NOVA mobility assistive system: Developed and remotely controlled with IOPT-tools

UID/EEA/00066/2020In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers’ specification, validation, and implementation, with remote operation capabilities. The infrastructures’ controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers’ implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.publishersversionpublishe

Graphic Model for Shop Floor Simulation and Control in the Context of Industry 5.0

Industry 5.0 changes the paradigm of the current production model, with repercussions throughout the value chain, and opens up opportunities for new approaches that include reducing waste to optimize the use of the planet’s resources. This paper proposes a functional and executable model that implements a Holonic Manufacturing System (HMS) architecture inspired by the I5.0 guidelines. This architecture presents the factory floor as a service provider for the product to be built, intending to make the manufacturing process adaptable to changes. The model uses Reference nets as the modeling language, a high-level class of Petri nets, Java programming language as the annotation language, and free tool support. The model can be used to perform software-level simulations and can also be interconnected to existing physical devices using Internet of things technologies, enabling interactions between Cyber–Physical Systems (CPSs). It thus allows for the control of the shop floor and the reuse of the current machine park to make its adoption more sustainable. The model was used to generate several simulation results, which are presented and analyzed, thus demonstrating the model’s usefulness

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV’s mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static textures- Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures- Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera