Optimal Path Finding Algorithm Research Articles

Optimal path finding in 3D environment: Application for the exhibition space through 3D visualization and BIM

The application of 3D visualization technology in building construction has also increased. The study used Revit software to construct a 3D building information model (BIM) for the exhibition space of Chuzhou Higher Education City Development Collaborative Innovation Center to achieve a 3D visualization display; based on the 3D visualization, a particle swarm optimization (PSO) algorithm was used to find the optimal path for the exhibition space, so as to achieve the layout design of the exhibition space. The PSO algorithm was optimized in terms of inertia weight, acceleration coefficient, and initial population to obtain the improved PSO (IPSO) algorithm. The experimental results showed that the optimal path found by the IPSO algorithm was 78.56 meters in distance, 98.2 seconds in time consumption, and 50.11% in smoothness, which were better than the other two algorithms. Meanwhile, the IPSO algorithm had a lower value of particle fitness function, indicating that the IPSO algorithm had the highest performance and the strongest path finding ability among the three algorithms. It is confirmed that it is feasible to use the IPSO algorithm for optimal visit path finding in 3D environment. It is effective to visualize the exhibition space in 3D by constructing a BIM.

Reliable efficient cluster routing protocol based HTDE scheme for UWSN

<p>Underwater sensor networks (UWSNs) are recently recognized as a promising method for monitoring and exploring the underwater environment. Due to real-time remote data monitoring requirements, UWSN has become a preferred network to a large extent. But reliable and efficient secure data transmission to the receiver is one of the most important challenges for UWSNs, often suffer from irreplaceable batteries and high latency for long-distance communications. The most challenging task is extending network life, shortening transmission distances for each node. Thus, this paper presents the reliable efficient cluster routing (RECR) protocol, optimal shortest path finding (OSPF) algorithm and hill transformation data encryption (HTDE) algorithm for UWSNs. The proposed encryption algorithm encrypts the data from the source node, and the RECR protocol is used for reliable data delivery from source to destination. To extend the network's life, RECR employs autonomous underwater vehicle (AUV) from the sink node (SN) for data collection. The OSPF algorithm is used to find the shortest path for data transmission to avoid latency. The proposed RECR protocol, HTDE and OSPF algorithm enhance the secure data transmission efficiency, minimizing the energy consumption and improves the network life time. The proposed protocol decreases end-to-end latency, packet loss.</p>

Flexible networked rural electrification using levelized interpolative genetic algorithm

Networked rural electrification is an alternative approach to accelerate rural electrification. Using satellite photos and GIS tools, an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost. To design the network, optimal paths connecting all node-pairs are identified, followed by finding a network topology that minimizes cost. Earlier work has illustrated that A* (A-star, an optimal path-finding algorithm) is inefficient for this application due to the complex topography in rural areas. The multiplier-accelerated A* (MAA*) algorithm overcomes key performance issues, but, like A*, produces only one path connecting each node-pair. Relying on one path increases project risk because adverse conditions, such as inaccurate GIS estimation, unexpected soil conditions, land-rights disputes, political issues, etc. can occur during implementation. In this paper, a hybrid path-finding method combining genetic algorithm and A* / MAA* algorithm is proposed. The proposed method provides a family of near-optimal paths instead of a single optimal path for routing. A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available, and flexibly change network topology before or during implementation with minimal impact on project cost.

A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport

In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is added to the public transport network, passengers choose the fastest route to their destination. The proposed methodology is applied to actual lines (bus and subway lines) in Seoul, the capital of South Korea, and it is validated through the observed traffic volume of those lines recorded in the smart card data. The experiments are conducted using smart card data, with more than 100 million trips stored, extracted from about 1 million passengers who have check-in records in the catchment area of the new lines. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data.

AI intelligent wayfinding based on Unreal Engine 4 static map

The development of games is closely related to the development of game engines. Unreal Engine is a mainstream game development engine. Unreal Engine has a powerful lighting rendering function and blueprint programming system.Therefore, using Unreal Engine for game development will better meet the requirements of modern game users for game quality and game content. In games, the AI path-finding system is often the core of a game. However, the path-finding component of the unreal engine is usually only applies to three-dimensional game and is used for intelligent path-finding in dynamic environment. Howerve, the 2D game usually finds the way in static environment. At present, there are a lareg number of path-finding algorithms and theoretical studies based on static environment, but there is no simulation experiment and detailed explanation of intelligent path-finding in static environment based on the Unreal Engine platform. This article firstly explains the principle of path-finding algorithms that are several commonly used in static environment. Then it compares the performance of different algorithms, and selects the optimal solution among several schemes by summarizing the experimental results. Then based on the optimal path-finding algorithm, using blueprint programming to implement AI path-finding in the Unreal engine. Finally, this article sort out and summarize the overall work process and indicate direction for future optimization.

