On Weighted Regions and Social Crowds: Autonomous-agent Navigation in Virtual Worlds

Virtual environments have gained in importance in many aspects of the world we live in today. Immersive virtual worlds are ubiquitous in modern movies, video games, and online communities. They are also important in non-entertainment applications such as training and education software, simulations of mass events, evacuation scenarios, human factor analysis, and urban city planning. Training and education software itself comprises various application areas, ranging from teaching children with the help of virtual characters, to training policemen and firefighters, or training soldiers in virtual environments for military operations. Other applications are online mapping services such as Open Street Map, Google Street View, or Mapillary. A key aspect of creating an immersive virtual world is the development of algorithms that handle the navigation of its virtual inhabitants. This involves the creation of believable paths that are smooth, do not contain unnecessary detours, keep clearance from obstacles, respect terrain and region information, and avoid collisions with other moving entities. Furthermore, it involves the coordination of large virtual crowds in both sparse and dense situations, and the generation of social behavior among virtual groups. State-of-the-art algorithms and crowd-simulation models struggle with such tasks, and consequently, the range of possible character behaviors is still limited up to the present day. This thesis focuses on three computational tasks, with which state-of-the-art algorithms still struggle: Region-based path planning, region-based path following, and coordinating dense virtual crowds and social groups. We show why these tasks are difficult to solve with existing algorithms when using grids or graph-based representations of the traversable space in a virtual environment. Furthermore, we show that these tasks can be solved efficiently on a surface-based representation when using novel methods. These novel methods on region-based planning and coordinating crowds and social groups are presented in detail, and they form the main contributions of this thesis

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

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