Automation

Advancements in robotics and computer vision are transforming how infrastructure is monitored and maintained. This paper presents a novel, fully automated pipeline for pavement condition assessment that integrates real-time image analysis with PCI (Pavement Condition Index) computation, which is specifically designed for deployment on mobile and robotic platforms. Unlike traditional methods that rely on costly equipment or manual input, the proposed system uses deep learning-based object detection and ensemble segmentation to identify and measure multiple types of road distress directly from 2D imagery, including surface weathering, a key precursor to pothole formation often overlooked in previous studies. Depth estimation is achieved using a monocular diffusion model, enabling volumetric assessment without specialized sensors. Validated on real-world footage captured by a smartphone, the pipeline demonstrated reliable performance across detection, measurement, and scoring stages. Its potential hardware-agnostic design and modular architecture position it as a practical solution for autonomous inspection by drones or ground robots in future smart infrastructure systems.

This work presents a nonlinear aerodynamic model that describes the dynamics of a coaxial-rotor MAV. We have designed seven control laws based on linear and nonlinear controllers for path-following with a coaxial-rotor MAV in the presence of unknown disturbances, such as wind gusts. The linear controllers include Proportional–Derivative (PD) and Proportional–Integral–Derivative (PID). The nonlinear techniques encompass nested saturation, sliding mode control, second-order sliding mode, high-order sliding mode, and adaptive backstepping. The results are shown after multiple computer simulations.

A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve alone. With widespread applications in domains such as robotics, smart grids, and communication networks, the coordination and control of networked systems have become a vital research focus—driven by the complexity of distributed interactions and decision-making processes. Graph-based reinforcement learning (GRL) has emerged as a powerful paradigm that combines reinforcement learning with graph signal processing and graph neural networks (GNNs) to develop policies that are relationally aware, scalable, and adaptable to diverse network topologies. This survey aims to advance research in this evolving area by providing a comprehensive overview of GRL in the context of networked coordination and control. It covers the fundamental principles of reinforcement learning and graph neural networks, examines state-of-the-art GRL models and algorithms, reviews training methodologies, discusses key challenges, and highlights real-world applications. By synthesizing theoretical foundations, empirical insights, and open research questions, this survey serves as a cohesive and structured resource for the study and advancement of GRL-enabled networked systems.