An Optimal Path-Finding Algorithm in Smart Cities by Considering Traffic Congestion and Air Pollution

Finding the shortest and cleanest path in the cities is vital, especially in metropolises. Although several algorithms and some software have been introduced to manage the traffic or suggest a path with minimum traffic congestion, none considers air quality a deciding factor. This paper introduces a novel algorithm to find the shortest path based on traffic congestion and air quality. In the proposed algorithm, the city map is fetched from the Google Map app and is converted into a weighted graph. Traffic data is collected from GPS devices, which will be available through the local cloud services. The C-means clustering method is used to cluster traffic congestion. Also, the air quality information is collected from air pollution monitoring stations. The graph weights are calculated based on both air quality and traffic congestion factors, simultaneously. Finding the shortest path problem is then defined as an optimization problem, and the linear programming method is used to solve it. Finally, the proposed algorithm’s performance is evaluated by finding the shortest path in Tehran, Iran in different scenarios.

F-Cardinal Conflicts in Conflict-Based Search

Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF) which features strong performance. In CBS, one conflict in a high-level node is resolved to generate two child nodes, until a node with no conflicts is found. Choosing the right conflict to resolve can greatly speed up the search. It is currently recommended to resolve cardinal conflicts first, resolving them yields two child nodes with a higher cost than the cost of their parent. However, since the recent addition of high-level heuristics to CBS, when resolving cardinal conflicts, the h-value of high-level child nodes often decreases by the same amount as their cost increases. This diminishes the effectiveness of the cardinal conflicts distinction. We propose an expanded categorization of conflicts into f-cardinal, g-cardinal, and non-cardinal. F-cardinal conflicts should be resolved first. Resolving f-cardinal conflicts generates child nodes with an increased f-value relative to their parent. We propose two methods for identifying f-cardinal conflicts. Finally, we demonstrate on standard benchmarks that choosing conflicts according to this expanded categorization increases the effectiveness of modern CBS.

Conflict-Based Search for Optimal Multi-Agent Path Finding

We present a new two-level search algorithm for optimal multi-agent path finding called Conflict Based Search (CBS). At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. Experimental results on various problems shows a speedup of up to a full order of magnitude over previous approaches.

Further Improved Heuristics For Conflict-Based Search

Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). At its high level, CBS expands nodes by resolving conflicts. In recent years, admissible heuristics were added to the high level of CBS. We enhance all known heuristic functions for CBS by using information about the cost of resolving certain conflicts, with only a small computational overhead. We experimentally demonstrate that the improved heuristics contribute to making CBS even more efficient.

Experimental Evaluation of Classical Multi Agent Path Finding Algorithms

Modern optimal multi-agent path finding (MAPF) algorithms can scale to solve problems with hundreds of agents. To facilitate comparison between these algorithms, a benchmark of MAPF problems was recently proposed. We report a comprehensive evaluation of a diverse set of state-of-the-art optimal MAPF algorithms over the entire benchmark. The results show that in terms of coverage, the recently proposed LazyCBS algorithm outperforms all others significantly, but it is usually not the fastest algorithm. This suggests algorithm selection methods can be beneficial. Then, we characterize different setups for algorithm selection in MAPF, and evaluate simple baselines for each setup. Finally, we propose an extension of the existing MAPF benchmark in the form of different ways to distribute the agents’ source and target locations.

F-Aware Conflict Prioritization & Improved Heuristics For Conflict-Based Search

Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). The main step of CBS is to expand nodes by resolving conflicts (where two agents collide). Choosing the ‘right’ conflict to resolve can greatly speed up the search. CBS first resolves conflicts where the costs (g-values) of the resulting child nodes are larger than the cost of the node to be split. However, the recent addition of high-level heuristics to CBS and expanding nodes according to f=g+h reduces the relevance of this conflict prioritization method. Therefore, we introduce an expanded categorization of conflicts, which first resolves conflicts where the f-values of the child nodes are larger than the f-value of the node to be split, and present a method for identifying such conflicts. We also enhance all known heuristics for CBS by using information about the cost of resolving certain conflicts, and with only a small computational overhead. Finally, we experimentally demonstrate that both the expanded categorization of conflicts and the improved heuristics contribute to making CBS even more efficient.

An Application of a Link-Based Optimal Pathfinding Algorithm with the Integrated Transit Network from Smart Card Systems

With the spread of information technology in the transportation field, many cities around the globe have introduced smart card systems to stimulate the use of transit services. The systems have resulted in not only the improvement of users’ convenience but also the dramatic changes of travel behaviors with transit services. One prevailing phenomenon is the user’s perception for all transit modes as one single mode in the integrated transit network because of seamless services provided by smart card systems. Although this change has brought tremendous advantages to the dynamics of transit services, it has become more complicated to understand travel behaviors in the integrated transit network which includes features of transfers, service schedule, waiting time and fares. Given that smart card systems have affluent information which can reflect such characteristics, their proper utilizations are still in question. In this sense, when compared with a single-mode network, specific approaches need to be considered to build an integrated transit network from smart card systems, and to develop a suitable model that can reflect the features of the integrated transit network. With smart card systems in the greater Seoul area, Korea, this research proposes methodological techniques which can create the integrated transit network and facilitate transfers automatically between modes. In addition, an attempt is made to verity the applicability of a link-based optimal pathfinding algorithm that has been spotlighted to consider complex travel behaviors in a transit network but has not been proven in the integrated transit network from smart card systems. The concepts of mode-based link labeling and “big node” for transfers are employed with this algorithm. From their applications, optimal transit paths are successfully searched by proposed concepts with smart card data. Given that building an integrated transit network and finding optimal paths are essential tasks for transit analysis studies, this research provides irreplaceable contributions to utilize link-based optimal pathfinding algorithms in the integrated transit network with smart card systems.

