Probabilistic Roadmaps for Virtual Camera Pathing with Cinematographic Principles

As technology use increases in the world and inundates everyday life, the visual aspect of technology or computer graphics becomes increasingly important. This thesis presents a system for the automatic generation of virtual camera paths for fly-throughs of a digital scene. The sample scene used in this work is an underwater setting featuring a shipwreck model with other virtual underwater elements such as rocks, bubbles and caustics. The digital shipwreck model was reconstructed from an actual World War II shipwreck, resting off the coast of Malta. Video and sonar scans from an autonomous underwater vehicle were used in a photogrammetry pipeline to create the model. This thesis presents an algorithm to automatically generate virtual camera paths using a robotics motion planning algorithm, specifically the probabilistic roadmap. This algorithm uses a rapidly-exploring random tree to quickly cover a space and generate small maps with good coverage. For this work, the camera pitch and height along a specified path were automatically generated using cinematographic and geometric principles. These principles were used to evaluate potential viewpoints and influence whether or not a view is used in the final path. A computational evaluation of ‘the rule of thirds’ and evaluation of the model normals relative to the camera viewpoint are used to represent cinematography and geometry principles. In addition to the system that automatically generates virtual camera paths, a user study is presented which evaluates ten different videos produced via camera paths with this system. The videos were created using different viewpoint evaluation methods and different path generation characteristics. The user study indicates that users prefer paths generated by our system over flat and randomly generated paths. Specifically, users prefer paths generated using the computational evaluation of the rule of thirds and paths that show the wreck from a large variety of angles but without too much camera undulation

Real-Time Support of Haptic Interaction by Means of Sampling-Based Path Planning

Haptic feedback enables the support of a human during the interaction with an environment. A variety of concepts have been developed to achieve an effective haptic support of the user in specific scenarios, e.g. Virtual Fixtures. However, most of these methods do not enable an adaptive support of the motion from a user within a (real or virtual) environment, which would be desirable in many situations. Especially when dynamical obstacles are involved or when the desired motion of the human is not known beforehand, an online computation of this support is essential, which should be based on a fast and effective determination of feasible motions.In contrast to other methods, sampling-based path planning is applicable to arbitrary interaction scenarios and enables to find a solution if it exists at all. Thus, it seems to be ideally suited for a generic framework that is able to deal with various kinematics, as e.g. a virtual prototyping test bed for the haptic evaluation of mechanisms requires. At such a test bed, the path planner could directly be coupled to the haptic rendering of a virtual scene to assist a user in approaching a target.This motivated the development of SamPP, a sampling-based path planning library with implementations of the most important algorithms. It can be used for nearly arbitrary rigid robots and environments. By performing numerous benchmarks, we prove the effectiveness and efficiency of SamPP. It is shown that a single-threaded version of the path planning can be used for real-time support of the haptic interaction at a novel actuated car door.Furthermore, we enhance the path planning performance for unknown or dynamical environments significantly by the OR-Parallelization of different path planning queries. This Generalized OR-Parallelization is a novel concept that to the best knowledge of the authors has not been proposed beforehand. We show that for the case of dynamic environments the likelihood of a fast path planning result is higher with our approach. Thus, even in dynamic or unknown environments, a real-time support of haptic interaction can be achieved. Finally, we highlight four promising research directions to exploit the concept of Generalized OR-Parallelization: 1) Combination of PRMs and RRTs to achieve a synergy of the advantages of both concepts, 2) concurrent use of different parameter sets of path planning algorithms, 3) online adaptation of these parameter sets and 4) online adaptation of the types and numbers of parallel executed path planning programs

Practical motion planning for aerial-like virtual agents in Meta!Blast: A full and complex three dimensional virtual environment

Motion planning, or enabling agents to navigate around a virtual environment autonomously, is an essential requirement for video games and simulations. A well implemented motion planning technique can create a realistic and immersive user experience. If motion planning is not implemented properly, agents will exhibit unrealistic behavior and cause a distraction for the user. Motion planning is often difficult to implement due to the agents\u27 movement capabilities and the complexity of the virtual environment in which the agents exist. In a traditional three dimensional video game in which the agents are bound by gravity, the agents\u27 motion takes place mostly in the XZ-plane. In other words, the agents\u27 degree of freedom (DOF) is three. In this case, motion planning is translated into a two-dimensional problem, which is relatively easier to compute. However, when the agents can move in any three dimensional direction or to any three dimensional position in space, motion planning is much more complex. Meta!Blast is a three dimensional educational video game. Implementing motion planning in Meta!Blast is challenging for three reasons: The first reason is the agents have at least six degrees of freedom and can be translated or rotated about any axis in the three dimensional virtual environment. The second reason is the agents exist in a dense environment with many irregularly shaped models that need to be considered during planning. Lastly, Meta!Blast will be deployed in the high school classroom where computer hardware resources are limited, eliminating some planning techniques found in the literature. This thesis provides a practical solution for high DOF agents in dense environments using a combination of octree space partitioning, A* path-planning, and steering behaviors

Hierarchical Path Finding to Speed Up Crowd Simulation

Path finding is a common problem in computer games. Most videogames require to simulate thousands or millions of agents who interact and navigate in a 3D world showing capabilities such as chasing, seeking or intercepting other agents. A new hierarchical path finding solution is proposed for large environments. Thus, a navigation mesh as abstract data structure is used in order to divide the 3D world. Then, a hierarchy of graphs is built to perform faster path finding calculations than a common A*. The benefits of this new approach are demonstrated on large world models

Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés