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To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques. [ABSTRACT FROM AUTHOR]

Water loss management (WLM) in water distribution systems (WDSs) is generally time consuming, costly and difficult. Therefore, the most appropriate methodology should be defined based on the current status, requirements and technical capacity of the utilities. In this study, a novel WLM model was developed to improve the WLM practices, to define the most appropriate and applicable targets and to determine the components that need to be improved primarily using the Harris Hawk Optimization (HHO) algorithm. Firstly, a total of 144 components in the CSAS proposed by Bozkurt et al. (Welcome Sigma J Eng Natural Sci 40:1–13, 2022b) are scored in three utilities. The averages of current scores in utilities are 3.4167, 2.9792 and 1.1597, respectively. Then the most appropriate targets were defined with the optimization algorithm by considering the current situation and the dynamic structure of utility. The target scores defined by optimization model in utilities are 4.4097, 4.3958 and 3.2292, respectively. This novel model will contribute to the prevention of time-consuming and costly processes by determining the most appropriate progress. It is thought that this study will provide significant benefits for creating a road map for practitioners in WLM. [ABSTRACT FROM AUTHOR]

In terms of trajectory planning, the main objective is to reach a certain location in the shortest time possible, avoiding obstacles along the way. This paper proposes two path planning methods, in an environment with presence of obstacles, Probabilistic RoadMap and a fusion between Voronoi diagram and Dijkstra's Algorithm. Path planning for both algorithms is developed in the XY coordinate plane, therefore only horizontal movement is considered. This means that the UAV must fly around obstacles and cannot fly over or under them. The use of two-dimensional space in favour of three-dimensional space reduces the implementation time of the algorithm for solving the problem. Finally, an analysis of the length and the computational time of the trajectories generated by the proposed algorithms is made. [ABSTRACT FROM AUTHOR]

The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas. [ABSTRACT FROM AUTHOR]

Ship route planning is a crucial activity that must consider not only the safety of the ship but also the safe passage of nearby ships in the same space and time. This study aims to provide general route-planning guidance to shipping traffic by improving conventional sampling-based route-planning algorithms in accordance with the maritime environment from a ship operator's perspective. The obstacle safety margin in a marine environment can be reflected in a binary image map space based on an electronic navigational chart. Consequently, an initial route was created using the probabilistic road map (PRM) algorithm in the configured map space to increase the speed of the conventional sampling-based route-planning algorithm. Based on the initial route created, a designated space—that is, a multi-elliptical area—was created to limit the route-search range. After searching the final route in the designated space based on the rapidly-exploring random tree (RRT*) algorithm, optimal route planning could be achieved by generating a collision-free space graph to remove unnecessary nodes from the searched final route. The simulation results showed that the route was shortened by approximately 33.7 km compared with the conventional RRT* algorithm, and the calculation time was shortened by approximately 2.5 times. [ABSTRACT FROM AUTHOR]

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length. [ABSTRACT FROM AUTHOR]

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems. [ABSTRACT FROM AUTHOR]

This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC). [ABSTRACT FROM AUTHOR]

Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabilize the urban VANET topology. However, the management of various load control parameters between the different components of the urban VANET network remains a problem to be studied. This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved video segment delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms such as Seamless and Authorized Multimedia Streaming and P-DSR. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the delivery rate of video segments. [ABSTRACT FROM AUTHOR]

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The fireworks algorithm, which is inspired from the phenomenon of fireworks explosion, is a special kind of swarm intelligence algorithm proposed in 2010. Since then, it has been attracting more and more research interest and has been widely employed in many real-world problems due to its unique search manner and high efficiency. In this article, we present a comprehensive review of its advances and applications. We begin with an introduction to the original fireworks algorithm. Then we review its algorithmic research work for single objective and multi-objective optimization problems. After that, we present the theoretical analyses of the fireworks algorithm. Finally, we give a brief overview of its applications and implementations. Hopefully, this article could provide a useful road map for researchers and practitioners who are interested in this algorithm and inspire new ideas for its further development. [ABSTRACT FROM AUTHOR]

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposedhybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92. [ABSTRACT FROM AUTHOR]

A road map can be interpreted as a graph embedded in the plane, in which each vertex corresponds to a road junction and each edge to a particular road section. In this paper, we consider the computational cartographic problem to place non-overlapping road labels along the edges so that as many road sections as possible are identified by their name, i.e., covered by a label. We show that this is NP-hard in general, but the problem can be solved in O (n 3) time if the road map is an embedded tree with n vertices and constant maximum degree. This special case is not only of theoretical interest, but our algorithm in fact provides a very useful subroutine in exact or heuristic algorithms for labeling general road maps. [ABSTRACT FROM AUTHOR]

We study five existing map construction algorithms, designed and tested with urban vehicle data in mind, and apply them to hiking trajectories with different terrain characteristics. Our main goal is to better understand the existing strategies and their limitations, in order to shed new light into the current challenges for map construction algorithms. We carefully analyze the results obtained by each algorithm focusing on the local details of the generated maps. Our analysis includes the characterization of 10 types of common artifacts, which occur in the results of more than one algorithm, and 7 algorithmic-specific artifacts, which are consequences of different algorithmic strategies. This allows us to extract systematic conclusions about the main challenges to fully automatize the construction of maps from trajectory data, to detect the strengths and weaknesses of the potential different strategies, and to suggest possible ways to design higher-quality map construction methods. We consider that this analysis will be of help for designing new and better methods that perform well in wider and more realistic contexts, not only for road map or hiking reconstruction, but also for other types of trajectory data. [ABSTRACT FROM AUTHOR]

Intelligent vehicles face a more complex driving environment, which requires their path planning algorithms to have the ability to respond quickly to complex environmental changes. Therefore, it is very necessary to plan the optimal path for smart vehicles in real-time. Recently, the convergence of graph dynamics and its equivalence with the shortest path solution have been proved, and the biased minimum consensus algorithm has been successfully applied to the shortest path planning problem. In this paper, considering the road conditions (degree of congestion, whether an accident occurred, etc.) and the Euclidean distance of the route, we improve biased minimum consensus algorithm to achieve optimal path planning in the complex situation of real-time updating of road conditions. In the simulation, we used the algorithm for path planning in the road topological maps of Beijing City, and compared with the RRT (Rapidly-exploring Random Trees Algorithm) algorithm and the BRRT (Bidirectional Rapidly-exploring Random Trees Algorithm) algorithm. The improved biased minimum consensus algorithm has better performance in real-time path planning problems. The research results shed new light on the real-time dynamic path planning in real traffic, which has practical significance. [ABSTRACT FROM AUTHOR]

In this paper, solved a robot path planning problem in known environment using anew algorithm called the Fuzzy Probalistic Road Map Star PRM* algorithm. In this algorithm, we have modeled the workplace including obstacles As fuzzy. Here, we develop PRM algorithm, so that it can run in a fuzzy environment.We assign a weight for each obstacle (fuzzy number) which represents the obstaclemeasures, meaning that whenever this weight is larger, the obstacle becomes more impassable (or more dangerous). This algorithm has been designed for using by robot's movement in difficult areas (such as areas full with obstacles in which the robot requiresto crossover these obstacles to reach its goal). By compare the FPRM* withnormal PRM we found that modeling the environment as fuzzy helps to improve the path. [ABSTRACT FROM AUTHOR]

Previous map-matching algorithms perform well in sparse road areas and simple road network structures. Nevertheless, mismatches emerge when the algorithms are implemented in a complex road network. Special road patterns such as parallel or auxiliary roads, roundabouts, intersections, U-turns, or overpasses make it harder to identify features correctly. This paper proposes a scenario-based map-matching algorithm which balances accuracy and efficiency. Instead of matching every point by a constant process, the proposed algorithm provides specific combinations of filters for different operational environments. To eliminate improper candidate roads at each step, five road filters are introduced. Each filter has its own trigger condition, and if the condition is not satisfied, the filter will be skipped. The algorithm is validated using taxi GPS data on various complex roads in Shenzhen. The algorithm correctly identified 99.60%, 96.40%, 93.18%, 96.87%, and 94.11% of the roads for simple, opposing parallel, and auxiliary roads; intersections; and unconnected buffer situations, respectively. The proposed algorithm has two important features: 1. the algorithm is suitable for various complex intersections; 2.the map-matching process can be adjusted according to the traffic scenario, which saves computational time. The evaluated performance indicates that the proposed algorithm performs better than previous map-matching algorithms in both simple and complex road networks and can be efficiently applied to GPS datasets using various time intervals. [ABSTRACT FROM AUTHOR]

