Finding Optimal Paths Research Articles

Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework

Road is one of important traffic lifelines that could be damaged after disaster by landslide rubble, buildings debris, and collapsed branches of trees. Therefore, road damage detection and assessment using post-Disaster High-Resolution Remote Sensing Images is extremely important for finding optimal paths and conducting rescue missions. In an emergency context, the existing methods based on change detection for road damage detection are difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster remote sensing imagery are hard to obtain. In this paper, a novel method based on the Tracking, Learning, and Detector (TLD) framework for detecting the damaged road region from post-disaster high-resolution remote sensing image is presented. First, a spoke wheel operator is employed to define the initial template of road. Then, the TLD framework is used to identify the suspected road damaged areas. Finally, the damaged road areas are extracted by pruning the false damaged roads. The proposed method was evaluated using post-disaster high-resolution remote sensing images collected over Beichuan, China in 2008 and Lushan, China in 2013. The results show that the proposed method is feasible and effective for road damage detection and assessment. Our main conclusion is that such an approach qualifies for practical use.

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.

Spatial Analysis on Networks: Towards identifying similarity of routes

Finding an optimal path in a road network is a method of planning and decision-making that is mainly related to transportations and emergency response. The paper presents an algorithm for finding optimal paths in spatial networks, through the utilization of open source GIS and mathematical analysis of Networks using Graph Theory as well as using geographical proximity attributes of network nodes. The geometric and spatial information of the network as well as its relations with points of interest (POI) of the study areas located at the nodes and edges of the network, are transformed into spatial information, which by applying spatial queries in a geographical database (Postgis/Pgrouting) give query-enabled paths. The case study for the application of the algorithm and finding a route based on spatial queries is the island of Lesvos. This island combines intense topography and a complex road network with multiple geometric relationships. The area also has points of interest such as cultural, tourist and social. The final route will be a synthesis of morphological, tourist and cultural elements similar to the spatial search queries. Finally, the methodology as well as the search algorithm can be applied to any Spatial Network (transportations, environment, energy) described by its geographical features, considering all kinds of geographical issues, thus solving spatial problems and contributing to local development.

An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA).

Optimal Any-Angle Pathfinding on a Sphere


 
 
 
 Pathfinding in Euclidean space is a common problem faced in robotics and computer games. For long-distance navigation on the surface of the earth or in outer space however, approximating the geometry as Euclidean can be insufficient for real-world applications such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya as described by Harabor & Grastien for Euclidean space can be extended to Spherical geometry. There, where the shortest-distance line between coordinates is defined instead by a great-circle path, the optimal solution is typically a curved line in Euclidean space. In addition the turning points for optimal paths in Spherical geometry are not necessarily corner points as they are in Euclidean space, as will be shown, making further substantial adaptations to Anya necessary. Spherical Anya returns the optimal path on the sphere, given these different properties of world maps defined in Spherical geometry. It preserves all primary benefits of Anya in Euclidean geometry, namely the Spherical Anya algorithm always returns an optimal path on a sphere and does so entirely on-line, without any preprocessing or large memory overheads. Performance benchmarks are provided for several game maps including Starcraft and Warcraft III as well as for sea navigation on Earth using the NOAA bathymetric dataset. Always returning the shorter path compared with the Euclidean approximation yielded by Anya, Spherical Anya is shown to be faster than Anya for the majority of sea routes and slower for Game Maps and Random Maps. 
 
 
 

Effective rural electrification via optimal network: Optimal path-finding in highly anisotropic search space using multiplier-accelerated A* algorithm

United Nations’ 7th Sustainable Development Goal envisions the availability of modern energy for everyone by 2030. While the progress has been satisfactory in the last few years, further rural electrification is increasingly challenging. The current mainstream approach of electrifying villages individually is becoming cost-ineffective due to uncertainties in both resource availability and energy demand for small, difficult-to-reach, residences. A networked rural electrification model, i.e. a cost-optimized network connecting villages and generation facilities, could improve resources utilization, reliability and flexibility. However, determining optimal paths with common search algorithms is extremely inefficient due to complex topographic features of rural areas. This work develops and applies an artificial intelligence search method to efficiently route inter-village power connections in the common rural electrification situation where substantial topological variations exist. The method is evolved from the canonical A* algorithm. Results compare favorably with optimal A* results, at significantly reduced computational effort. Furthermore, users can adaptively trade-off between computation speed and optimality and hence quickly evaluate sites and configurations at reasonable accuracy, which is impossible with classical methods.

