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Resumo Na primeira infância, a poluição do ar leva a um aumento das doenças, mortes prematuras e perturbações no desenvolvimento. O material particulado fino (MP2,5) é considerado o poluente clássico de maior importância. No entanto, no Brasil, o monitoramento de material particulado fino é limitado e restrito a poucas cidades. Na cidade do Rio de Janeiro, apenas uma estação registra as concentrações de MP2,5 e divulga diariamente os índices de qualidade do ar. Este estudo é parte do projeto “Ares Novos para a Primeira Infância” coordenado pela Fundação Horizonte Cidadão, com o objetivo de obter evidências para estabelecer um plano para reduzir a exposição das crianças à poluição do ar. Os dados são compartilhados numa plataforma virtual global. Os resultados mostram níveis de MP2,5 altos quando comparados com as recomendações da OMS e a necessidade de ações coordenadas para reduzir as emissões de poluentes primários e de precursores de poluentes secundários.

Automatic depalletizing is becoming a practice widely applied in warehouses to automatize and speed-up logistics. On the other hand, the necessity to adapt the preexisting logistic lines to a custom automatic system can be a limit for the application of robotic solutions into smaller facilities like supermarkets. In this work, we tackle this issue by proposing a flexible and adaptive gripper for robotic depalletizing. The gripper is designed to be assembled on the end-tip of an industrial robotic arm. A novel patent-pending mechanism allows grasping boxes and products from both the upper and the lateral side enabling the depalletizing of boxes with complex shape. Moreover, the gripper is reconfigurable with five actuated degrees of freedom, that are automatically controlled using the embedded sensors to adapt grasping to different shapes and weights.

Volatile organic compounds (VOCs) and nitrogen oxides (NOx) play a key role in tropospheric chemistry. A typical urban scenario, under VOC-controlled conditions (low VOC/NOx ratios), is discussed with emphasis on the kinetic conditions leading to high ozone concentrations for an urban area of Brazil. As a base case, kinetic and mechanistic reactivities of individual VOCs, were evaluated in the district of Iraja (Rio de Janeiro, Brazil), which receives air masses originating from an industrial area. Aromatic compounds contributed with 41.1% of total mass. They were the most reactive species, representing 48.8% of the total kinetic reactivity and 71.0, 61.0 and 55.9% of the mechanistic reactivity, in MIR, MOIR and EBIR scales, respectively. Results showed that high ozone concentrations are due to a combination of conditions, mainly, relatively low NOx levels and a relatively high reactive VOC mix, primarily originating from an industrial area. Average ozone forming potential for the samples were calculated as 3.57, 1.39 and 0.74 for the MIR, MOIR and EBIR scales, respectively.

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This paper presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the Saturation in the Null Space (SNS) algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported., Accepted to Transactions on Robotics (T-RO). Final version in preparaton. 20 pages, 19 figures

In this chapter, the design of nonlinear controllers for non-prehensile holonomic rolling system is reviewed. A general model for the class of non-prehensile rolling system considered in this work is first formulated. Then, both the input-state linearisation approach and the interconnection and damping assignment passivity-based control technique for rolling systems are addressed. The class of control designs presented in this chapter make use of energy concepts and physical properties. Three benchmark examples are used to illustrate the control design presented, namely the disk-on-disk, the ball-and-beam, and the eccentric disk-on-disk This chapter is based on the works presented in [1–3].

Stretching a pizza dough with a rolling pin is a nonprehensile manipulation. Since the object is deformable, force closure cannot be established, and the manipulation is carried out in a nonprehensile way. The framework of this pizza dough stretching application that is explained in this chapter consists of four sub-procedures: (i) recognition of the pizza dough on a plate, (ii) planning the necessary steps to shape the pizza dough to the desired form, (iii) path generation for a rolling pin to execute the output of the pizza dough planner, and (iv) inverse kinematics for the bi-manual robot to grasp and control the rolling pin properly. Using the deformable object model described in Chap. 3, each sub-procedure of the proposed framework is explained sequentially.