In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs' application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks. [ABSTRACT FROM AUTHOR]

This paper analyzes the limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints which restricts how the agents can move. First, the (unlimited range) spatial load balancing problem is introduced and the minimization problem with area constraints is defined. Then, to extend the problem for limited ranges, two cost functions and a sub-partition are defined. The problems are then analyzed and the results prove the existence of a partition that satisfies the area constraints. The non-convex environment makes the problem difficult to solve in continuous-space. Therefore, a probabilistic roadmap is used to approximate agents’ cells via a graph. A distributed algorithm is proven to converge to an approximate solution. Finally, the convergence of the algorithm is shown in simulation. [ABSTRACT FROM AUTHOR]

Roadmaps constructed by many sampling-based motion planners coincide, in the absence of obstacles, with standard models of random geometric graphs (RGGs). Those models have been studied for several decades and by now a rich body of literature exists analyzing various properties and types of RGGs. In their seminal work on optimal motion planning, Karaman and Frazzoli conjectured that a sampling-based planner has a certain property if the underlying RGG has this property as well. In this paper, we settle this conjecture and leverage it for the development of a general framework for the analysis of sampling-based planners. Our framework, which we call localization–tessellation, allows for easy transfer of arguments on RGGs from the free unit hypercube to spaces punctured by obstacles, which are geometrically and topologically much more complex. We demonstrate its power by providing alternative and (arguably) simple proofs for probabilistic completeness and asymptotic (near-)optimality of probabilistic roadmaps (PRMs) in Euclidean spaces. Furthermore, we introduce three variants of PRMs, analyze them using our framework, and discuss the implications of the analysis. [ABSTRACT FROM AUTHOR]

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data. [ABSTRACT FROM AUTHOR]

Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly exploring random tree (RRT) algorithms, represent one of the most successful approaches to robotic motion planning, due to their strong theoretical properties (in terms of probabilistic completeness or even asymptotic optimality) and remarkable practical performance. Such algorithms are probabilistic in that they compute a path by connecting independently and identically distributed (i.i.d.) random points in the configuration space. Their randomization aspect, however, makes several tasks challenging, including certification for safety-critical applications and use of offline computation to improve real-time execution. Hence, an important open question is whether similar (or better) theoretical guarantees and practical performance could be obtained by considering deterministic, as opposed to random, sampling sequences. The objective of this paper is to provide a rigorous answer to this question. Specifically, we first show that PRM, for a certain selection of tuning parameters and deterministic low-dispersion sampling sequences, is deterministically asymptotically optimal, in other words, it returns a path whose cost converges deterministically to the optimal one as the number of points goes to infinity. Second, we characterize the convergence rate, and we find that the factor of sub-optimality can be very explicitly upper-bounded in terms of the ℓ 2 -dispersion of the sampling sequence and the connection radius of PRM. Third, we show that an asymptotically optimal version of PRM exists with computational and space complexity arbitrarily close to O ( n ) (the theoretical lower bound), where n is the number of points in the sequence. This is in contrast to the O ( n log n ) complexity results for existing asymptotically optimal probabilistic planners. Fourth, we discuss extending our theoretical results and insights to other batch-processing algorithms such as FMT*, to non-uniform sampling strategies, to k-nearest-neighbor implementations, and to differentially constrained problems. Importantly, our main theoretical tool is the ℓ 2 -dispersion, an interesting consequence of which is that all our theoretical results also hold for low- ℓ 2 -dispersion random sampling (which i.i.d. sampling does not satisfy). In other words, achieving deterministic guarantees is really a matter of i.i.d. sampling versus non-i.i.d. low-dispersion sampling (with deterministic sampling as a prominent case), as opposed to random versus deterministic. Finally, through numerical experiments, we show that planning with deterministic (or random) low-dispersion sampling generally provides superior performance in terms of path cost and success rate. [ABSTRACT FROM AUTHOR]

