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    A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario
    ( 2021) Santos,J ; Paulo Miranda Rebelo ; Luís Freitas Rocha ; Pedro Gomes Costa ; Germano Veiga ; 5159 ; 5364 ; 5674 ; 7077
    A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.
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    Accuracy and Repeatability Tests on HoloLens 2 and HTC Vive
    ( 2021) Soares,I ; Ricardo Barbosa Sousa ; Marcelo Petry ; António Paulo Moreira ; 5157 ; 5240 ; 7908
    Augmented and Virtual Reality have been experiencing a rapidly growth in recent years, but there is not still a deep knowledge on their capabilities and where they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of the Microsoft's HoloLens 2 (Augmented Reality device) and HTC Vive (Virtual Reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, while in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimetre scale, while the HoloLens 2 revealed not so accurate measures (around 2 centimetres). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not an inherit controller made a huge impact. The results were considered a significant step for the on going project of developing a human-robot interface to program by demonstration an industrial robot using Extended Reality, which shows great potential to succeed based on this data.
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    Active Perception Fruit Harvesting Robots - A Systematic Review
    ( 2022) Sandro Augusto Magalhães ; António Paulo Moreira ; Filipe Neves Santos ; Dias,J ; 5157 ; 7481 ; 5552
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    Adaptive Portfolio Optimization for Multiple Electricity Markets Participation
    ( 2016) Pinto,T ; Morais,H ; Sousa,TM ; Sousa,T ; Vale,Z ; Praca,I ; Faia,R ; Eduardo Pires
    The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.
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    AdaptPack studio translator: translating offline programming to real palletizing robots
    ( 2020) Silva,MF ; João Pedro Souza ; Castro,AL ; Luís Freitas Rocha ; 5364 ; 7366
    Purpose This paper aims to propose a translation library capable of generating robots proprietary code after their offline programming has been performed in a software application, named AdaptPack Studio, running over a robot simulation and offline programming software package. Design/methodology/approach The translation library, named AdaptPack Studio Translator, is capable to generate proprietary code for the Asea Brown Boveri, FANUC, Keller und Knappich Augsburg and Yaskawa Motoman robot brands, after their offline programming has been performed in the AdaptPack Studio application. Findings Simulation and real tests were performed showing an improvement in the creation, operation, modularity and flexibility of new robotic palletizing systems. In particular, it was verified that the time needed to perform these tasks significantly decreased. Practical implications The design and setup of robotics palletizing systems are facilitated by an intuitive offline programming system and by a simple export command to the real robot, independent of its brand. In this way, industrial solutions can be developed faster, in this way, making companies more competitive. Originality/value The effort to build a robotic palletizing system is reduced by an intuitive offline programming system (AdaptPack Studio) and the capability to export command to the real robot using the AdaptPack Studio Translator. As a result, companies have an increase in competitiveness with a fast design framework. Furthermore, and to the best of the author’s knowledge, there is also no scientific publication formalizing and describing how to build the translators for industrial robot simulation and offline programming software packages, being this a pioneer publication in this area.
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    AdaptPack Studio: an automated intelligent framework for offline factory programming
    ( 2020) João Pedro Souza ; Manuel Santos Silva ; Luís Freitas Rocha ; Castro,AL ; 5655 ; 7366 ; 5364
    Purpose This paper aims to propose an automated framework for agile development and simulation of robotic palletizing cells. An automatic offline programming tool, for a variety of robot brands, is also introduced. Design/methodology/approach This framework, named AdaptPack Studio, offers a custom-built library to assemble virtual models of palletizing cells, quick connect these models by drag and drop, and perform offline programming of robots and factory equipment in short steps. Findings Simulation and real tests performed showed an improvement in the design, development and operation of robotic palletizing systems. The AdaptPack Studio software was tested and evaluated in a pure simulation case and in a real-world scenario. Results have shown to be concise and accurate, with minor model displacement inaccuracies because of differences between the virtual and real models. Research limitations/implications An intuitive drag and drop layout modeling accelerates the design and setup of robotic palletizing cells and automatic offline generation of robot programs. Furthermore, A* based algorithms generate collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. As a consequence, industrial solutions are available for production in record time, increasing the competitiveness of companies using this tool. Originality/value The AdaptPack Studio framework includes, on a single package, the possibility to program, simulate and generate the robot code for four different brands of robots. Furthermore, the application is tailored for palletizing applications and specifically includes the components (Building Blocks) of a particular company, which allows a very fast development of new solutions. Furthermore, with the inclusion of the Trajectory Planner, it is possible to automatically develop robot trajectories without collisions.
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    Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead
    ( 2021) Oliveira,LFP ; António Paulo Moreira ; Manuel Santos Silva ; 5157 ; 5655
    The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.