New Valid Inequalities in Branch-and-Cut-and-Price for Multi-Agent Path Finding

BCP, a state-of-the-art algorithm for optimal Multi-agent Path Finding, uses the branch-and-cut-and-price framework to decompose the problem into (1) a master problem that selects a set of collision-free low-cost paths, (2) a pricing problem that adds lower-cost paths to the master problem, (3) separation problems that resolve various kinds of conflicts in the master problem, and (4) branching rules that split the nodes in the high-level branch-and-bound search tree. This paper focuses on the separation aspects of the decomposition by introducing five new classes of fractional conflicts and valid inequalities that remove these conflicts to tighten the linear programming relaxation in the master problem. Experimental results on 12820 instances across 16 maps indicate that including the five families of inequalities allows BCP to solve an additional 585 instances, optimize the same instances 41% faster, and solve 2068 more instances than CBSH-RM and 157 more than Lazy CBS.

Single-Camera Computer Vision Algorithm for Robot Shortest Path Estimator using morphological structuring element with variable sizes

The robotic challeng of developing and implementing a shortest-path finding algorithm to reach a destination without bumping into obstacles has been tackled in this paper. The proposed algorithm is utilized a single ceiling fixed camera and based on the mask and robot size with different sizes for dilation mask. The implementation has been successfully produced thirty-three computer vision figures and two comparison tables of simulation results by using morphological structuring element with different sizes. This paper’s proposal of optimal path finding algorithm is effeciently save robot energy in reaching targets.

Вибір алгоритму пошуку оптимального шляху передавання даних у розподіленій системі

Вибір алгоритму пошуку оптимального шляху передавання даних у розподіленій системі

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

Optimal Multi-Agent Pathfinding Algorithms

The multi-agent path finding (MAPF) problem is a generalization of the single-agent path finding problem for k > 1 agents. It consists of a graph and a number of agents. Foreach agent, a unique start state and a unique goal state are given, the task is to find paths for all agents from their start states to their goal states, under the constraint that agents cannot collide during their movements. In many cases there is an additional goal of minimizing a cumulative cost function such as the sum of the time steps required for every agent to reach its goal. The goal of my research is providing new methods to solve MAPF optimally and provide theoretical understandings that will help choose the best solver given a problem instance.

Research on Optimal Path Finding Algorithm

Optimal path finding algorithm is a very important research direction in computer science. It is a node search method based on graph theory. This paper focus on the fundamental researches of the optimal path finding algorithm. By comparing various path finding algorithms, this paper implement the A* algorithm and present a variant strategy of A* which is named Turning Point A*(TPA*). The experiment shows that searching with TPA* can speed up A* by an order of magnitude and more and report significant improvement.

Pathfinding in the cognitive map: Network models of mechanisms for search and planning

Abstract The hippocampus has long been thought to be critical in learning and representing the cognitive map, and thus support functions such as search, pathfinding and route planning. This work aims to demonstrate the utility of hippocampus-based neural networks in modeling human search task behavior. Human solutions to pathfinding problems are generally fast but approximate, in contrast to traditional AI approaches. In this paper, we report data on a human search task, and then examine a set of models, based upon the structure of the hippocampus, which use a goal scent mechanism similar to the optimal pathfinding algorithms used in artificial intelligence systems. We compare five distinct search models, and conclude that a goal scent model driven by multiple goals spread throughout the search space provides the best and most accurate account of the human data. This research suggests a convergence in traditional AI and biologically-inspired approaches to pathfinding that may be mutually beneficial.

Improving Computation Power by Reducing Query Response Time in Peer-to-Peer Environment

Problem statement: The Peer-to-Peer (P2P) was an emerging model and was being widely adopted in today’s internet computing.P2P traffic contributes the largest portion of the internet traffic which was unnecessary and led to delay. In existing Scalable Bipartite Overlay (SBO) network, Out of “N” peers, one group of peers were probing the messages (or) queries whereas the other group of peers were computing. This resulted in high query response time .Since the system was bipartite all peers were not capable of computing and probing, resulting in increased computation time. Approach: To overcome the above issue we propose a system RQR (Reduction on query response time) which is decentralized and unstructured where each and every peer can perform both probing and computing with no restrictions. Results: Optimal path finding algorithm is designed to find all possible path existing in the P2P network along with the optimal path. Conclusion: Our comparison with earlier approaches show that less than 60% of reduction on query response time which increases the system performance.