to train the algorithms. Comparison among algorithms was given in terms of the Root Mean Square Error (RMSE) of the predicted surgery duration and the algorithms' computing time. The algorithm with the best performance was compared to the currently used experience-based method. Results All the ML algorithms predict the surgery duration with an error between 26 and 37 min. The best overall performance was obtained using Bagged Trees (26 min RMSE, 3.16 min training time, 0.49 min testing time) when using a subset of the DB with the nine specialties containing 80% of the surgeries. Bagged Trees also outperformed the experience-based method with a lower RMSE; however, it also shifted from a predominant overestimation to underestimating surgeries' duration. Conclusions Different ML algorithms for predicting the surgical time duration, showing and comparing their performance. Bagged Trees showed the best performance in terms of RMSE and computing time. Depending on the initial data, Bagged Trees outperformed the experience-based method, but future work is necessary to suit it, like any other ML algorithm, to the hospitals' needs.

Robotics applications such as spot-welding, spray-painting, drilling, and objects handling, require the robot to visit successively multiple targets. The robot travel time among targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the visiting order and by the robot configurations used to reach each target. It is crucial to optimize these elements during the motion planning stage, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP).

This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

The recycling industry in Colombia needs improvements to keep up with market evolution. Currently, low-income families are the ones in charge of collecting recyclable materials directly from mixed-waste bins and taking them to public collection centers, where they are manually classified. Collection centers become an interesting scenario to introduce collaborative robots in the monotonous and unhealthy task of classifying plastic bottles. However, safety and reliance are critical points that need to be ensured. This paper presents the design of a safety protocol for the waste separation task. A system that integrates computer vision with an industrial robot is proposed to secure the human-robot interaction, based on ISO 10218-2 and ISO/TS15066 norms. The protocol assessment is conducted on a testbed created to emulate a pre-classification workstation of plastic bottles.

This paper presents the design and implementation of a tool for Human-Robot collaborative tasks. Industry 4.0 proposes a new scenario where robots can safely work in direct cooperation with humans, within a defined workspace. These robots are called collaborative robots or COBOTS. In this paper, we propose a tool that can be used by a robot and/or an operator for pick and place tasks. The tool was designed to meet certain criteria for the robot, so that it can identify it, hold it, and perform the task. Additionally, it was designed to comply with ergonomic parameters for the human operator. A prototype of the tool was printed with a FusedForm 3D printer. This prototype was used for a test where a robot and an operator will use the tool for picking plastic bottles from a worktable. An UR3 robot was used in the test. It was equipped with a camera in charge of identifying and estimating the position of the tool using ArUco codes placed on the tool. Results show the functionality of the tool for both the robot and the human operator. Additionally, ergonomic tests provided insights to improve the handling system of the tool for the human operator.

In Colombia up to 40% of yield variability is due to the effects of climate variations. Rapid phenotyping methods are needed to properly assess the crop and improve production rates. In this paper, we propose to focus on developing a noninvasive system for speeding up monitoring tasks in rice crops. Unmanned Aerial Vehicles are used to gather multispectral visual information for high-throughput crop monitoring. Geo-referenced digital surface models of the crop are generated based on image mosaicing techniques to allow for the autonomous computation of several vegetative indices. This paper presents the implemented system (hardware and software) and a field report of experiments carried out at different crop growth stages.

A large amount of data, provided in the form of video data, is acquired during manned inspections flights of electric power lines. This data is analyzed by expert human inspectors to detect faults in the power lines infrastructure and prepare the inspection reports. This process is extremely time consuming, very expensive and prone to human error. In this paper, we present PoLIS: the Power Line Inspection Software, which has been developed with the objective of assisting the analysis of the data acquired during inspection flights. PoLIS is based on the cooperation between computer vision and machine learning techniques to automatically process video sequences acquired during inspection flights, resulting in a set of representative images per electric tower which we call Key Frames. These representative images can then be used for inspection purposes, leading to a drastic reduction of the human operators’ workload. At the core of the strategy lies an electric tower detector, which is in charge of estimating the location of the towers within the images based on the combination of a sliding window search technique and a supervised classifier. The location of the tower is then tracked using a tracking-by-registration algorithm based on direct methods, estimating the position of the tower in different images. Finally, different criteria are applied for defining whether the image corresponds to a Key Frame image or not. Extensive evaluation of the proposed strategy is conducted using videos acquired during manned helicopter inspections. The videos constituting this database contain several thousand frames representing both medium and high voltage power transmission lines in the infra-red (IR) and visible spectra. The obtained results show that the proposed strategy can reduce the large amount of data present in the inspection videos to a few Key Frames for each tower. It is also demonstrated that the learning-based approach proposed in PoLIS is appropriate for detecting electric towers, a process which is made faster and more robust by coupling it with a tower tracking algorithm. A Graphical User Interface allowing the application of PoLIS to user-provided videos is also presented in this paper, illustrating the whole process and the automated generation of an inspection report.