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    Advances in Forest Robotics: A State-of-the-Art Survey
    ( 2021) Oliveira,LFP ; António Paulo Moreira ; Manuel Santos Silva ; 5157 ; 5655
    The development of robotic systems to operate in forest environments is of great relevance for the public and private sectors. In this sense, this article reviews several scientific papers, research projects and commercial products related to robotic applications for environmental preservation, monitoring, wildfire firefighting, inventory operations, planting, pruning and harvesting. After conducting critical analysis, the main characteristics observed were: (a) the locomotion system is directly affected by the type of environmental monitoring to be performed; (b) different reasons for pruning result in different locomotion and cutting systems; (c) each type of forest, in each season and each type of soil can directly interfere with the navigation technique used; and (d) the integration of the concept of swarm of robots with robots of different types of locomotion systems (land, air or sea) can compensate for the time of executing tasks in unstructured environments. Two major areas are proposed for future research works: Internet of Things (IoT)-based smart forest and navigation systems. It is expected that, with the various characteristics exposed in this paper, the current robotic forest systems will be improved, so that forest exploitation becomes more efficient and sustainable.
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    Assessing physical activity intensity by video analysis
    ( 2015) Silva,P ; Santiago,C ; Reis,LP ; Armando Sousa ; Mota,J ; Welk,G
    Assessing physical activity (PA) is a challenging task and many different approaches have been proposed. Direct observation (DO) techniques can objectively code both the behavior and the context in which it occurred, however, they have significant limitations such as the cost and burden associated with collecting and processing data. Therefore, this study evaluated the utility of an automated video analysis system (CAM) designed to record and discriminate the intensity of PA using a subject tracking methodology. The relative utility of the CAM system and DO were compared with criterion data from an objective accelerometry-based device (Actigraph GT3X+). Eight 10 year old children (three girls and five boys) wore the GT3X+ during a standard basketball session. PA was analyzed by two observers using the SOPLAY instrument and by the CAM system. The GT3X+ and the CAM were both set up to collect data at 30 Hz while the DO was performed every two minutes, with 10s of observation for each gender. The GT3X+ was processed using cut points by Evanson and the outcome measure was the percentage of time spent in different intensities of PA. The CAM data were processed similarly using the same speed thresholds as were used in establishing the Evenson cut-off points (light: <2 mph; walking: 2-4 mph; very active: >4 mph). Similar outcomes were computed from the SOPLAY default analyses. A chi-square test was used to test differences in the percentage of time at the three intensity zones (light, walking and very active). The Yates' correction was used to prevent overestimation of statistical significance for small data. When compared with GT3X+, the CAM had better results than the SOPLAY. The chi-square test yielded the following pairwise comparisons: CAM versus GT3x+ was chi(2) (5) = 24.18, p < .001; SOPLAY2 versus GT3x+ was chi(2) (5) = 144.44, p < .001; SOPLAY1 versus GT3x+ was chi(2) (5) = 119.55, p < .001. The differences were smaller between CAM and GT3x+, suggesting that the video tracking system provided better agreement than DO. The small sample size precludes a definitive evaluation but the results show that the CAM video system may have promise for automated coding of physical activity behavior.
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    Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor
    ( 2016) Moreira,E ; Luís Freitas Rocha ; Andry Maykol Pinto ; António Paulo Moreira ; Germano Veiga
    This letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts. © 2016 IEEE.
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    Autonomous wheelchair for patient’s transportation on healthcare institutions
    ( 2021) André Rodrigues Baltazar ; Marcelo Petry ; Manuel Santos Silva ; António Paulo Moreira ; 5157 ; 5240 ; 5655 ; 7458
    AbstractThe transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.
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    Beat tracking for multiple applications: A multi-agent system architecture with state recovery
    ( 2012) Luís Paulo Reis ; João Lobato Oliveira ; Fabien Gouyon ; Matthew Davies
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    Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato
    ( 2022) Germano Filipe Moreira ; Sandro Augusto Magalhães ; Tatiana Martins Pinho ; Filipe Neves Santos ; Mário Cunha ; 5983 ; 7332 ; 7481 ; 8764 ; 5552
    The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.
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    Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models
    ( 2023) Sandro Augusto Magalhães ; Filipe Neves Santos ; Machado,P ; António Paulo Moreira ; Dias,J ; 5157 ; 7481 ; 5552
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    Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection
    ( 2021) André Silva Aguiar ; Monteiro,NN ; Filipe Neves Santos ; Eduardo Pires ; Daniel Queirós Silva ; Armando Sousa ; José Boaventura ; 5152 ; 5552 ; 5773 ; 5777 ; 7844 ; 8276
    The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.
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    Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection
    ( 2021) André Silva Aguiar ; Monteiro,NN ; Filipe Neves Santos ; Eduardo Pires ; Daniel Queirós Silva ; Armando Sousa ; José Boaventura ; 5152 ; 5552 ; 5773 ; 5777 ; 7844 ; 8276
    The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.
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    Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator
    ( 2017) Josenalde Barbosa Oliveira ; Paulo Moura Oliveira ; José Boaventura ; Tatiana Martins Pinho
    The use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal.
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    Collaborative Welding System using BIM for Robotic Reprogramming and Spatial Augmented Reality
    ( 2019) Carlos Miguel Costa ; Luís Freitas Rocha ; Malaca,P ; Pedro Gomes Costa ; António Paulo Moreira ; Tavares,P ; Armando Sousa ; Germano Veiga ; 6164 ; 5152 ; 5157 ; 5159 ; 5364 ; 5674
    The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality. © 2019 Elsevier B.V.