Browsing by Author "Fernandes, Lucas de Azevedo"
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- Development of an intelligent robotic system for rehabilitation of upper limbs using a collaborative robotPublication . Fernandes, Lucas de Azevedo; Lima, José; Nakano, Alberto Yoshihiro; Leitão, PauloRehabilitation is a relevant process for the recovery from dysfunctions and improves the realisation of patient’s Activities ofDaily Living (ADLs). Therefore, the development of technologies for this field has significant importance because the improvement of the rehabilitation can affect many people. This work proposes a robotic system for the rehabilitation of the upper limbs using a collaborative robot and an intelligent control algorithm that makes the solution robust and adaptable to each patient. The UR3 from Universal Robots© was used to implement two Reinforcement Learning algorithms, the SARSA and Q-learning, applied to this rehabilitation problem. The goal of this system provides a common training force applying resistance on the movement performed by the patient. This thesis is divided into twomain parts. The first one was the development of a simulation composed by the UR3 and a human model in V-REP platformthat could be controlled through a dedicated interface or externally through the MATLAB using the self-control algorithms. This simulation was created with a graphical interface for visualisation, and a human-machine interface, to control the robotic system with RL algorithm, built onMATLAB. The results obtained with the simulation presented the expected system behaviour. The second part was the experiment of the real system with a healthy subject. The experiment was divided in two phases the first considering the training only in one axis and second in the three Cartesian axes. The used algorithms were the same as the simulation, but in this case, they were implemented in Python language. The experiment considering one axis presents satisfactory results, while for the three axes the results were not so good. The obtained results with the real system experiment for one and three axis were compared with the human armmodel proposed in other studies to validate the applied methodology. This work represents an important contribution for the field because presents a new feature to help therapists and patients to get better results in the rehabilitation process.
- A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10Publication . Brito, Thadeu; Queiroz, Jonas; Piardi, Luis; Fernandes, Lucas de Azevedo; Lima, José; Leitão, PauloThe 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. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.
- A real framework to apply collaborative robots in upper limb rehabilitationPublication . Fernandes, Lucas de Azevedo; Brito, Thadeu; Piardi, Luis; Lima, José; Leitão, PauloRehabilitation is an important recovery process from dysfunctions that improves the patient’s activities of daily living. On the other hand, collaborative robotic applications, where humans and machines can share the same space, are increasing once it allows splitting a task between 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 rehabilitation of the upper limb of patients, complemented by an intelligent system that learns and adapts its behaviour according to the patient’s performance during the therapy. This intelligent system implements the reinforcement learning algorithm, which makes the system robust and independent of the path of motion. The validation of the proposed approach uses a UR3 collaborative robot training in a real environment. The main control is the resistance force that the robot is able to do against the movement performed by the human arm.
- Robot@factory lite: an educational approach for the competition with simulated and real environmentPublication . Braun, João; Fernandes, Lucas de Azevedo; Moreira, Thiago Fernandes Moya; Oliveira, Vitor; Brito, Thadeu; Lima, José; Costa, Paulo Gomes daTeaching based on challenges and competitions is one of the most exciting and promising methods for students. In this paper, a competition of the Portuguese Robotics Open is addressed and a solution is proposed. The Robot@Factory Lite is a new challenge and accepts participants from secondary schools (Rookie) and universities. The concepts of simulation, hardware-in-the-loop and timed finite state machine are presented and validated in the real robot prototype. The aim of this paper is to disseminate the developed solution in order to attract more students to STEM educational program.
- Using a collaborative robot to the upper limb rehabilitationPublication . Fernandes, Lucas de Azevedo; Lima, José; Leitão, Paulo; Nakano, Alberto YoshiroRehabilitation is a relevant process for the recovery from dysfunctions and improves the realization of patient's Activities of Daily Living (ADLs). Robotic systems are considered an important field within the development of physical rehabilitation, thus allowing the collection of several data, besides performing exercises with intensity and repeatedly. This paper addresses the use of a collaborative robot applied in the rehabilitation field to help the physiotherapy of upper limb of patients, specifically shoulder. To perform the movements with any patient the system must learn to behave to each of them. In this sense, the Reinforcement Learning (RL) algorithm makes the system robust and independent of the path of motion. To test this approach, it is proposed a simulation with a UR3 robot implemented in V-REP platform. The main control variable is the resistance force that the robot is able to do against the movement performed by the human arm.