The ROS-Industrial open source project was intended to expand the applicability of industrial robots by reducing the gap that existed between researchers and manufacturers, in order to start developing state of the art applications for the industry. This is part of the scope of the PIR (Perception for Industrial Robots) project from the Pontificia Universidad Javeriana Bogota (PUJ). PIR project looks for expanding robot’s capabilities for working in dynamic industrial environments. This paper presents the starting point of this project: the setup of the Motoman SDA_10 dual-arm robot with ROS-Industrial. In this paper, the different steps and modifications carried out to setup the system are presented. Simulation experiments and real tests were conducted in order to analyse and check the behaviour of the system. Results show some of the advantages of using ROS-Industrial, and the wide range of tasks that can be explored with these kind of robots from the research and application point of views (in Latin America only a few robots with this type of configuration is available for research purposes).

Around 8 to 10 million Ton of rice are required in the following years to be able to supply the demand of the overall population. Analysis and monitoring of rice crops becomes nowadays very important issue for farmers, for ensuring a rice production level to cope this demand. This paper presents simulation results of an algorithm that allows to plan and create 2D maps using the technique of image mosaicking with multiple geo-referenced aerial images (multispectral images in the scope of the project). The planning algorithm is called Image Capture algorithm. It takes into account the area the UAV has to cover, the camera configuration, and the state of the UAV in order to define where to take the pictures to build the mosaic. The algorithm presented in this paper was developed in ROS (Indigo) and simulated in Gazebo. The results of this first approach to the 2D mapping of a rice crop allows to see that using the proposed algorithm, it is possible to automate the process of acquiring the pictures for creating the mosaic, ensuring that all the area of interest is covered. By using this algorithm, pictures will be acquired only in specific areas. Therefore, keeping the storage capacity on-board, under control.

The vast majority of approaches make use of features to track objects. In this paper, we address the tracking problem with a tracking-by-registration strategy based on direct methods. We propose a hierarchical strategy in terms of image resolution and number of parameters estimated in each resolution, that allows direct methods to be applied in demanding real-time visual-tracking applications. We have called this strategy the Hierarchical Multi-Parametric and Multi-Resolution strategy (HMPMR). The Inverse Composition Image Alignment Algorithm (ICIA) is used as an image registration technique and is extended to an HMPMR-ICIA. The proposed strategy is tested with different datasets and also with image data from real flight tests using an Unmanned Aerial Vehicle, where the requirements of direct methods are easily unsatisfied (e.g. vehicle vibrations). Results show that using an HMPMR approach, it is possible to cope with the efficiency problem and with the small motion constraint of direct methods, conducting the tracking task at real-time frame rates and obtaining a performance that is comparable to, or even better than, the one obtained with the other algorithms that were analyzed.

Mediator is a conserved transcriptional co-activator that links transcription factors bound at enhancer elements to RNA Polymerase II. Mediator-RNA Polymerase II interactions can be sterically hindered by the Cyclin Dependent Kinase 8 (CDK8) module, a submodule of Mediator that acts to repress transcription in response to discrete cellular and environmental cues. The CDK8 module is conserved in all eukaryotes and consists of 4 proteins: CDK8, CYCLIN C (CYCC), MED12, and MED13. In this study, we have characterized the CDK8 module of Mediator in maize. The maize genome contains single copy genes forCdk8, CycC,andMed13,and two genes forMed12.Analysis of expression data for the CDK8 module demonstrated that all five genes are broadly expressed in maize tissues, withZmMed12a, ZmMed12b,andZmMed13exhibiting similar expression patterns. We performed aDissociation (Ds)insertional mutagenesis, recovering two independent insertions in theZmMed12agene. One of theseDsinsertions results in a truncation of theZmMed12atranscript. Our molecular characterization of the maize CDK8 module, as well as transposon tagging ofZmMed12a,establish the basis for molecular and functional studies of these important transcriptional regulators inZea mays.