The remote surveillance and tracking of vehicles on roads is generally achieved by means of a network of sensors deployed along the roadside. The effectiveness of such networks depends fundamentally on the precision with which the sensor locations are known Global Positioning Systems provide an effective means of pinpointing the sensor locations. However, such systems are expensive, and are therefore impractical for large-scale sensor deployment. Accordingly, this paper proposes a low-cost sensor localization service based on binary detection information and a road map. In the proposed algorithm, the sensors are classified as either intersection nodes or non-intersection nodes in accordance with the stochastic characteristics of the binary detection information. Having identified all of the non-intersection nodes along each road segment, the intersection nodes are connected with the non-intersection nodes by means of a clustering technique. Finally, the positions of the roadside sensors are identified by mapping the clustered sensors to the known road map. The simulation results show that the proposed algorithm outperforms existing localization algorithms reported in the literature. [ABSTRACT FROM AUTHOR]

The two most commonly used hypergeometric functions are the confluent hypergeometric function and the Gauss hypergeometric function. We review the available techniques for accurate, fast, and reliable computation of these two hypergeometric functions in different parameter and variable regimes. The methods that we investigate include Taylor and asymptotic series computations, Gauss-Jacobi quadrature, numerical solution of differential equations, recurrence relations, and others. We discuss the results of numerical experiments used to determine the best methods, in practice, for each parameter and variable regime considered. We provide 'roadmaps' with our recommendation for which methods should be used in each situation. [ABSTRACT FROM AUTHOR]

Motion planning is a crucial, basic issue in robotics, which aims at driving vehicles or robots towards to a given destination with various constraints, such as obstacles and limited resource. This paper presents a new version of rapidly exploring random trees (RRT), that is, liveness-based RRT (Li-RRT), to address autonomous underwater vehicles (AUVs) motion problem. Different from typical RRT, we define an index of each node in the random searching tree, called "liveness" in this paper, to describe the potential effectiveness during the expanding process. We show that Li-RRT is provably probabilistic completeness as original RRT. In addition, the expected time of returning a valid path with Li-RRT is obviously reduced. To verify the efficiency of our algorithm, numerical experiments are carried out in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of finding the best paths connecting a given pair of nodes in a directed graph with arbitrary lengths. We introduce an algorithm to determine the best paths in order of length when repeat nodes in the paths are allowed. We obtain an O time and O space algorithm to implicitly determine the shortest paths in a directed graph with nodes and arcs. Empirical results where the algorithm was used to compute one hundred million paths in USA road networks are reported. A non-trivial modification of the previous algorithm is performed obtaining an O time method to compute paths without repeat nodes and to answer the next question: how many paths in practice are needed to identify simple paths using the previous algorithm? We find that the response is usually O based on an experiment computing one million paths in USA road networks. The determination of a theoretical tight bound on remains as an open problem. [ABSTRACT FROM AUTHOR]

This article considers the problem of calculating the set of all Pareto-optimal solutions in one-to-one biobjective shortest path problems with positive cost vectors. The efficiency of multiobjective best-first search algorithms can be improved with the use of consistent informed lower bounds. More precisely, the use of the ideal point as a lower bound has recently been shown to effectively increase search performance. In theory, the use of lower bounds that better approximate the Pareto frontier using sets of vectors (bound sets), could further improve performance. This article describes a lower bound set calculation method for biobjective shortest path problems. Improvements in search efficiency with lower bound sets of increasing precision are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. [ABSTRACT FROM AUTHOR]

Map construction methods automatically produce and/or update street map datasets using vehicle tracking data. Enabled by the ubiquitous generation of geo-referenced tracking data, there has been a recent surge in map construction algorithms coming from different computer science domains. A cross-comparison of the various algorithms is still very rare, since (i) algorithms and constructed maps are generally not publicly available and (ii) there is no standard approach to assess the result quality, given the lack of benchmark data and quantitative evaluation methods. This work represents a first comprehensive attempt to benchmark such map construction algorithms. We provide an evaluation and comparison of seven algorithms using four datasets and four different evaluation measures. In addition to this comprehensive comparison, we make our datasets, source code of map construction algorithms and evaluation measures publicly available on . This site has been established as a repository for map construction data and algorithms and we invite other researchers to contribute by uploading code and benchmark data supporting their contributions to map construction algorithms. [ABSTRACT FROM AUTHOR]

