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Optimizing path planning for energy efficiency is critical for achieving sustainable vehicular transportation. This paper presents a novel framework for evaluating the impact of path planning algorithms (PPAs) on energy consumption within a simulated environment. We leverage the CARLA simulator to conduct a comparative analysis between the widely used A* and a Hybrid Genetic Algorithm (HGA) across diverse vehicular scenarios. This investigation aims to quantify the influence of PPA selection on vehicle energy expenditure, enabling data-driven optimization for energy minimization. We leverage an offline energy estimation model to further streamline the comparison of the two PPAs. Extensive simulations are employed to demonstrate the efficiency and adaptability of the proposed framework. The findings contribute to the development of energy-efficient path-planning strategies, promoting advancements toward sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is a technology that has been used extensively in the field of cartography, with one particular application being that of navigation. However even now, AR in navigation is still at an active exploratory stage with multiple different approaches regarding the techniques, methodologies, and development processes. In this work, we present a systematic review of application development in AR navigation research, aiming to fill the gap in the literature by identifying trends in aspects such as hardware, software, and methodologies in AR navigation. We review 107 publications from the past 25 years that present AR navigation software for real-world environments, and analyze their main characteristics regarding intended environments and users, hardware, development platforms, and AR methodologies. We note a rise in AR navigation research interest from 2010 onward, driven in large part by smartphone and mobile device ubiquity, supported further by significant advances and streamlined processes in AR application development. Furthermore, we highlight a rise in the use of increasingly more complex AR methodologies in recent years, signaling that the field is steadily evolving and maturing. At the same time, we also identify significant inconsistencies and omissions regarding the reporting of technical details, with nearly half of reviewed works not reporting one or more of the aspects of interest. After identifying the level of maturity and complexity in the field AR navigation, we provide a list of recommendations and future directions for researchers aiming to develop AR navigation applications. [ABSTRACT FROM AUTHOR]

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well. [ABSTRACT FROM AUTHOR]

We introduce a new chamfering paradigm, locally connecting pixels to produce path distances that approximate Euclidean space by building a small network (a replacement product) inside each pixel. These “ RE-grid graphs” maintain near-Euclidean polygonal distance contours even in noisy data sets, making them useful tools for approximation when exact numerical solutions are unobtainable or impractical. The RE-grid graph creates a modular global architecture with lower pixel-to-pixel valency and simplified topology at the cost of increased computational complexity due to its internal structure. We present an introduction to chamfering replacement products with a number of case study examples to demonstrate the potential of these graphs for path-finding in high frequency and low resolution image spaces which motivate further study. Possible future applications include morphology, watershed segmentation, halftoning, neural network design, anisotropic image processing, image skeletonization, dendritic shaping, and cellular automata. [ABSTRACT FROM AUTHOR]

We present an in-depth discussion on the subband Boltzmann transport (SBTE) methodology, its evolution, and its application to the simulation of nanoscale MOSFETs. The evolution of the method is presented from the point of view of developing a commercial general-purpose SBTE solver, the GTS nano device simulator (NDS). We show a wide range of applications SBTE is suited for, including state-of-the-art nonplanar and well-established planar technologies. It is demonstrated how SBTE can be employed both as a path-finding tool and a fundamental component in a DTCO-flow. [ABSTRACT FROM AUTHOR]

We introduce a new chamfering paradigm, locally connecting pixels to produce path distances that approximate Euclidean space by building a small network (a replacement product) inside each pixel. These “ $RE$ -grid graphs” maintain near-Euclidean polygonal distance contours even in noisy data sets, making them useful tools for approximation when exact numerical solutions are unobtainable or impractical. The $RE$ -grid graph creates a modular global architecture with lower pixel-to-pixel valency and simplified topology at the cost of increased computational complexity due to its internal structure. We present an introduction to chamfering replacement products with a number of case study examples to demonstrate the potential of these graphs for path-finding in high frequency and low resolution image spaces which motivate further study. Possible future applications include morphology, watershed segmentation, halftoning, neural network design, anisotropic image processing, image skeletonization, dendritic shaping, and cellular automata. [ABSTRACT FROM AUTHOR]

Ray-tracers are used for modeling the propagation behavior of mobile communications or radar within a given scenario. The necessary CPU-time for the path-finding is often the bottleneck of the simulation. The paper shows a new interpolation method, which exploits the similarity of an incoming plane wave at a receiver position with that in the vicinity of the position. This is combined with a linear weighting, as in general the waves at other receiver positions can be used for the interpolation. Examples using a ray-tracer equipped with the method demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Background: As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. Results: We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. Conclusion: We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells. [ABSTRACT FROM AUTHOR]

Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray images and uses the Hausdorff-distance measure for ranking persons. This combination of state-of-the- art approaches with a novel lowest cost path-based method for separating a dental X-ray image into individual teeth, is able to achieve comparable and better results than what is available in the literature. The proposed scheme is fully functional and is used to accurately identify people within a real dental database. The system is able to perfectly separate 88.7% of the teeth in the test set. Further, in the verification process, the system ranks the correct person in top in 86% of the cases, and among the top five in an astonishing 94% of the cases. The approach has compelling potential to significantly reduce the time spent on dental identification. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a path-finding algorithm to solve problems with an arbitrary load-displacement relationship which results from geometrical and material nonlinear models to simulate e.g. timber structures realistically. Design/methodology/approach – A method using combined load and displacement control for the Newton method along with path-characterising measures and sub-incremention is introduced. A path-related stiffness measure is used to identify the situation when it is necessary to select the displacement control and chose the best degree of freedom as a parameter instead of the load factor. The nonlinearity index extracts information about the convergence behaviour during one incremental step. Together with the reduction of the load increments it avoids leaving the equilibrium path. Findings – The method is discussed based on numerical examples with highly nonlinear behaviour. It is capable to solve systems with decreasing load capacity and snap-back effects. Originality/value – The algorithm combines load and displacement control and adaptively choses the method and the corresponding degree of freedom and cares for reliable path following. [ABSTRACT FROM AUTHOR]

Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A * is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the abstract and the original maps. However, most of these methods do not consider obstacle distributions, which may result in unnecessary storage and non-optimal paths in certain open areas. In this paper, a new abstract graph-based path-finding method named Genetic Convex A * is proposed. An important convex map concept which guides the partition of the original map is defined. It is proven that the path length between any two nodes within a convex map is equal to their Manhattan distance. Based on the convex map, a fitness function is defined to improve the extraction of key nodes; and genetic algorithm is employed to optimize the abstraction. Finally, the on-line refinement is accelerated by Convex A *, which is a fast alternative to A * on convex maps. Experimental results demonstrated that the proposed abstraction generated by Genetic Convex A *guarantees the optimality of the path whilst searches less nodes during the on-line processing. [ABSTRACT FROM AUTHOR]

We argue that A*, the popular technique for path-finding for NPCs in games, suffers from three limitations that are pertinent to game worlds: (a) the grid maps often restrict the optimality of the paths, (b) A* paths exhibit wall-hugging behavior, and (c) optimal paths are more predictable. We present a new algorithm, VRA*, that varies map-resolution as needed, and repeatedly calls A*. We also present an extension of an existing post-smoothing technique, and show that these two techniques together produce more realistic looking paths than A*, that overcome the above limitations, while using significantly less memory and time than A*. [ABSTRACT FROM AUTHOR]

Learning how biological systems solve problems could help to design new methods of computation. Information processing in simple cellular organisms is interesting, as they have survived for almost 1 billion years using a simple system of information processing. Here we discuss a well-studied model system: the large amoeboid Physarum plasmodium. This amoeba can find approximate solutions for combinatorial optimization problems, such as solving a maze or a shortest network problem. In this report, we describe problem solving by the amoeba, and the computational methods that can be extracted from biological behaviors. The algorithm designed based on Physarum is both simple and useful. [ABSTRACT FROM AUTHOR]

We were recently asked to evaluate the Footprint-to-Pathfinder1 path-finding implementation for possible reuse in OneSAF Objective System. The Footprint-to-Pathfinder path-finding code implements a variation of an algorithm described by Nils Nilsson called the A Algorithm. The A Algorithm can guarantee the optimal, lowest cost path between two points when it uses an “admissible” evaluation function. It is then called the A* (A-Star) Algorithm. The Footprint-to-Pathfinder implementation uses a simplification to the A Algorithm that preserves optimality when a particular condition exists. The condition that enables the simplification—the monotone restriction—is neither well known nor explained with most references for the A Algorithm. In fact, most references imply that an admissible evaluation function is sufficient. However, there are admissible evaluation functions that will cause the simplified implementation to return a path that is sub-optimal. OneSAF Objective System does not restrict admissible evaluation functions as Footprint-to-Pathfinder does. Thus the simplified algorithm is inappropriate for the OneSAF Objective System. Because the monotone restriction is not well publicized, this paper describes it in the context of path finding for combat simulations. [ABSTRACT FROM PUBLISHER]

We were recently asked to evaluate the Footprint-to-Pathfinder1 path-finding implementation for possible reuse in OneSAF Objective System. The Footprint-to-Pathfinder path-finding code implements a variation of an algorithm described by Nils Nilsson called the A Algorithm. The A Algorithm can guarantee the optimal, lowest cost path between two points when it uses an “admissible” evaluation function. It is then called the A* (A-Star) Algorithm. The Footprint-to-Pathfinder implementation uses a simplification to the A Algorithm that preserves optimality when a particular condition exists. The condition that enables the simplification—the monotone restriction—is neither well known nor explained with most references for the A Algorithm. In fact, most references imply that an admissible evaluation function is sufficient. However, there are admissible evaluation functions that will cause the simplified implementation to return a path that is sub-optimal. OneSAF Objective System does not restrict admissible evaluation functions as Footprint-to-Pathfinder does. Thus the simplified algorithm is inappropriate for the OneSAF Objective System. Because the monotone restriction is not well publicized, this paper describes it in the context of path finding for combat simulations. [ABSTRACT FROM PUBLISHER]

This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardware to compute the shortest path on large-scale networks. Thus, it is necessary to reduce the time complexity while exploiting a low cost commodity hardwares. To cope with this situation, we propose a novel approach that reduces the A* algorithm into a set of Map and Reduce tasks for running the path computation on Hadoop MapReduce framework. An application on real road networks illustrates the feasibility and reliability of the proposed framework. The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time. [ABSTRACT FROM AUTHOR]