An Efficient Congestion Avoiding Approach for Optimal Path Finding for Emergency Vehicle

Background: Emergency vehicles required a quick clearance so as to reach the destination with minimum delay and the human life could be saved. Emergency vehicle required a dedicated path for clearance. The dedicated path creates a chaos by blocking the entire route for others vehicles and it is not always possible to create a dedicated path. So there is an imperative need for a smart road navigation system which adapts to the traffic congestion in real-time velocity of vehicle, count of vehicle, number of lanes and distance from source to destination. Objective: The objective of this paper is to find an optimal route for providing a least congested optimal route for emergency vehicle with least delay considering various issues such as congestion, numbers of vehicles, average traffic flow on the roads and width of the lane between source and destination. Proposed approach: Real-time traffic data like number of vehicles, velocity of the vehicles, and the width of the road and distance of the route are used to determine the congestion factor on all possible route. Congestion factor is used for finding the shortest route to requesting emergency vehicle. Result: Experimental results establish that the travel time of a vehicle is reduced by approximately 26%, when the vehicle uses the optimized route. This is beneficial for any emergency vehicle as the optimal path is provided on a real-time basis. Conclusion: This research work proposes an analytical approach that provides the least congested optimal route on-demand based on real-time traffic.

Improved Heuristics for Optimal Pathfinding on Game Maps

As computer game worlds get more elaborate the more visible pathfinding performance bottlenecks become. The heuristic functions typically used for guiding A*-based path inding are too simplistic to provide the search with the necessary guidance in such large and complex game worlds. This may result in A*-search exploring the entire game map in order to find a path between two distant locations. This article presents two effective heuristics for estimating distances between locations in large and complex game maps. The former, the dead-end heuristic, eliminates from the search map areas that are provably irrele- vant for the current query, whereas the second heuristic uses so-called gateways to improve its estimates. Empirical evaluation on actual game maps shows that both heuristics reduce the exploration and time complexity of A* search significantly over a standard octile distance metric.

Towards a human-like approach to path finding

Path finding for autonomous agents has been traditionally driven by finding optimal paths, typically by using A* search or any of its variants. When it comes to simulating virtual humanoids, traditional approaches rarely consider aspects of human memory or orientation. In this work, we propose a new path finding algorithm, inspired by current research regarding how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid with counters, based on brain research that has investigated how memory cells are fired. Our path finder then combines a method for exploring unknown environments while building such a cognitive map, with an A* search using a modified heuristic that takes into account the cognitive map. The resulting paths show how as the agent learns the environment, the paths become shorter and more consistent with the optimal A* search. Moreover, we run a perceptual study to demonstrate that the viewers could successfully identify the intended level of knowledge of the simulated agents. This line of research could enhance the believability of autonomous agents’ path finding in video games and other VR applications.

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.

Benchmarks for Pathfinding in 3D Voxel Space

The problem of finding optimal paths in 3D space is computationally more complex than in a 2D plane. While a range of different approaches have been developed across different fields, there has been relatively little work studying the complexities and tradeoffs of implementations for path planning in 3D space. This paper describes the 3D path planning problem faced in the game Warframe. The solution used by industry was tuned for their own constraints, but little is known about the applicability of other approaches to solving this problem. Thus, the makers of this game have made their underlying planning data available for researchers to use. This paper describes this new data set which is publicly available for study and dissemination.

