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Publicações

Publicações por José Lima

2021

A dobot manipulator simulation environment for teaching aim with forward and inverse kinematics

Autores
Brito, T; Lima, J; Braun, J; Piardi, L; Costa, P;

Publicação
Lecture Notes in Electrical Engineering

Abstract
Industrial Manipulators were becoming used more and more at industries since the third industrial revolution. Actually, with the fourth one, the paradigm is changing and the collaborative robots are being accepted for the community. It means that smaller manipulators with more functionalities have been used and installed. New approaches have appeared to teach students according to the new robot’s capabilities. The DOBOT robot is an example of that since it captivates the student’s attention with an uncomplicated programming front-end, tools, grippers and extremely useful for teaching STEM. This paper proposes a dynamic based simulation environment that can be used to teach, test and validate solutions to the DOBOT robot. By this way, the student can try and validate, at their homework without the real robot, the developed solutions and further test them at the laboratory with the real robot. Currently, remote testing and validation without the use of a real robot is an advantage. The comparison of the provided simulation environment and the real robot is presented in the approach. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

2022

Design and Modelling of a Modular Robotic Joint

Autores
Rocha, M; Pinto, VH; Lima, J; Costa, P;

Publicação
ROBOTICS FOR SUSTAINABLE FUTURE, CLAWAR 2021

Abstract
The industry tends to increasingly automate as many processes as possible, and to make this possible, they often resort to the use of robotic arms. This paper presents the development of a proposal for a modular joint for robotic arms that allows: to obtain the best possible torque/weight ratio; to be controlled in speed and/or position; to communicate with other joints and external microcontrollers; to keep the cost as low as possible; and to be easily reconfigurable. The proposed prototype was validated with real results.

2018

A genetic algorithm approach for the scheduling in a robotic-centric flexible manufacturing system

Autores
Pereira, AI; Ferreira, A; Barbosa, J; Lima, J; Leitão, P;

Publicação
Human-Centric Robotics- Proceedings of the 20th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2017

Abstract
Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process. © 2018 by World Scientific Publishing Co. Pte. Ltd.

2020

A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems

Autores
Brito T.; Queiroz J.; Piardi L.; Fernandes L.A.; Lima J.; Leitão P.;

Publicação
Procedia Manufacturing

Abstract
The 4th industrial revolution promotes the automatic inspection of all products towards a zero-defect and high-quality manufacturing. In this context, collaborative robotics, where humans and machines share the same space, comprises a suitable approach that allows combining the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the smart inspection and corrective actions for quality control systems in the manufacturing process, complemented by an intelligent system that learns and adapts its behavior according to the inspected parts. This intelligent system that implements the reinforcement learning algorithm makes the approach more robust once it can learn and be adapted to the trajectory. In the preliminary experiments, it was used a UR3 robot equipped with a Force-Torque sensor that was trained to perform a path regarding a product quality inspection task.

2022

Reinforcement Learning for Collaborative Robots Pick-and-Place Applications: A Case Study

Autores
Gomes, NM; Martins, FN; Lima, J; Wörtche, H;

Publicação
Automation

Abstract
The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an ?-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.

2022

A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models

Autores
Amoura, Y; Pereira, AI; Lima, J;

Publicação
SUSTAINABLE ENERGY FOR SMART CITIES, SESC 2021

Abstract
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.

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