Several studies in multiple sclerosis (MS) suggest a trend of increasing disease frequency in women during the last decades. A direct comparison of gender ratio trends among MS populations from Argentina remains to be carried out. The objective of the study was to compare gender ratio trends, over a 50-year span in MS populations from Argentina. Methods: multicenter study that included patients from 14 MS Centers of Argentina. Patients with definite MS with birth years ranging from 1940 to 1989 were included. Gender ratios were calculated by five decades based on year of birth and were adjusted for the F/M born-alive ratio derived from the Argentinean national registry of births. The F/M ratios were calculated using a multivariate logistic regression per five decades by the year of birth approach. Analyses were performed using Stata 10.1. Results: 1069 patients were included. Gender ratios showed a significant increase from the first to the last decade in the whole MS sample (from 1.8 to 2.7; p value for trend = 0.023). The Gender ratio did not show differences considering MS subtype. Conclusion: our study showed a modest increase of the F/M ratio (from 1.8 to 2.7) over time among patients affected by MS in Argentina.

11 páginas, 14 figuras, 3 tablas.-- El Pdf del artículo es la versión pre-print. This paper presents an aircraft attitude and heading estimator using catadioptric images as a principal sensor for UAV or as a redundant system for IMU (Inertial Measure Unit) and gyro sensors. First, we explain how the unified theory for central catadioptric cameras is used for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then, we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Finally the tests and results using the UAV COLIBRI platform and the validation of them in real flights are presented, comparing the estimated data with the inertial values measured on board. The authors would like to thank Jorge Leon for supporting the flight trials, the I.A. Institute-CSIC for collaborating in the flights, and the Universidad Politécnica de Madrid, and Consejería de Educación de la Comunidad de Madrid and the Fondo Social Europeo (FSE) for the authors' Ph.D. funding. This work has been sponsored by the Spanish Science Technology Ministry under the Grant CICYT DPI 2007-66156.

Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy the different requirements of the inspection: simultaneously detect transmission towers, check for defects, and analyze security distances. Following this direction, this paper proposes a supervised learning approach for solving the tower detection and classification problem, where HOG (Histograms of Oriented Gradients) features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds. In the different evaluations, highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.

Adenoviral conjuctivitis is one of the most common causes of acute red eye. Other diagnostic considerations include herpes virus conjunctivitis, chlamydial conjunctivitis, allergic conjunctivitis, and various other less common infections. Careful history taking can help in identifying a viral cause. The presentation may range from a minor conjunctivitis resulting from an upper respiratory tract infection to a serious, debilitating epidemic keratoconjunctivitis. Local care and interventions to minimize transmission are the cornerstones of management. Infection is usually self-limiting. Warm soaks and artificial tear lubricants may relieve itching and burning. Patients should be instructed to avoid touching their eyes, wash hands often, use disposable towels, and avoid group activities for as long as an ocular discharge is present. Use of topical corticosteroids or antibacterial preparations can lead to complications, and injudicious use of topical corticosteroids may mask serious conditions that require other interventions.

Autonomous aerial refueling is a key enabling technology for both manned and unmanned aircraft where extended flight duration or range are required. The results presented within this paper offer one potential vision-based sensing solution, together with a unique test environment. A hierarchical visual tracking algorithm based on direct methods is proposed and developed for the purposes of tracking a drogue during the capture stage of autonomous aerial refueling, and of estimating its 3D position. Intended to be applied in real time to a video stream from a single monocular camera mounted on the receiver aircraft, the algorithm is shown to be highly robust, and capable of tracking large, rapid drogue motions within the frame of reference. The proposed strategy has been tested using a complex robotic testbed and with actual flight hardware consisting of a full size probe and drogue. Results show that the vision tracking algorithm can detect and track the drogue at real-time frame rates of more than thirty frames per second, obtaining a robust position estimation even with strong motions and multiple occlusions of the drogue.