Fast, Optimal Pathfinding with Compressed Path Databases

Most existing pathfinding methods are based on runtime search. Despite an impressive progress achieved in recent years, search-based methods could explore, in bad cases, large portions of a map, leading to an increased running time. We take a significantly different approach from search-based methods. Given a map, our program precomputes all-pairs shortest paths (APSP) information. APSP data are compressed, reducing the size dramatically with no information loss. We call the resulting data a compressed path database (CPD). At runtime, optimal moves are retrieved one by one until a complete (or partial, if desired) optimal path is retrieved. CPDs lead to a fast path computation, eliminating the need for runtime search. The first move lag is very low, as each move is retrieved independently from subsequent moves. The price to pay includes a significant preprocessing time and memory to build and store a CPD.

The JPS Pathfinding System

We describe a pathfinding system based on Jump Point Search (JPS): a recent and very successful search strategy that performs symmetry breaking to speed up optimal pathfinding on grid maps. We first modify JPS for grid maps where corner-cutting moves are not allowed. We then describe JPS+: a new derivative search strategy that reformulates an input graph into an equivalent symmetry-reduced form that can be searched more efficiently. JPS and JPS+ were both submitted to the 2012 Grid-based Path Planning Competition.

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.

Pruning Techniques for the Increasing Cost Tree Search for Optimal Multi-agent Pathfinding

We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional single-agent search variants of the A* algorithm. In Sharon et. al. (2011), we introduced a novel two-level search algorithm framework for this problem. The high-level searches a novel search tree called increasing cost tree (ICT). The low-level performs a goal test on each ICT node. The new framework, called ICT search (ICTS), showed to run faster than the previous state-of-the-art A* approach by up to three orders of magnitude in many cases. In this paper we focus on the low-level of ICTS which performs the goal test. We introduce a number of optional pruning techniques that can significantly speed up the goal test. We discuss these pruning techniques and provide supporting experimental results.

Optimal path finding in stochastic quasi-dynamic environments using particle swarm optimization

Finding the optimal path for a mobile robot in complex environments is a crucial part of automation. In this application, we seek a path that avoids obstacles inside the scene. This paper discusses the path planning problem in cases where 2D obstacles change their areas in stochastic form. The trade-off between shortness and safety of the path is captured by two objective functions: distance-based and risk-based objective functions. The Risk-based objective function describes how likely the suggested path avoids the obstacles. Particle swarm optimization (PSO) is employed to find the best path. A new set of parameters are introduced to the problem model to capture the stochastic nature of the environment. The range and behavior of these parameters are studied under different difficulty-level scenarios. The results show that the proposed approach can predict the stochastic changes in obstacles and plan a path that is simultaneously safe and short, as compared to the regular approach with a standard distance-only cost function. These results manifest the use of the proposed approach in environments where there are elastic objects that can block the path between two points.

Optimal path discovery for two moving sinks with a common junction in a wireless sensor network

A new algorithm is described for determining the optimal round-trip paths for two moving sinks in a wireless sensor network. The algorithm uses binary integer programming to select two non-overlapping shortest paths except having a common junction node to cover all the sensor nodes. The two paths are balanced as nearly equal as possible. That is the sensor nodes along each path are equal or differ by just one depending on whether the total number of sensor nodes excluding the junction node is even or odd. In this method, both the path lengths are made equal or very nearly equal while the total length is minimized. This integrated approach is a novel and unique solution to solve the dual moving sink path problem in a wireless sensor network.

The Closed List Is an Obstacle Too

The baseline approach for optimal path finding in 4-connected grids is A* with Manhattan Distance. Nevertheless, a large number of enhancements were suggested over the years, usually requiring a preprocessing phase and/or additional memory to store smart lookup tables. In this paper we introduce an enhancement to A* (called BOXA*) on grids which does not need any preprocessing and only needs negligible additional memory. The main idea is to treat the closed-list as a dynamic obstacle. We maintain a list of rectangles which surround CLOSED nodes and calculate an admissible heuristic using the fact that an optimal path from a given node must go around these rectangles. We experimentally show the benefits of this approach on a variety of grid domains.

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.