The sliding primitive is a ubiquitous nonprehensile manipulation task, generally performed by mechanical systems represented by underactuated nonlinear models. A literature review of the recent works dealing with this task is first introduced. Then, a particular nonprehensile manipulation task that has arisen in the framework of the RoDyMan project, i.e., a pizza and peel mechanical system, is addressed. A more in-depth study is presented for this system, including modelling, control, and stability analysis. Finally, a discussion on the current achievements and some directions for future work is provided. This chapter is based on the work presented in [1].

Materials like fluids are long since important research objects of continuum mechanics as well as of computer graphics. Smoothed particle hydrodynamics(SPH) is one of the representation methods employed for continuous materials. Its simplicity in implementation and its realistic representation are drastically improved during the last decades. More recently, highly viscous fluids like honey, jam, and bread dough based on the SPH formulation have gained attention with impressive results. In this chapter, a novel implicit viscosity method is proposed. The internal viscosity forces are recursively calculated from the difference of the nearby velocities of the particles until they are small enough to be neglected. The proposed approach has longer time-steps compared with existing explicit viscosity methods, resulting in shorter computation time. Besides, the proposed method uses a physical viscosity coefficient, not an artificial one like in existing implicit viscosity methods, which helps predict the viscous behavior of continuous materials more accurately. The obtained results show that the computational time for the proposed approach is pre- dictable, while the accuracy in modelling the viscosity behaviour is similar or higher than existing methods.

This chapter reviews the problem of nonholonomic rolling in nonprehen- sile manipulation tasks through two challenging and illustrative examples: the robotic hula-hoop and the ballbot system. The hula-hoop consists of an actuated stick and an unactuated hoop. First, the corresponding kinematic model is derived. Second, the dynamic model is derived through the Lagrange-D’Alembert equations. Then a control strategy is designed to rotate the hoop at some desired constant speed whereas positioning it over a desired point on the stick surface. A stability analysis, which guarantees ultimate boundedness of all signals of interest, is carried out. The ball-bot is an underactuated and nonholonomic constrained mobile robot whose upward equilibrium point must be stabilised by active controls. Coordinate-invariant equations of motion are derived for the ballbot. The linearised equations of motion are then derived, followed by the detailed controllability analysis. Excluding the rotary degree of freedom of the ball in the inertial vertical direction, the linear system turns out to be controllable. It follows that the nonlinear system is locally controllable, and a proportional-derivative type controller is designed to locally exponentially stabilise the upward equilibrium point and the translation of the ball. Numerical simulations for these two examples illustrate the effectiveness of the proposed methods. This chapter is based on the works presented in [1–4].

Nowadays haptic devices have lots of applications in virtual reality systems. While using a haptic device, one of the main requirements is the stable behavior of the system. An unstable behavior of a haptic device may damage itself and even may hurt its operator. Stability of haptic devices in the presence of inevitable time delay in addition to a suitable zero-order hold is studied in the presented paper, using two different methods. Both presented methods are based on Lyapunov-Krazuvskii functional. In the first method, a model transform is performed to determine the stability boundary, while the second approach is based on Free Weighing Matrices (FWMs). Delay-dependent stability criteria are determined by solving Linear Matrix Inequalities (LMIs). Results of these two methods are compared with each other and verified by simulations as well as experiments on a KUKA Light Weight Robot 4 (LWR4). It is concluded that using free weighing matrices leads to more unknown parameters and needs more calculation, but its results are less conservative.

This paper presents a new procedure to design a control law using the classical interconnection and damping assignment technique within the passivity-based port-Hamiltonian framework. The sought goal is to reduce the complexity of solving the so-called matching equations. The proposed approach is applied to two case studies of planar rolling nonprehensile manipulation, namely, the ball-and-beam and the eccentric disk-on-disk. The performance of the resulting controllers is illustrated through both simulations and experimental results, showing the applicability of the design in a real setup.

International audience; Today, physical Human-Robot Interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, Aerial Physical Interaction (APhI) is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach that makes pHRI safe. A controller, inspired by the literature on flexible manipulators, computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to non-idealities like additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI.

Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensory information for autonomous object interaction. To address such limitations, this research proposes to augment the previously developed kernelized synergies framework with visual perception to automatically adapt to the unknown objects. The kernelized synergies, inspired from humans, retain the same reduced subspace for object grasping and manipulation. To detect object in the scene, a simplified perception pipeline is used that leverages the RANSAC algorithm with Euclidean clustering and SVM for object segmentation and recognition respectively. Further, the comparative analysis of kernelized synergies with other state of art approaches is made to confirm their flexibility and effectiveness on the robotic manipulation tasks. The experiments conducted on the robot hand confirm the robustness of modified kernelized synergies framework against the uncertainties related to the perception of environment.

The COVID-19 pandemic is forcing a rethink in robotics. In the form it is known today, robotics has been the prerogative of a broad community of insiders. But now, in the wreckage left behind by COVID-19, a new era is beginning. What does it hold? During the pandemic, increasing numbers of people had manifested the hope that robotics might bring novel solutions. And this interest has emerged beyond the usual boundaries of the experts or technology enthusiasts. This provides an opportunity to reinforce the community of people involved in the process of innovation. By involving citizens, the community becomes more comprehensive (that is, plural and diverse). This broadening will involve more practical knowledge and therefore produce better robots of many shapes and functions. If progress is possible in the industry, why not in the hospitals, shopping malls, restaurants, hotels, and schools? What is more, the approach endorsed by the Italian robotics community during the lockdown has established a new cooperation among those who labor with robots, and the professionals who work in hospitals, which is bound to last a long time. As a major impact, this experience will enable an improvement on science's relationship with and for society. This may entail a further shift: to value more scientific knowledge and scientific literacy.

International audience; Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system. This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the paper, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter.

Mathematical modeling of hybrid soft robots is complicated by the description of the complex shape that they undergone when subject to actuation and external loads. It might be noticed that several approaches have been used so far in robotics, and the problem is not yet fully solved. This short paper aims at presenting an overview of modeling and simulation approaches for soft robots based on finite element methods. Benefits and perspectives of future directions are also discussed.

Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands, increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the re-usability of same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies is parameterized by kernelized movement primitives to preserve its grasping and manipulation characteristics and allows its reuse for new objects. The grasp stability of proposed framework is assessed with the force closure quality index, as a cost function. For performance evaluation, the proposed framework is initially tested on two different simulated robot hand models using the Syngrasp toolbox and experimentally, four complex grasping and manipulation tasks are performed and reported. Results confirm the hand agnostic approach of proposed framework and its generalization to distinct objects irrespective of their dimensions.

The PRISMA Hand II is a mechanically robust anthropomorphic hand developed at PRISMA Lab, University of Naples Federico II. The hand is highly underactuated, three motors drive 19 joints via elastic tendons. Thanks to its particular mechanical design, the hand can perform not only adaptive grasps but also in-hand manipulation. Each fingertip integrates a tactile/force sensor, based on optoelectronic technology, to provide tactile/force feedback during grasping and manipulation, particularly useful with deformable objects. The paper briefly describes the mechanical design and sensor technology of the hand and proposes a calibration procedure for tactile/force sensors. A comparison between different models of Neural Networks architectures, suitable for sensors calibration, is shown. Experimental tests are provided to choose the optimal tactile sensing suite. Finally, experiments for the regulation of the forces are made to show the effectiveness of calibrated sensors.

In this work we perform a manipulability analysis in the context of a surgical task subject to a remote center of motion constraint, https://youtu.be/WpmbEzYEIb0

Modern robots act in dynamic and partially unknown environments where path replanning can be mandatory if changes in the environment are observed. Task-prioritized control strategies are well known and effective solutions to ensure local adaptation of robot behaviour. The highest priority in a stack of tasks is typically given to the management of correct robot operation or safe interaction with the environment such as obstacles or joint limits avoidance, that we can consider as constraints. If a constraint makes impossible achieving a certain task, such as tracking a Cartesian trajectory, a local control algorithm partially sacrifices the latter which is only accomplished to the best of the robot's ability to generate internal motions. In this control framework, problems may occur in some applications, like in the surgical domain, where it is not safe that some tasks are simply sacrificed without prior notice. The contribution of this work is to introduce a coordinate invariant index, that is used to provide a geometrical interpretation of task conflicts in a task-priority control framework and to develop a method for on-line detection of algorithmic singularities, with the goal of increasing safety and performances during robot operations.

