Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

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

Reitinetsintä kolmiulotteisissa peliympäristöissä

Reitinetsintä on yleinen haaste, joka pitää ratkaista monenlaisissa videopeleissä. Sen ratkaiseminen on usein perusvaatimuksena pelihahmojen toiminnalle, sillä hahmojen täytyy kyetä suunnistamaan luontevasti pelimaailman läpi. Tämän tutkielman tavoitteena on kartoittaa kolmiulotteisten peliympäristöjen reitinetsintään soveltuvia tekniikoita sekä arvioida niiden ongelmia ja mahdollisuuksia. Tutkielmassa perehdytään ensin yleisimpiin peleissä käytettyihin reitinetsintäalgoritmeihin ja sitten tapoihin, joilla näitä algoritmeja voidaan soveltaa peliympäristöihin. Yleisiksi reitinetsintäalgoritmeiksi osoittautuvat A*-algoritmi, geneettiset algoritmit ja muurahaisyhdyskuntaoptimointi. A*-algoritmi on melko yksinkertainen ja tehokas reitinetsintäalgoritmi, mutta sen toimintakyky heikkenee, jos ympäristössä tapahtuu usein muutoksia. Geneettiset algoritmit ja muurahaisyhdyskuntaoptimointi sen sijaan soveltuvat hyvin myös muuttuviin ympäristöihin. Havaitaan, että A*-algoritmista on tehty erilaisia muunnoksia, jotka tehostavat algoritmin tiettyjä osia, mutta heikentävät toisia. Tällaisen muunnoksen käytöstä voi olla hyötyä, jos tehostetut ominaisuudet ovat pelille tärkeitä. Heikentyneet ominaisuudet täytyy kuitenkin ottaa myös huomioon ja arvioida, miten muunnos vaikuttaisi kokonaisuutena peliin. Jotta reitinetsintäalgoritmeja voidaan käyttää pelissä, peliympäristön pohjalta täytyy ensin muodostaa graafi. Tutkielmassa havaitaan, että tämän graafin toteutustavan valinta on tärkeä reitinetsintäjärjestelmän ominaisuuksiin vaikuttava tekijä. Kirjallisuudesta löydetään kolme yleisesti käytettyä graafimallia, joista jokainen soveltuu paremmin tietynlaisiin käyttökohteisiin ja huonommin toisiin. Näistä kolmesta mallista soveltuvimmaksi kolmiulotteisiin ympäristöihin osoittautuu navigaatioverkko. Navigaatioverkot mallintavat pintoja, joilla pelihahmot voivat kävellä, ja ne mahdollistavat kahta muuta tarkasteltua graafimallia tarkemman ja joustavamman reitinetsinnän. Verkon voi luoda pitkälti automaattisesti peliympäristön pohjalta, mikä säästää aikaa, mutta manuaalinen muokkaaminen on myös mahdollista. Näin pelisuunnittelijat voivat ensin luoda karkean navigaatioverkon automaattisesti ja sitten hienosäätää sitä pelin tarpeisiin. Tutkielman yhteydessä toteutetaan myös yksinkertainen navigaatioverkkoja hyödyntävä reitinetsintäjärjestelmä, joka havainnollistaa navigaatioverkkojen toimintaa. Järjestelmälle voidaan antaa mielivaltainen kolmiulotteinen malli navigaatioverkoksi, joten reitinetsintää voidaan tarkastella monenlaisissa tilanteissa. Järjestelmää voitaisiin myös helposti laajentaa oikeaksi peliksi, sillä se toteutetaan samoilla työkaluilla, joilla monet pelit on tehty

Dynamically Pruned A* for Re-planning in Navigation Meshes

Modern simulations feature crowds of AI-controlled agents moving through dynamic environments, with obstacles appearing or disappearing at run-time. A dynamic navigation mesh can represent the traversable space of such environments. The A* algorithm computes optimal paths through the dual graph of this mesh. When an obstacle is inserted or removed, the mesh changes and agents should re-plan their paths. Many existing re-planning algorithms are too memory-intensive for crowds, or they cannot easily be used on graphs that structurally change. In this paper, we present Dynamically Pruned A* (DPA*), an extension of A* for re-planning optimal paths in dynamic navigation meshes. DPA* has similarities to adaptive algorithms that make the A* heuristic more informed based on previous queries. However, DPA* prunes the search using only the previous path and its relation to the dynamic event. We describe this relation using four scenarios; DPA* uses different rules in each scenario. Our algorithm is memory-friendly and robust against structural changes, which makes it suitable for crowds in dynamic navigation meshes. Experiments show that DPA* performs particularly well in large environments and when the dynamic event is visible to the agent. We integrate the algorithm into crowd simulation software to model large crowds in dynamic environments in real-time

