International roughness index (IRI) is a widely-accepted parameter that indicates pavement performance and ride quality. This study develops a prediction model for IRI using artificial neural networks (ANN) for flexible pavements located in wet-freeze, dry-freeze, wet no-freeze and dry no-freeze climate zones. The long-term pavement performance (LTPP) database is used for obtaining climate and traffic data. Annual average temperature, freezing index, maximum humidity, minimum humidity, precipitation, average daily traffic, and average daily truck traffic are considered as input parameters for predicting IRI. The proposed ANN model is trained with 50% of the available climate and traffic data and the remaining 50% of the data are used for testing the model. The comparison of LTPP recorded data and ANN predicted data is validated by calculating root mean square error (RMSE). The 7-9-9-1 ANN model with a hyperbolic tangent sigmoid transfer function generated the lowest RMSE of 0.01. The 7-9-9-1 ANN model is further tuned for robustness and consistency with several synthetic data sets and 70%, 15%, and 15% of the synthetic data sets are used to train, test, and validate, respectively, the ANN model. The ANN model predicts the IRI with reasonable accuracy and the lowest RMSE 0.027 in measured.

Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research.

This paper advances the previous theoretical work of authors by conducting experiments on a generalized coverage optimization algorithm using a team of heterogeneous mobile robots. A scalar measure (herein called the density) of the environment defines a nonuniform coverage metric of the area. Mobile robots are spatially configured such that their asymptotic placements in an area maximize the nonuniform coverage metric. Over the last fifteen years, a large body of research work has been conducted in solving the area coverage optimization problem where the focus was mainly on theoretical results followed by mostly numerical simulations. In some cases, the coverage optimization algorithms were validated with experimental results, but the implementation platforms were suitable to specific homogeneous robot platforms. Here, the emphasis is on the real-time implementation of the authors' previously published theoretical results on area coverage optimization problems using a team of heterogeneous mobile robots. The robots are heterogeneous in the sense that they have different actuator limits, physical dimensions, and processing capabilities. The modularity of the algorithm stems from the fact that the additional hardware/software architecture is open-source and can be applied to different robots regardless of their internal electromechanical system architectures. The algorithm is implemented using the emerging robot operating system in a multithreaded manner. A commercial robot simulator was used to validate the coverage performance followed by a set of experiments conducted in an indoor environment.

We consider nonuniform coverage optimization with respect to a non-autonomous coverage metric by spatially deploying a platoon of mobile agents in a planar region. Conventional coverage metrics usually encode a density field that weights points in the workspace. We consider a time-varying diffusive density that evolves according to a conservation law, and the induced time-varying coverage. Boundary conditions can model a time-varying flux across the boundary, and/or a time varying boundary density. We propose a decentralized state-feedback control law that maximizes the generalized non-autonomous coverage metric. The current approach of nonuniform deployment of autonomous agents applies to environmental monitoring and intervention, with deployment of mobile sensors in areas affected by penetration of substances governed by diffusion mechanisms, as for example oil in a marine environment, that pose immediate or long-term threats. We establish asymptotic convergence results illustrated by simulations.

Addressing the navigation (localization and motion control) problem of a mobile robot, coupled with its mapping problem, remains a significant challenge to date. The well-known simultaneous localization and mapping (SLAM) problem of mobile robots has been addressed in the literature without specifically taking into account the robot's motion control tasks. Moreover, its implementation can cost more than the robot itself. Robot's motion control strategies developed in the literature either (i) rely on sophisticated hardware platforms, (ii) assume noise-free environments, or (iii) are based on abstract theories which are validated using computer simulations only. The current work solves the navigation and mapping problems of mobile robots using open-source hardware and range-only measurements from a network of radio sources. The hardware platform used in this work is customized such that it is cost-effective and easy-to-implement, addressing the aforementioned issues in developing motion control strategies. Here, the robot estimates its position and orientation and builds a map of its operating environment using radio frequency (RF) signals received from radio sources. It then navigates through a path defined by a set of 2D points on the ground using a motion control strategy in cooperation with a tool of computational intelligence. The proposed robot navigation and mapping scheme is tested in an indoor laboratory environment and its performance is compared with the simulation counterpart using a commercial robot simulator.

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.