Symmetry-Based Search Space Reduction For Grid Maps

In this paper we explore a symmetry-based search space reduction technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by up to 38 times. Our technique decomposes grid maps into a set of empty rectangles, removing from each rectangle all interior nodes and possibly some from along the perimeter. We then add a series of macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. We also develop a novel online pruning technique to further speed up search. Our algorithm is fast, memory efficient and retains the same optimality and completeness guarantees as searching on an unmodified grid map

Best-first heuristic search for multicore machines

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals

Hierarchical path-finding for Navigation Meshes (HNA*)

Path-finding can become an important bottleneck as both the size of the virtual environments and the number of agents navigating them increase. It is important to develop techniques that can be efficiently applied to any environment independently of its abstract representation. In this paper we present a hierarchical NavMesh representation to speed up path-finding. Hierarchical path-finding (HPA*) has been successfully applied to regular grids, but there is a need to extend the benefits of this method to polygonal navigation meshes. As opposed to regular grids, navigation meshes offer representations with higher accuracy regarding the underlying geometry, while containing a smaller number of cells. Therefore, we present a bottom-up method to create a hierarchical representation based on a multilevel k-way partitioning algorithm (MLkP), annotated with sub-paths that can be accessed online by our Hierarchical NavMesh Path-finding algorithm (HNA*). The algorithm benefits from searching in graphs with a much smaller number of cells, thus performing up to 7.7 times faster than traditional A¿ over the initial NavMesh. We present results of HNA* over a variety of scenarios and discuss the benefits of the algorithm together with areas for improvement.Peer ReviewedPostprint (author's final draft

A comprehensive study on pathfinding techniques for robotics and video games

This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas

Performance Evaluation of Pathfinding Algorithms

Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls

Toward human-like pathfinding with hierarchical approaches and the GPS of the brain theory

Pathfinding for autonomous agents and robots has been traditionally driven by finding optimal paths. Where typically optimality means finding the shortest path between two points in a given environment. However, optimality may not always be strictly necessary. For example, in the case of video games, often computing the paths for non-player characters (NPC) must be done under strict time constraints to guarantee real time simulation. In those cases, performance is more important than finding the shortest path, specially because often a sub-optimal path can be just as convincing from the point of view of the player. When simulating virtual humanoids, pathfinding has also been used with the same goal: finding the shortest path. However, humans very rarely follow precise shortest paths, and thus there are other aspects of human decision making and path planning strategies that should be incorporated in current simulation models. In this thesis we first focus on improving performance optimallity to handle as many virtual agents as possible, and then introduce neuroscience research to propose pathfinding algorithms that attempt to mimic humans in a more realistic manner.In the case of simulating NPCs for video games, one of the main challenges is to compute paths as efficiently as possible for groups of agents. As both the size of the environments and the number of autonomous agents increase, it becomes harder to obtain results in real time under the constraints of memory and computing resources. For this purpose we explored hierarchical approaches for two reasons: (1) they have shown important performance improvements for regular grids and other abstract problems, and (2) humans tend to plan trajectories also following an topbottom abstraction, focusing first on high level location and then refining the path as they move between those high level locations. Therefore, we believe that hierarchical approaches combine the best of our two goals: improving performance for multi-agent pathfinding and achieving more human-like pathfinding. Hierarchical approaches, such as HNA* (Hierarchical A* for Navigation Meshes) can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, the method suffers from a bottleneck in the step that connects the Start and Goal positions with the hierarchy. This bottleneck can drop performance drastically.In this thesis we present different approaches to solve the HNA* bottleneck and thus obtain a performance boost for all hierarchical configurations. The first method relies on further memory storage, and the second one uses parallelism on the GPU. Our comparative evaluation shows that both approaches offer speed-ups as high as 9x faster than A*, and show no limitations based on hierarchical configuration. Then we further exploit the potential of CUDA parallelism, to extend our implementation to HNA* for multi-agent path finding. Our method can now compute paths for over 500K agents simultaneously in real-time, with speed-ups above 15x faster than a parallel multi-agent implementation using A*. We then focus on studying neurosience research to learn about the way that humans build mental maps, in order to propose novel algorithms that take those finding into account when simulating virtual humans. We propose a novel algorithm for path finding that is inspired by neuroscience research on how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid, based on the GPS of the brain theory, and fires memory cells as counters. 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 GPS of the brain cognitive map.El problema de Pathfinding para agentes autónomos o robots, ha consistido tradicionalmente en encontrar un camino óptimo, donde por óptimo se entiende el camino de distancia mínima entre dos posiciones de un entorno. Sin embargo, en ocasiones puede que no sea estrictamente necesario encontrar una solución óptima. Para ofrecer la simulación de multitudes de agentes autónomos moviéndose en tiempo real, es necesario calcular sus trayectorias bajo condiciones estrictas de tiempo de computación, pero no es fundamental que las soluciones sean las de distancia mínima ya que, con frecuencia, el observador no apreciará la diferencia entre un camino óptimo y un sub-óptimo. Por tanto, suele ser suficiente con que la solución encontrada sea visualmente creíble para el observado. Cuando se simulan humanoides virtuales en aplicaciones de realidad virtual que requieren avatares que simulen el comportamiento de los humanos, se tiende a emplear los mismos algoritmos que en video juegos, con el objetivo de encontrar caminos de distancia mínima. Pero si realmente queremos que los avatares imiten el comportamiento humano, tenemos que tener en cuenta que, en el mundo real, los humanos rara vez seguimos precisamente el camino más corto, y por tanto se deben considerar otros aspectos que influyen en la toma de decisiones de los humanos y la selección de rutas en el mundo real. En esta tesis nos centraremos primero en mejorar el rendimiento de la búsqueda de caminos para poder simular grandes números de humanoides virtuales autónomos, y a continuación introduciremos conceptos de investigación con mamíferos en neurociencia, para proponer soluciones al problema de pathfinding que intenten imitar con mayor realismo, el modo en el que los humanos navegan el entorno que les rodea. A medida que aumentan tanto el tamaño de los entornos virtuales como el número de agentes autónomos, resulta más difícil obtener soluciones en tiempo real, debido a las limitaciones de memoria y recursos informáticos. Para resolver este problema, en esta tesis exploramos enfoques jerárquicos porque consideramos que combinan dos objetivos fundamentales: mejorar el rendimiento en la búsqueda de caminos para multitudes de agentes y lograr una búsqueda de caminos similar a la de los humanos. El primer método presentado en esta tesis se basa en mejorar el rendimiento del algoritmo HNA* (Hierarchical A* for Navigation Meshes) incrementando almacenamiento de datos en memoria, y el segundo utiliza el paralelismo para mejorar drásticamente el rendimiento. La evaluación cuantitativa realizada en esta tesis, muestra que ambos enfoques ofrecen aceleraciones que pueden llegar a ser hasta 9 veces más rápidas que el algoritmo A* y no presentan limitaciones debidas a la configuración jerárquica. A continuación, aprovechamos aún más el potencial del paralelismo ofrecido por CUDA para extender nuestra implementación de HNA* a sistemas multi-agentes. Nuestro método permite calcular caminos simultáneamente y en tiempo real para más de 500.000 agentes, con una aceleración superior a 15 veces la obtenida por una implementación paralela del algoritmo A*. Por último, en esta tesis nos hemos centrado en estudiar los últimos avances realizados en el ámbito de la neurociencia, para comprender la manera en la que los humanos construyen mapas mentales y poder así proponer nuevos algoritmos que imiten de forma más real el modo en el que navegamos los humanos. Nuestro método representa el espacio como una red hexagonal, basada en la distribución de ¿place cells¿ existente en el cerebro, e imita las activaciones neuronales como contadores en dichas celdas. Nuestro buscador de rutas combina un método para explorar entornos desconocidos mientras construye un mapa cognitivo hexagonal, utilizando una búsqueda A* con una nueva heurística adaptada al mapa cognitivo del cerebro y sus contadores

