RobotAtFactory 4.0: a ROS framework for the SimTwo simulator

Robotics competitions encourage the development of solutions to new challenges that emerge in sync with the rise of Industry 4.0. In this context, robotic simulators are employed to facilitate the development of these solutions by disseminating knowledge in robotics, Education 4.0, and STEM. The RobotAtFactory 4.0 competition arises to promote improvements in industrial challenges related to autonomous robots. The official organization provides the simulation scene of the competition through the open-source SimTwo simulator. This paper aims to integrate the SiwTwo simulator with the Robot Operating System (ROS) middleware by developing a framework. This integration facilitates the design of robotic systems since ROS has a vast repository of packages that address common problems in robotics. Thus, competitors can use this framework to develop their solutions through ROS, allowing the simulated and real systems to be integrated.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI20/11780028.info:eu-repo/semantics/publishedVersio

Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.The authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. The authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança–IPB (UIDB/05757/2020 and UIDP/05757/2020), the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, and Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) and IPB, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Large-scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing

Cooperative heterogeneous robots for autonomous insects trap monitoring system in a precision agriculture scenario

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.The authors would like to thank the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). In addition, the authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. In addition, the authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Braganca (IPB) - Campus de Santa Apolonia, Portugal, Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Portugal, INESC Technology and Science - Porto, Portugal and Universidade de Trás-os-Montes e Alto Douro - Vila Real, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation used to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Artificial intelligence architecture based on planar LIDAR scan data to detect energy pylon structures in a UAV autonomous detailed inspection process

The technological advances in Unmanned Aerial Vehicles (UAV) related to energy power structure inspection are gaining visibility in the past decade, due to the advantages of this technique compared with traditional inspection methods. In the particular case of power pylon structure and components, autonomous UAV inspection architectures are able to increase the efficacy and security of these tasks. This kind of application presents technical challenges that must be faced to build real-world solutions, especially the precise positioning and path following for the UAV during a mission. This paper aims to evaluate a novel architecture applied to a power line pylon inspection process, based on the machine learning techniques to process and identify the signal obtained from a UAV-embedded planar Light Detection and Ranging - LiDAR sensor. A simulated environment built on the GAZEBO software presents a first evaluation of the architecture. The results show an positive detection accuracy level superior to 97% using the vertical scan data and 70% using the horizontal scan data. This accuracy level indicates that the proposed architecture is proper for the development of positioning algorithms based on the LiDAR scan data of a power pylon.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. This work has also been supported by Fundação Araucária (grant 34/2019), and by CAPES and UTFPR through stundent scholarships.info:eu-repo/semantics/publishedVersio

Large-scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.Hertz Foundatio

Vertical Transmission of Mycoplasma pneumoniae Infection

Mycoplasma pneumoniae is a significant cause of pneumonia in school-aged children and young adults. We report a case of neonatal M. pneumoniae pneumonia in a preterm child manifesting in the first hours of life. Vertical transmission was demonstrated by the detection of M. pneumoniae in inflamed placental tissue indicating chorioamnionitis

Illness Labels and Social Distance

The authors examine a key proposition in the modified labeling theory—that a psychiatric label increases vulnerability to negative evaluation and social rejection—using an experimental design wherein female participants interact with a female teammate over a computer. The authors also evaluate a hypothesis derived from the disease-avoidance account of disgust by examining this same process for a nonpsychiatric illness: food poisoning. In addition, they introduce a composite measure of social distance behavior that is easy to implement in a laboratory experiment. The authors find, as predicted, that women seek greater social distance from teammates with a history of psychiatric or food poisoning hospitalization than they do from teammates with no hospitalization history. But, contrary to predictions, a teammate’s hospitalization history does not affect participants’ ratings of her likability. The results also do not vary significantly by psychiatric diagnosis (depression vs. schizophrenia), suggesting that the stigma of depression may be just as strong as the stigma of schizophrenia when information about symptoms is not available. The authors discuss the implications of these findings for the modified labeling theory of mental illness and for the literature on disgust and stigma. They also outline avenues for future research.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Search for Λc+→ϕpπ0\Lambda_c^+\to\phi p \pi^0 and branching fraction measurement of Λc+→K−π+pπ0\Lambda_c^+\to K^-\pi^+ p \pi^0

We have searched for the Cabibbo-suppressed decay $\Lambda_c^+\to\phi p\pi^0$ in $e^+e^-$ collisions using a data sample corresponding to an integrated luminosity of 915 $\rm fb^{-1}$. The data were collected by the Belle experiment at the KEKB $e^+e^-$ asymmetric-energy collider running at or near the $\Upsilon(4S)$ and $\Upsilon(5S)$ resonances. No significant signal is observed, and we set an upper limit on the branching fraction of $\mathcal{B}(\Lambda_c^+\to \phi p\pi^0) <15.3\times10^{-5}$ at 90% confidence level. The contribution for nonresonant $\Lambda_c^+\to K^+K^- p\pi^0$ decays is found to be consistent with zero and the corresponding upper limit on its branching fraction is set to be $\mathcal{B}(\Lambda_c^+\to K^+K^-p\pi^0)_{\rm NR} <6.3\times10^{-5}$ at 90% confidence level. We also measure the branching fraction for the Cabibbo-favored decay $\Lambda_c^+\to K^-\pi^+p\pi^0$; the result is $\mathcal{B}(\Lambda_c^+\to K^-\pi^+p\pi^0)= (4.42\pm0.05\, (\rm stat.) \pm 0.12\, (\rm syst.) \pm 0.16\, (\mathcal{B}_{\rm Norm}))\%$, which is the most precise measurement to date. Finally, we have searched for an intermediate hidden-strangeness pentaquark decay $P^+_s\to\phi p$. We see no evidence for this intermediate decay and set an upper limit on the product branching fraction of ${\cal B}(\Lambda_c^+\to P^+_s \pi^0)\times {\cal B}(P^+_s\to\phi p) <8.3\times 10^{-5}$ at 90% confidence level.Comment: 8 pages, 5 figures, 1 table, minor text change in version

Measurement of the τ\tau lepton polarization and R(D∗)R(D^*) in the decay Bˉ→D∗τ−νˉτ\bar{B} \rightarrow D^* \tau^- \bar{\nu}_\tau with one-prong hadronic τ\tau decays at Belle

With the full data sample of $772 \times 10^6$ $B{\bar B}$ pairs recorded by the Belle detector at the KEKB electron-positron collider, the decay $\bar{B} \rightarrow D^* \tau^- \bar{\nu}_\tau$ is studied with the hadronic $\tau$ decays $\tau^- \rightarrow \pi^- \nu_\tau$ and $\tau^- \rightarrow \rho^- \nu_\tau$. The $\tau$ polarization $P_\tau(D^*)$ in two-body hadronic $\tau$ decays is measured, as well as the ratio of the branching fractions $R(D^{*}) = \mathcal{B}(\bar {B} \rightarrow D^* \tau^- \bar{\nu}_\tau) / \mathcal{B}(\bar{B} \rightarrow D^* \ell^- \bar{\nu}_\ell)$, where $\ell^-$ denotes an electron or a muon. Our results, $P_\tau(D^*) = -0.38 \pm 0.51 {\rm (stat)} ^{+0.21}_{-0.16} {\rm (syst)}$ and $R(D^*) = 0.270 \pm 0.035{\rm (stat)} ^{+0.028}_{-0.025}{\rm (syst)}$, are consistent with the theoretical predictions of the Standard Model. The polarization values of $P_\tau(D^*) > +0.5$ are excluded at the 90\% confidence level.Comment: 17 pages, 11 figures, submitted to Physical Review