Simplex Control Methods for Robust Convergence of Small Unmanned Aircraft Flight Trajectories in the Constrained Urban Environment

Constrained optimal control problems for Small Unmanned Aircraft Systems (SUAS) have long suffered from excessive computation times caused by a combination of constraint modeling techniques, the quality of the initial path solution provided to the optimal control solver, and improperly defining the bounds on system state variables, ultimately preventing implementation into real-time, on-board systems. In this research, a new hybrid approach is examined for real-time path planning of SUAS. During autonomous flight, a SUAS is tasked to traverse from one target region to a second target region while avoiding hard constraints consisting of building structures of an urban environment. Feasible path solutions are determined through highly constrained spaces, investigating narrow corridors, visiting multiple waypoints, and minimizing incursions to keep-out regions. These issues are addressed herein with a new approach by triangulating the search space in two-dimensions, or using a tetrahedron discretization in three-dimensions to define a polygonal search corridor free of constraints while alleviating the dependency of problem specific parameters by translating the problem to barycentric coordinates. Within this connected simplex construct, trajectories are solved using direct orthogonal collocation methods while leveraging navigation mesh techniques developed for fast geometric path planning solutions. To illustrate two-dimensional flight trajectories, sample results are applied to flight through downtown Chicago at an altitude of 600 feet above ground level. The three-dimensional problem is examined for feasibility by applying the methodology to a small scale problem. Computation and objective times are reported to illustrate the design implications for real-time optimal control systems, with results showing 86% reduction in computation time over traditional methods

Navigating Through Virtual Worlds: From Single Characters to Large Crowds

With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment

Coastal path planning algorithm using quadtree and visibility graph

학위논문(박사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 김태완.With the introduction of e-Navigation technology by the International Maritime Organization, it has been recommended for ships to exchange data with maritime centers. For this purpose, the necessary information for ships is electronically exchanged for information exchange, and this information includes route information. Therefore, the vessel must make a voyage plan and transmit it to the maritime center. Maritime center must check that there is no problem in the voyage plan and send the voyage plan to the vessel. Since weather routing system is established for ships sailing the ocean, it is easy to exchange route data between ships and maritime center. However, it is difficult to exchange route data for coastal ships because the navigator decide the route manually using their professional knowledge. In order to compensate the manual route planning along with the digitalization of the route, the demand for an automatic route system in the coast has increased. Coastal ships include passenger ships, fishing boats, and yachts for various purposes. Vessels must calculate the route to their destination to achieve their objectives. The purpose of this study is to compute a safe and efficient route within a coast with a complex environment. It is necessary to secure the safety of the vessel by providing a route plan in consideration of the changing marine environment to the vessel in operation before departure or in real time. In order to determine a route, information such as shoreline, water depth, weather, and tide is required. Based on this information, many studies on route planning of ships sailing the ocean have been developed, but most of them are focused on reducing fuel consumption and sailing time in open sea. Because the coastline is complex and there are many obstacles in the coast, it is difficult to apply the existing research in coastal environment. In addition, since there are many areas with a large tide difference in Korea, the environment changes frequently during navigation. Therefore, it is necessary to calculate the route within a short time to consider frequent environment change. In this paper, we propose a novel route planning algorithm for complex offshore environments by integrating the advantages of quadtree and visibility graph. A quadtree is used to improve the inefficient environment representation method such as the uniform grid. The graph is constructed using quadtree and the shortest path is computed on the quadtree based graph. Using the waypoints of the previously computed route, a visibility graph is generated to calculate the optimal route. To find the optimal path, Dijkstra algorithm is used at this time. The proposed algorithm can perform route planning from any angle and establishes a safe route plan by maintaining a certain distance from the coastline. In addition, the fuel consumption is computed in consideration of the weather information according to the departure time of the vessel, and the route with the minimum fuel consumption is planned. To calculate the fuel consumption, the braking power must be computed. The braking power can be calculated from the effective power, which is composed of the ship's resistance and speed. The resistance of the vessel is composed of resistance in still water, additional wind resistance, and additional wave resistance, and the net speed of the vessel. At this time, the additional resistance and the speed of the ship are computed based on ISO 15016. For validation of proposed algorithm, the minimum fuel consumption route and the shortest distance route were compared and evaluated. For performance evaluation, the algorithm proposed in this study was applied to the west and south sea of Korea. It was confirmed that the topology of the minimum fuel consumption route was different from the shortest route due to the influence of the weather information. Overall, it was confirmed that the proposed algorithm can generate the optimal route in complex environment with a short time.국제해사기구(International Maritime Organization)가 e-Navigation 기술을 도입하면서 선박이 해상 센터와 데이터를 교환해야 하는 것이 표준이 되었다. 이를 위해 선박이 항해하는데 필요한 정보들이 정보 교환을 위해 전자화되고 있으며, 이러한 정보에는 항로 정보도 포함되어 있다. 따라서 선박은 항해 계획을 세운 뒤 해상 센터에 전송해야 하며, 해상 센터는 항해 계획에 문제가 없는지 확인하고 이를 다시 선박으로 보내야 한다. 대양을 항해하는 선박은 자동으로 항로를 계산하는 시스템이 구축되어 있기 때문에 항로의 데이터 교환이 용이하지만 연안을 항해하는 선박은 선장 및 항해사가 전문적 지식을 이용하여 수작업으로 항로를 결정하기 때문에 항로 데이터 교환이 어렵다. 항로의 전자화와 함께 수작업으로 항로를 계산하는 과정을 보완하기 위해 연안 내 항로를 자동으로 계산하는 시스템에 대한 요구가 증가하였으며, 이와 관련하여 활발한 연구가 진행되고 있다. 연안 선박은 여객선, 어선, 요트 등이 존재하며 다양한 목적을 갖고 있다. 선박은 각각의 목적을 달성하기 위해 목적지까지의 경로를 연안 내에서 계산해야 한다. 본 연구의 목적은 환경이 복잡한 연안 내에서 안전한 항로를 계산하는 것이다. 변화하는 해양환경을 고려한 경로 계획을 출발 전 혹은 실시간으로 운항 중인 선박에게 제공하여 선박의 안전을 확보해야 한다. 항로를 계획하기 위해 해안선, 수심, 기상, 조위 등의 정보가 필요하다. 이러한 정보를 기반으로 대양을 항해하는 선박의 항로 계획 연구가 많이 개발되었으나, 대부분의 연구는 연료 소모량과 항해 시간을 줄이는데 초점을 맞추고 있다. 연안에는 해안선이 복잡하고 장애물이 많기 때문에 기존의 연구를 그대로 적용하기 어렵다. 또한, 한국의 경우에는 조위 차가 큰 영역이 많기 때문에 항해 시 주변 환경이 자주 변하므로 빠른 시간 내에 항로를 계산해야 하는데 기존 연구는 이에 적합하지 않다. 본 논문에서는 쿼드 트리와 가시성 그래프의 장점을 통합하여 연안 내 복잡한 해양 환경을 위한 새로운 경로 계획 알고리즘을 제안한다. 기존의 균일 격자와 같은 비효율적인 환경 표현 방법을 개선하기 위해 쿼드 트리를 사용하였다. 쿼드 트리 위에서 그래프를 구성하고 쿼드 트리의 최단 경로를 계산한다. 앞서 계산한 항로의 웨이포인트를 기반으로 가시성 그래프를 생성하여 최적 경로를 계산하며, 이 때 Dijkstra 알고리즘을 사용한다. 제안한 알고리즘은 모든 각도에서 항로 계획을 수행할 수 있으며, 해안선으로부터 일정 간격을 유지하여 안전한 항로 계획을 수립한다. 또한, 선박의 출항 시간에 따른 기상 정보를 고려하여 연료 소모량을 계산하였으며, 연료 소모량이 최소가 되는 항로를 계획한다. 연료 소모량을 계산하기 위해 제동 동력을 계산해야 하며, 제동 동력은 선박의 저항과 속도의 곱으로 이루어진 유효 동력으로부터 계산할 수 있다. 선박의 저항은 정수 중 저항, 바람 부가저항, 파랑 부가저항으로 구성되며, 선박의 속력은 조류를 반영하여 이루어진다. 이 때 부가저항과 선박의 속력은 ISO 15016을 기반으로 하여 계산된다. 이러한 과정을 수행하여 계산한 최소 연료 소모량 항로와 최단 거리 항로를 비교 및 평가하였다. 성능 평가를 위해 본 연구에서 제안한 알고리즘을 대한민국 서해안과 남해안 영역에 대해 적용하였다. 기상 정보의 영향으로 최소 연료 소모량 항로가 최단 거리 항로와 형상이 다른 것을 확인했다. 전반적으로 제안한 알고리즘이 기존 연구 방법에 비해 빠른 시간 안에 최적 항로를 생성할 수 있음을 확인하였다.1 Introduction 1 1.1 Background 1 1.2 Objective and method 3 1.3 Contributions of this paper 8 1.4 Outline 9 2 Theoretical background 10 2.1 Environment modelling 14 2.1.1 Regular grids 14 2.1.2 Irregular grids 16 2.1.3 Navigation mesh 17 2.1.4 Voronoi diagram 19 2.1.5 Visibility graph 20 2.1.6 Comparison of environment modeling 21 2.2 Path planning methods 24 2.2.1 Graph based methods 24 2.2.2 Sampling based methods 26 2.2.3 Evolutionary methods 29 2.2.4 Comparison of path planning methods 32 3 Related work 33 3.1 Genetic algorithm with navigation mesh 33 3.2 Dijkstra algorithm with Voronoi diagram 34 3.3 A* algorithm with quadtree 35 3.4 Comparison of related works with this thesis 38 4 Data acquisition and preprocess 43 4.1 Topography data 43 4.1.1 Topography and depth 43 4.1.2 Coastline expansion algorithm 46 4.2 Weather data 51 4.2.1 Wave data (KMA) 52 4.2.2 Wind data (KMA) 54 4.2.3 Current data (KMA) 56 4.2.4 Tide data (KHOA) 58 5 Quadtree based graph 60 5.1 Quadtrees 60 5.2 PMR quadtree insertion and split algorithm 67 5.3 Graph construction using PMR quadtree 76 5.4 The shortest path on quadtree based graph 85 6 Visibility graph construction with quadtree 87 6.1 Visibility graph 87 6.2 Visibility graph with quadtree 90 6.3 Improved line of sight algorithm 91 7 Fuel oil consumption estimation 94 7.1 Ship resistance 94 7.2 Ship speed 100 7.3 Fuel oil consumption 101 7.4 Example of fuel oil consumption computation 103 8 Coastal path planning algorithm 107 8.1 Problem definition 107 8.2 Assumptions for route optimization 109 8.3 Procedure of coastal path planning algorithm 109 9 Experimental results 113 9.1 Simulation results in quadtree generation 113 9.2 Simulation results with different draft 115 9.3 Simulation results in south sea of Korea 119 9.4 Simulation results in west sea of Korea 134 9.5 Fuel oil consumption minimization path results 148 9.5.1 First case in west sea of Korea 148 9.5.2 Second case in west sea of Korea 156 9.5.3 Third case in south sea of Korea 161 9.5.4 Second case in south sea of Korea 169 9.6 Long voyage path results 174 10 Conclusion and future works 179 References 181박

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

Generating gameplay scenarios through faction conflict modeling.

A gameplay scenario can be defined as a series of events that emerge from a given context. These events can potentially be influenced by the player through gameplay, which results in meaningful interaction with the simulation. Creating gameplay scenarios in computer games and training simulators is an immensely expensive and time consuming undertaking. A common trait for most scenarios created is that they tend to be static. Once a player has completed the scenario once, he knows exactly how it will behave the next time, reducing or removing the replay value of the gameplay scenario. This thesis investigates how artificial intelligence techniques can be used to define virtual worlds and interaction between entities, such as virtual humans, to dynamically generate gameplay scenarios by simulating the conflict between entities as they clash over conflicting interests in the world. The first part of this thesis introduces the vast field of artificial intelligence, how it is usually applied in games, and how new concepts are slowly trickling into the field of game artificial intelligence. Topics introduced include crowd simulation techniques, agent simulation and how one can describe arbitrary virtual worlds through the use of semantics, smart objects and fuzzy logic. The second part describes the practicalities of the implementation. Here, the game engine used to develop the prototype game world is presented and compared to other alternatives. Next, the design and implementation details of the proof of concept implementation, called the “Faction Interaction Framework”, are described in detail. The design allows for quickly defining the important resources, actions, and potential interactions between entities in a virtual world. Finally, the implementation can be run as an add-on to a virtual world, which can be used to drive scenario generation through conflict simulation. The work presented in this thesis provides a proof of concept solution for dynamically generating gameplay scenarios. By providing game developers with a pattern for defining the elements of their virtual world that is the source of conflict, the “Faction interaction framework” provides an approach to have the virtual world autonomously generate myriads of gameplay scenarios depending on user input. This has potential application especially to large, open world games, massively multiplayer online games and training simulators, where the generation of novel gameplay scenarios is challenging due to the large amount required

Navigation Queries from Triangular Meshes

Abstract. Navigation meshes are commonly employed as a practical represen-tation for path planning and other navigation queries in animated virtual envi-ronments and computer games. This paper explores the use of triangulations as a navigation mesh, and discusses several useful triangulation–based algorithms and operations: environment modeling and validity, automatic agent placement, track-ing moving obstacles, ray–obstacle intersection queries, path planning with arbi-trary clearance, determination of corridors, etc. While several of the addressed queries and operations can be applied to generic triangular meshes, the efficient computation of paths with arbitrary clearance requires a new type of triangular mesh, called a Local Clearance Triangulation, which enables the efficient and correct determination if a disc of arbitrary size can pass through any narrow pas-sages of the mesh. This paper shows that triangular meshes can support the effi-cient computation of several navigation procedures and an implementation of the presented methods is available