For the purposes of achieving Autonomous Air-to-Air Refuelling (AAAR) in Unmanned Aerial Vehicles (UAVs), this paper presents the use of a visual tracking algorithm based on direct methods and image registration techniques, with the aim of solving the drogue tracking problem in order to obtain vision-based relative position estimations of the aircrafts for the probe and drogue refuelling method. Proposed vision-based AAAR approaches to date have explored the use of features (such as corners, painted marks, or LEDs) to detect and estimate the relative motion of either the receiver or the tanker aircraft, with the drawback that sometimes this requires the installation of specific hardware on-board. Conversely, the strategy we propose to use does not require the installation of additional hardware on-board. The strategy is based on a hierarchical implementation of an image registration technique: the Inverse Compositional Image Alignment ICIA. Real images and real flight hardware (probe and drogue) are used to test the algorithm using a robotic testbed that simulates the motion of the aircrafts (the tanker and the receiver) during the refuelling task. Results show that the tracking algorithm is robust to fast motion, changes in appearance, and situations where part of the drogue is occluded or is outside of the field of view of the camera. Additionally, results show that robust position estimations at real-time frame rates are obtained, proving that the technique is fast enough to form the basis for automated aerial refuelling sensing capabilities.

The goal of the work described in this chapter is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30 Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors.

In this paper, we apply a hierarchical tracking strategy of planar objects (or that can be assumed to be planar) that is based on direct methods for vision-based applications on-board UAVs. The use of this tracking strategy allows to achieve the tasks at real-time frame rates and to overcome problems posed by the challenging conditions of the tasks: e.g. constant vibrations, fast 3D changes, or limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations in which part of the object to track is outside of the field of view of the camera. The performance of the proposed tracking strategy on-board UAVs is evaluated with images from real-flight tests using manually-generated ground truth information, accurate position estimation using a Vicon system, and also with simulated data from a simulation environment. Results show that the hierarchical tracking strategy performs better than well-known feature-based algorithms and well-known configurations of direct methods, and that its performance is robust enough for vision-in-the-loop tasks, e.g. for vision-based landing tasks. © 2013 Springer Science+Business Media Dordrecht.

The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors.

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi- Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles’ state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle’s state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle’s state for more than one minute, at real-time frame rates based, only on visual information.

This paper presents a Fuzzy Control application for a landing task of an Unmanned Aerial Vehicle, using the 3D-position estimation based on visual tracking of piecewise planar objects. This application allows the UAV to land on scenarios in which it is only possible to use visual information to obtain the position of the vehicle. The use of the homography permits a realtime estimation of the UAV's pose with respect to a helipad using a monocular camera. Fuzzy Logic allows the definition of a model-free control system of the UAV. The Fuzzy controller analyzes the visual information to generate altitude commands for the UAV to develop the landing task.

This article presents a real time Unmanned Aerial Vehicles UAVs 3D pose estimation method using planar object tracking, in order to be used on the control system of a UAV. The method explodes the rich information obtained by a projective transformation of planar objects on a calibrated camera. The algorithm obtains the metric and projective components of a reference object (landmark or helipad) with respect to the UAV camera coordinate system, using a robust real time object tracking based on homographies. The algorithm is validated on real flights that compare the estimated data against that obtained by the inertial measurement unit IMU, showing that the proposed method robustly estimates the helicopter's 3D position with respect to a reference landmark, with a high quality on the position and orientation estimation when the aircraft is flying at low altitudes, a situation in which the GPS information is often inaccurate. The obtained results indicate that the proposed algorithm is suitable for complex control tasks, such as autonomous landing, accurate low altitude positioning and dropping of payloads.

A neural behavior initiating agent (BIA) is proposed to integrate relevant compressed image information coming from others cooperating and specialized neural agents. Using this arrangement the problem of tracking and recognizing a moving icon has been solved by partitioning it into three simpler and separated tasks. Neural modules associated to those tasks proved to be easier to train and show a good general performance. The obtained neural controller can handle spurious images and solve an acute image related task in a dynamical environment. Under prolonged dead-lock conditions the controller shows traces of genuine spontaneity. The overall performance has been tested using a pan and tilt camera platform and real images taken from several objects, showing the good tracking results discussed in the paper.

In this paper we introduce a real-time trinocular system to control rotary wing Unmanned Aerial Vehicles based on the 3D information extracted by cameras located on the ground. The algorithm is based on key features onboard the UAV to estimate the vehicle's position and orientation. The algorithm is validated against onboard sensors and known 3D positions, showing that the proposed camera configuration robustly estimates the helicopter's position with an adequate resolution, improving the position estimation, especially the height estimation. The obtained results show that the proposed algorithm is suitable to complement or replace the GPS-based position estimation in situations where GPS information is unavailable or where its information is inaccurate, allowing the vehicle to develop tasks at low heights, such as autonomous landing, take-off, and positioning, using the extracted 3D information as a visual feedback to the flight controller.