Among the various approaches to multiscale simulations, in recent years, force matching has been known for a quick growth. The method is based on a least-square fit of reference properties obtained from simulations at a certain scale, to parameterize the force field for coarser-grained scale simulations. Its advantage with respect to conventional schemes used for parameterizing force fields, lies in that only physically accessible configurations are sampled, and that the number of reference data per configuration is large. In this perspective article, we discuss some recent findings on the tailoring of the objective function, on the choice of the empirical potential, and on the way to improve the quality of the reference calculations. We present pros and cons of the algorithm, and we propose a road map to future developments. [ABSTRACT FROM AUTHOR]

Direction is a key factor of accessibility research. Therefore, this paper proposed a calculation method on space-time accessibility considering the direction of travel. First, it constructed an experiential hierarchy of road network using experiential taxi routes. Second, it used the Dijkstra algorithm based on hierarchical road network to compute the travel time between each OD, and then used a grid method and inverse distance weighting method to render the isochrones maps of Guangzhou CBD. Finally, the paper analyzed the accessibility of it in different direction of travel and different time periods. The experimental results prove that accessibility in different direction, especially in peak time, is quite different. [ABSTRACT FROM AUTHOR]

In recent years, new applications, architectures, and technologies have been proposed for vehicular ad hoc networks (VANETs). Regarding traffic safety applications for VANETs, warning messages have to be quickly and smartly disseminated in order to reduce the required dissemination time and to increase the number of vehicles receiving the traffic warning information. In the past, several approaches have been proposed to improve the alert dissemination process in multihop wireless networks, but none of them were tested in real urban scenarios, adapting its behavior to the propagation features of the scenario. In this paper, we present the Profile-driven Adaptive Warning Dissemination Scheme (PAWDS) designed to improve the warning message dissemination process. With respect to previous proposals, our proposed scheme uses a mapping technique based on adapting the dissemination strategy according to both the characteristics of the street area where the vehicles are moving and the density of vehicles in the target scenario. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in scenarios based on real city maps. [ABSTRACT FROM PUBLISHER]

Abstract: Automated crack-sealing machinery must accurately locate continuous individual crack segments so that they can be processed and treated effectively. In this paper, we illustrate the use of a geodesic minimal path based method for generating the crack map suitable for the path planning process. The user can detect continuous cracks that extend over several miles just by providing the starting point of a crack as an input to the algorithm. The algorithm can also detect transverse cracks by giving a single point on the crack. The continuous crack map generated can be utilized very efficiently to generate the optimal path for the crack sealer. An extensive qualitative and quantitative evaluation on real pavement images was done to show the usefulness of the algorithm. The algorithm is computationally fast and efficient. [Copyright &y& Elsevier]

Abstract: This article considers the application of exact multiobjective techniques to search in large size realistic road maps. In particular, the NAMOA∗ algorithm is successfully applied to several road networks from the DIMACS shortest path implementation challenge with two objectives. An efficient heuristic function previously proposed by Tung and Chew is evaluated. Heuristic values are precalculated with search. The precalculation effort is shown to pay off during the multiobjective search stage. An improvement to the calculation procedure is also proposed, resulting in added improved time performance in many problem instances. [Copyright &y& Elsevier]

We show how to compute in O( nlog n+ n klog n) time the distance between k given pairs of vertices of a planar graph G with n vertices. This improves previous results whenever ( n/log n)≤ k≤ n/log n. As an application, we speed up previous algorithms for computing the dilation of geometric planar graphs. [ABSTRACT FROM AUTHOR]

