Adaptable videogame platform for interactive upper extremity rehabilitation

The primary objective of this work is to design a recreational rehabilitation videogame platform for customizing motivating games that interactively encourage purposeful upper extremity gross motor movements. Virtual reality (VR) technology is a popular application for rehabilitation therapies but there is a constant need for more accessible and affordable systems. We have developed a recreational VR game platform can be used as an independent therapy supplement without laboratory equipment and is inexpensive, motivating, and adaptable. The behaviors and interactive features can be easily modified and customized based on players\u27 limitations or progress. A real-time method of capturing hand movements using programmed color detection mechanisms to create the simulated virtual environments (VEs) is implemented. Color markers are tracked and simultaneously given coordinates in the VE where the player sees representations of their hands and other interacting objects whose behaviors can be customized and adapted to fit therapeutic objectives and players\u27 interests. After gross motor task repetition and involvement in the adaptable games, mobility of the upper extremities may improve. The videogame platform is expanded and optimized to allow modifications to base inputs and algorithms for object interactions through graphical user interfaces, thus providing the adaptable need in VR rehabilitation

Bio-inspired anatomy for autonomous DPWS-compliant automation components

Dissertação apresentada na Faculdade de Ciências e Engenharia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresThis thesis approaches the use of the DPWS technology to implement web-services on small devices, addresses its limitations, and explains an architecture to solve it. An approach to an autonomous device’s simple architecture was realized, using DPWS, and was called Simple DPWS. The objective was to implement/simplify some features in a device in a way that the device can work on its own. The designed architecture is based on that each component has its framework of modules, having always at least the skeleton modules communication and Event Router-Scheduler. The communication module controls all the communication between the devices and the ERS is the responsible for the other modules’ real-time communication. The DPWS toolkit offers no capability of interacting with run-time-appearing services. Thus there was a necessity to do enhancements over the DPWS toolkit to have a dynamic stub and skeleton. This service was called the dynamic service. An experience was done connecting a DPWS toolkit sample service with the corresponding hand-created dynamic service. It was used the lighting service that consists on turning a lamp ON or OFF and getting its status. A GUI was done for the application to be more user-friendly. The results were satisfactory, as the connection worked

Utilizing Reinforcement Learning and Computer Vision in a Pick-And-Place Operation for Sorting Objects in Motion

This master's thesis studies the implementation of advanced machine learning (ML) techniques in industrial automation systems, focusing on applying machine learning to enable and evolve autonomous sorting capabilities in robotic manipulators. In particular, Inverse Kinematics (IK) and Reinforcement Learning (RL) are investigated as methods for controlling a UR10e robotic arm for pick-and-place of moving objects on a conveyor belt within a small-scale sorting facility. A camera-based computer vision system applying YOLOv8 is used for real-time object detection and instance segmentation. Perception data is utilized to ascertain optimal grip points, specifically through an implemented algorithm that outputs optimal grip position, angle, and width. As the implemented system includes testing and evaluation on a physical system, the intricacies of hardware control, specifically the reverse engineering of an OnRobot RG6 gripper is elaborated as part of this study. The system is implemented on the Robotic Operating System (ROS), and its design is in particular driven by high modularity and scalability in mind. The camera-based vision system serves as the primary input, while the robot control is the output. The implemented system design allows for the evaluation of motion control employing both IK and RL. Computation of IK is conducted via MoveIt2, while the RL model is trained and computed in NVIDIA Isaac Sim. The high-level control of the robotic manipulator was accomplished with use of Proximal Policy Optimization (PPO). The main result of the research is a novel reward function for the pick-and-place operation that takes into account distance and orientation from the target object. In addition, the provided system administers task control by independently initializing pick-and-place operation phases for each environment. The findings demonstrate that PPO was able to significantly enhance the velocity, accuracy, and adaptability of industrial automation. Our research shows that accurate control of the robot arm can be reached by training the PPO Model purely by applying a digital twin simulation

A Framework for Test & Evaluation of Autonomous Systems Along the Virtuality-Reality Spectrum

Test & Evaluation of autonomous vehicles presents a challenge as the vehicles may have emergent behavior and it is frequently difficult to ascertain the reason for software decisions. Current Test & Evaluation approaches for autonomous systems place the vehicles in various operating scenarios to observe their behavior. However, this introduces dependencies between design and development lifecycle of the autonomous software and physical vehicle hardware. Simulation-based testing can alleviate the necessity to have physical hardware; however, it can be costly when transitioning the autonomous software to and from a simulation testing environment. The objective of this thesis is to develop a reusable framework for testing autonomous software such that testing can be conducted at various levels of mixed reality provided the framework components are sufficient to support data required by the autonomous software. The paper describes the design of the software framework and explores its application through use cases

Formal development of control software in the medical systems domain

In this thesis we describe the effectiveness of applying a number of formal techniques to the development of industrial control software at Philips Healthcare. We demonstrate how these techniques were tightly incorporated to the industrial workflow and the issues encountered during the application. The work was established in an industrial context, dealing with real industrial projects and a real product concerning the development of interventional X-ray systems. The results are very conclusive in the sense that the used formal techniques could deliver substantially better quality code compared to the code developed in conventional development methods. Also, the results show that the productivity of the formally developed code is better than the productivity of code developed by projects at Philips Healthcare or projects reported worldwide. The thesis also includes a number of design and specification guidelines that assist constructing verifiable components using model checking. The guidelines were successful in designing and verifying a controller component developed at Philips Healthcare. Hence, the guidelines can provide an effective framework to design verifiable control components in industrial settings

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm