To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities’ safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.

This paper explores the application of artificial intelligence on edge devices to enhance security in critical infrastructures, with a specific focus on the use case of a battery-powered mobile system for fire detection in tunnels. The study leverages the YOLOv5 convolutional neural network (CNN) for real-time detection, focusing on a comparative analysis across three low-power platforms, NXP i.MX93, Xilinx Kria KV260, and NVIDIA Jetson Orin Nano, evaluating their performance in terms of detection accuracy (mAP), inference time, and energy consumption. The paper also presents a methodology for implementing neural networks on various platforms, aiming to provide a scalable approach to edge artificial intelligence (AI) deployment. The findings offer valuable insights into the trade-offs between computational efficiency and power consumption, guiding the selection of edge computing solutions in security-critical applications.