The task of planning a path between two spatial configurations of an artifact moving among obstacles is an important problem in practically all geometrically intensive applications. Despite the ubiquity of the problem, the existing approaches make specific limiting assumptions about the geometry and mobility of the obstacles, or those of the environment in which the motion of the artifact takes place. We present a strategy to construct a family of paths, or roadmaps, for two- and three-dimensional solids moving in an evolving environment that can undergo drastic topological changes. Our approach is based on a potent paradigm for constructing geometric skeletons that relies on constructive representations of shapes with R-functions that operate on real-valued half-spaces as logic operations. We describe a family of skeletons that have the same homotopy as that of the environment and contains the medial axis as a special case. Of importance, our skeletons can be designed so that they are “attracted to” or “repulsed by” prescribed spatial sites of the environment. Moreover, the R-function formulation suggests the new concept of a medial zone, which can be thought of as a “thick” skeleton with significant applications for motion planning and other geometric reasoning applications. Our approach can handle problems in which the environment is not fully known a priori, and intrinsically supports local and parallel skeleton computations for domains with rigid or evolving boundaries. Furthermore, our path planning algorithm can be implemented in any commercial geometric kernel, and has attractive computational properties. The capability of the proposed technique are explored through several examples designed to simulate highly dynamic environments. [ABSTRACT FROM AUTHOR]

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible. [ABSTRACT FROM AUTHOR]

Abstract: Distance is a variable of pivotal importance in transport studies. Therefore, after checking the validity of a potential criterion method for measuring active commuting route distances, this method was used to assess the validity and reproducibility of four methods of approximating the commuting route distances covered by pedestrians and bicyclists. The methods assessed were: self-estimated distance, straight-line distance, GIS shortest-route distance, and GPS-measured distance. For this purpose, participants were recruited when walking or bicycling in Stockholm, Sweden. Questionnaires and individually-adjusted maps were sent twice to 133 participants. The distances of map-drawn commuting routes functioned as criterion distances. The participants were also asked to estimate their distances. The straight-line distance between origin and destination was measured using map-drawn routes. The shortest route between home addresses and workplace addresses was calculated with three GIS algorithms. Eighty-six trips were measured with GPS. The main results were that test–retest intraclass correlation coefficients (ICC) exceeded 0.99 for all methods, except for self-estimated distance (ICC=0.76). No order effects existed between test and retest. Significant differences were, however, noted between criterion distance and self-estimated distance (114±63%), straight-line distance (79.1±10.5%), GIS shortest route (112±18% to 121±22%) and GPS distance (105±4%). We conclude that commonly-used distance estimation methods produce systematic errors of differing magnitudes when used in a context of active commuting in suburban and urban environments. These errors can at average level be corrected for, whereas individual relative errors will remain. [Copyright &y& Elsevier]

We consider the problem of constructing roadmaps of real algebraic sets. This problem was introduced by Canny to answer connectivity questions and solve motion planning problems. Given s polynomial equations with rational coefficients, of degree D in n variables, Canny's algorithm has a Monte Carlo cost of $s^{n}\log(s)D^{O(n^{2})}$ operations in ℚ; a deterministic version runs in time $s^{n}\log(s)D^{O(n^{4})}$ . A subsequent improvement was due to Basu, Pollack, and Roy, with an algorithm of deterministic cost $s^{d+1}D^{O(n^{2})}$ for the more general problem of computing roadmaps of a semi-algebraic set ( d≤ n is the dimension of an associated object). We give a probabilistic algorithm of complexity $(nD)^{O(n^{1.5})}$ for the problem of computing a roadmap of a closed and bounded hypersurface V of degree D in n variables, with a finite number of singular points. Even under these extra assumptions, no previous algorithm featured a cost better than $D^{O(n^{2})}$ . [ABSTRACT FROM AUTHOR]