The first COVID-19 case in Brazil was confirmed on February 25, 2020. Partial lockdown measures came into force in the city of Rio de Janeiro, Brazil, on March 23. While CO and NO2 levels showed significant reductions, PM10 levels were only reduced in the first partial lockdown week. By contrast, ozone levels increased in all studied locations. In this study, the factors leading to this behavior were analyzed. Monitoring data obtained at two automatic monitoring stations showed higher ratios between non-methane hydrocarbons and nitrogen oxides (NMHC/NOx) during the partial lockdown (up to 37.3%). The increase in ozone concentrations during the social distancing measures could be attributed to the increase in NMHC/NOx ratios since atmospheric chemistry in Rio de Janeiro is under VOC-controlled conditions. However, the increase was higher when air masses arrived from the industrial areas, not only because of the higher NMHC/NOx ratios, but also because the reactivity of VOC was highly increased by these air masses, which are rich in aromatic compounds., Graphical abstract Unlabelled Image, Highlights • Primary atmospheric pollutant concentrations decreased during the lockdown. • Ozone levels increased during lockdown in many cities of the world. • Ozone levels were related to the increase in NMHC/NOx ratios in a VOC-controlled scenario. • Air masses from the industrial areas contributed to ozone increase.

As COVID-19 spread all over the world, most of the countries adopted some kind of restrictions to avoid the collapse of health systems. In Brazil, São Paulo and Rio the Janeiro, the two most populated cities in the country, were the first to determine social distancing. In this study, the impact of the social distancing measures on the concentrations of the three main primary air pollutants (PM10, NO2 and CO) was analyzed. CO levels showed the most significant reductions (up to 100%) since it is related to light-duty vehicular emissions. NO2 also showed reductions (9.1%–41.8%) while PM10 levels were only reduced in the 1st lockdown week. The decrease of pollutants was not directly proportional to the vehicular flux reduction, because it depends on other factors such as the transport of air masses from industrial and rural areas. The differences observed can be explained considering the fleet characteristics in the two cities and the response of the population to the social distancing recommendations.

This article focuses on ethical issues raised by increasing levels of autonomy for surgical robots. These ethical issues are explored mainly by reference to state-ofart case studies and imminent advances in Minimally Invasive Surgery (MIS) and Microsurgery. In both area, surgicalworkspace is limited and the required precision is high. For this reason, increasing levels of robotic autonomy can make a significant difference there, and ethically justified control sharing between humans and robots must be introduced. In particular, from a responsibility and accountability perspective suitable policies for theMeaningfulHuman Control (MHC) of increasingly autonomous surgical robots are proposed. It is highlighted how MHC should be modulated in accordance with various levels of autonomy for MIS and Microsurgery robots. Moreover, finer MHC distinctions are introduced to deal with contextual conditions concerning e.g. soft or rigid anatomical environments. Keywords: RAS,MIS; microsurgery; ethics; meaningful human control; shared-control; human-robot interaction

Coordinates play an essential role in the description of real world objects and physical processes. In robotics, coordinates are used to describe the kinematic structure and the kinematic and dynamic behavior. The description mostly takes place in charts, assigned by the observer of the robotic system. However, it is crucial that the described physical process does not depend on the coordinate choice of the observer. In this work we show the relation between coordinates and manipulability analysis. Manipulability measures are dependent of joint coordinates of the robot and task coordinates in the workspace of the robot. Both relations can be analyzed with tensor geometry. We remove the dependency on joint coordinates through the use of an appropriate metric. With the help of tensor contraction, the resulting induced metric in the workspace can be transformed into a coordinate invariant matrix. After applying eigenvalue decomposition on this matrix, we can visualize the dynamic manipulability of a robot as a coordinate invariant ellipsoid.

International audience; We propose novel haptic guidance methods for a dual-arm telerobotic manipulation system, which are able to deal with several different constraints, such as collisions, joint limits, and singularities. We combine the haptic guidance with shared-control algorithms for autonomous orientation control and collision avoidance meant to further simplify the execution of grasping tasks. The stability of the overall system in various control modalities is presented and analyzed via passivity arguments. In addition, a human subject study is carried out to assess the effectiveness and applicability of the proposed control approaches both in simulated and real scenarios. Results show that the proposed haptic-enabled shared-control methods significantly improve the performance of grasping tasks with respect to the use of classic teleoperation with neither haptic guidance nor shared control.

The goal of this article is to disseminate the planning and control strategies for robotic nonprehensile manipulation results achieved so far with the Robotic Dynamic Manipulation (RoDyMan) project. The goal of the project is to advance the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to enhance the possibility of employing robots in anthropic environments. RoDyMan project's final demonstration will be acting as an autonomous pizza maker. This article highlights the lessons learned and paves the way toward critical discussions and future research.

A mechanical system consisting of a hoop and a pole is considered, for which the corresponding dynamic model represents an underactuated system subject to second-order nonholonomic constraints. The pursued goal is to simultaneously track a trajectory in the unactuated coordinates and to stabilize the actuated ones. For the model under consideration, the well-known noncollocated partial feedback linearization algorithm fails since the corresponding zero dynamics is unstable. In this work, we show that the actuated coordinates, i.e., the pole can be stabilized by exploiting the null space of the coupling inertia matrix without affecting the performance in the underactuated coordinates tracking. We present a formal mathematical analysis, which guarantees ultimate boundedness of all coordinates. Performed simulations bolster the proposed approach.

The contact kinematics of a robotic hoop and a pole system is obtained by using the Montana’s equations, considering the case of contact without slipping. The resulting kinematic model is completely nonholonomic. After some mild assumptions, a set of Pfaffian constraints is established. Then, a dynamic model of the system is developed by employing the Lagrange– d’Alembert formulation. This dynamic model, which represents an underactuated mechanical system, is later used to design a controller which does not need velocity measurements. The proposed method, and its robustness against model uncertainties, is validated through numeric simulations.

Purpose The purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented. Design/methodology/approach Cooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied. Findings The results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved. Practical implications A simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs. Originality/value The basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

On November 19, 2018, 30 years after establishment, the updated Brazilian National Air Quality Standards (NAQS) were published. These NAQS were formulated as a fundamental part and instrument of the National Program for the Control of Air Quality and considering, as a reference, the World Health Organization Air Quality Guidelines, published in 2005. An important contribution is the inclusion of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) as a criteria pollutant and a more restrictive target limit for PM10 (particles that have aerodynamic diameters less than or equal to 10 µm). In this work, the NAQS were discussed using, as a case study, data collected during the 2016 Olympic and Paralympic Games. The lack of PM2.5 data for Brazilian cities results in the calculation of a lower air quality index (AQI), leading to ostensibly good air quality. The results presented in this study clearly support the requirement of improvement of the new resolution since, in the present form, it does not meet the main goal of protecting public health.

Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown.

This chapter aims the broadcasting of the results achieved by the RoDyMan project about the task planning manipulation of deformable objects, and the nonprehensile manipulation control. The final demonstrator of the project is a pizza-making process. After an introduction to the general topic of nonprehensile manipulation, the mechatronic design and the high-level software architecture are described. Then, the smoothed particle hydrodynamic formulation is briefly introduced, along with the description of a detection method for a deformable object. The task planning for stretching a modelling clay, emulating the pizza dough, is sketched. After, the problematic control objective is split into several nonprehensile motion primitives: holonomic and nonholonomic rolling, friction-induced manipulation, and tossing are the described primitives. This chapter highlights the achievements reached so far by the project, and pave the way towards future research directions.

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments.

This letter addresses the task of stabilizing a wheeled unmanned aerial vehicle on a pipe, which is an emerging application in oil and gas facilities for nondestructive measurements. After the derivation of the dynamic model of the system, a discrete-time nonlinear model predictive controller is designed over a finite horizon. The analysis of the asymptotic stability of the designed controller is carried out. Numerical tests show the performance and the robustness of the proposed solution.