Pathfinding in hierarchical representation of large realistic virtual terrains

Pathfinding is critical to virtual simulation applications. One of the most prominent pathfinding challenges is the fast computation of path plans in large and realistic virtual terrain environments. To tackle this problem, this work proposes the exploration of a quadtree structure in the navigation map representation of large real-world virtual terrains. Exploring a hierarchical approach for virtual terrain representation, we detail how a global hierarchical pathfinding algorithm searches for a path in a coarse initial navigation map representation. Then, during execution time, the pathfinding algorithm refines regions of interest in this terrain representation in order to compute paths with a higher quality in areas where a large amount of navigation obstacles is found. The computational time of such hierarchical pathfinding algorithm is systematically measured in different hierarchical and non-hierarchical terrain representation structures that are instantiated in the modeling of a small real-world terrain scenario. Then, similar experiments are developed in a large real-world virtual terrain that is inserted in a real-life simulation system for the development of military tactical training exercises. The results show that the computational time required to generate pathfinding answers can be optimized when the proposed hierarchical pathfinding algorithm along with the easy and reliable implementation of the quadtree-based navigation map representation of the large virtual terrain are explored in the development of simulation systems

Direction based Heuristic for Pathfinding in Video Games

Pathfinding has been one of major research areas in video games for many years. It is a key problem that most of the video games are confronted with. Search algorithms such as the A* algorithm and the Dijkstra2019;s algorithm representing such regular grid, visibility graphs also have significant impact on the performance. This paper reviews the current widely used solutions for pathfinding and proposes a new method which is expected to generate a higher quality path using less time and memory than other existing solutions. The deployment of the methodologies and techniques is described in detail. The significance of the proposed method in future video games is addressed and the conclusion is given at the end

Influence map-based pathfinding algorithms in video games

Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems by intelligent agents, ranging from computer games and applications to robotics. Pathfinding is a particular kind of search, in which the objective is to find a path between two nodes. A node is a point in space where an intelligent agent can travel. Moving agents in physical or virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able to navigate through its surrounding environment without avoiding obstacles, it does not seem intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games. Pathfinding algorithms work well with single agents navigating through an environment. In realtime strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large and dynamic game environments. On the contrary, influence maps are not used in pathfinding. Influence maps are a spatial reasoning technique that helps bots and players to take decisions about the course of the game. Influence map represent game information, e.g., events and faction power distribution, and is ultimately used to provide game agents knowledge to take strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g., capture an enemy location and win the game. Tactical decisions are based on small and precise actions, e.g., where to install a turret, where to hide from the enemy. This dissertation work focuses on a novel path search method, that combines the state-of-theart pathfinding algorithms with influence maps in order to achieve better time performance and less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho mais curto, desde a àrea jogos de computador até à robótica. Pathfinding é um tipo particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar. Agentes móveis em mundos físicos e virtuais são uma componente chave para a simulação de comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz parte das tarefas fundamentais de inteligencia artificial em vídeo jogos. Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados no pathfinding. Influence maps são uma técnica de raciocínio espacial que ajudam agentes inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo. Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding com influence maps, afim de alcançar melhores performances a nível de tempo de pesquisa e consumo de memória, assim como obter caminhos visualmente mais suaves