Floating car data (FCD) is an important source of traffic data for the design and management of an intelligent transportation system, and the efficiency and accuracy of FCD map-matching influences the application of FCD. Most map-matching algorithms are very time-consuming because many links need to be evaluated as to whether they are the correct link for the travelling floating car, especially in urban areas where most FCD are collected. In fact, the highest degree of map-matching a link for an FCD is not always the correct link, so the use of candidate links is proposed here. In this study, candidate links are defined as those that fall within the FCD error. We propose a novel algorithm for quickly identifying accurate candidate links on the basis of a vector to raster road map conversion approach. First, a road buffer is constructed based on the size of the confidence region by taking into account the position error of the FCD and the width of the link. Second, all link buffers are converted to a raster map with resolution in metres, each pixel position of the raster map corresponding to a geographical coordinate. A pixel can have one or more values, which represent the link ID whether the link buffer covers the pixel; so a spatial index map between geographical coordinate and link ID is built in this way. Then, candidate links are calculated rapidly in the next three steps: (1) Selecting a link ID from the spatial index map that, according to the FCD positioning fix coordinate, must fall within the confidence region of the FCD. The number is very small and this step is very fast because it does not include a traverse process. (2) Getting data for selected link from the road networks vector map by the link ID. This step uses a binary search and is very fast. (3) Calculating the degree of map-matching of these links to identify candidate links of the FCD. This algorithm is far less time-consuming than earlier approaches. The algorithm was tested using more than 13 million real-world FCD collected in Wuhan City, China, and the performance was shown to be satisfactory. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce a roadmap algorithm for generating collision-free paths in terms of cubic B-spline curves for unmanned vehicles used in mining operations. The algorithm automatically generates collision-free paths that are curvature continuous with an upper bounded curvature and a small slope discontinuity of curvature at knots, when we are given the locations of the obstacles, the boundary geometry of the working area, positions and directions of the vehicle at the start, loading, and the goal points. Our algorithm also allows us to find a switch back point where the vehicle reverses its direction to enter the loading area. Examples are provided to demonstrate the effectiveness of the proposed algorithms. [Copyright &y& Elsevier]

Abstract: Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services. [Copyright &y& Elsevier]

Landmark-based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable for automotive navigation. In this paper, a light-weight embedded algorithm for high speed car navigation in the roads with branches is presented which can be efficiently used in real-time automotive systems. We implemented the proposed algorithm on a real-time MIPS-based embedded system and analyzed its accuracy and efficiency in some real road maps, especially for high speed movements in real roads. Experimental results show that the proposed algorithm can be used for high speeds (up to about 360 km/h) with a very small error rate. Additionally, the experimental results show that the power consumption of the proposed system is suitable for built-in car applications. [ABSTRACT FROM AUTHOR]

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive elastic roadmaps for global path planning among moving obstacles. A single global road map maintains the environment connectivity information that each agent queries simultaneously. Our algorithm takes into account dynamic obstacles and interagent interaction forces to continuously update the road map based on a physically based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of link bands" to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of heterogeneous human agents in indoor and outdoor scenes. [ABSTRACT FROM AUTHOR]

Abstract  Results are presented for analysis of the accuracy of contemporary approaches to forming constraint equations used with correction of systems for terrestrial navigation by digital road maps. On the basis of analysing the results of statistical tests, correction algorithms are proposed for terrestrial navigation systems obtained in accordance with the approaches analysed. Fields of application are determined for the approaches in question. [ABSTRACT FROM AUTHOR]

Abstract: Map-matching algorithms are used to integrate positioning data with digital road network data so that vehicles can be placed on a road map. However, due to error associated with both positioning and map data, there can be a high degree of uncertainty associated with the map-matched locations. A quality indicator representing the level of confidence (integrity) in map-matched locations is essential for some Intelligent Transport System applications and could provide a warning to the user and provide a means of fast recovery from a failure. The objective of this paper is to determine an empirical method to derive the integrity of a map-matched location for three previously developed algorithms. This is achieved by formulating a metric based on various error sources associated with the positioning data and the map data. The metric ranges from 0 to 100 where 0 indicates a very high level of uncertainty in the map-matched location and 100 indicates a very low level of uncertainty. The integrity method is then tested for the three map-matching algorithms in the cases when the positioning data is from either a stand-alone global positioning system (GPS) or GPS integrated with deduced reckoning (DR) and for map data from three different scales (1:1250, 1:2500, and 1:50000). The results suggest that the performance of the integrity method depends on the type of map-matching algorithm and the quality of the digital map data. A valid integrity warning is achieved 98.2% of the time in the case of the fuzzy logic map-matching algorithm with positioning data come from integrated GPS/DR and a digital map data with a scale of 1:2500. [Copyright &y& Elsevier]