2,298 research outputs found

    Path Finding and Collision Avoidance in Crowd Simulation

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    Motion planning for multiple entities or a crowd is a challenging problem in today’s virtual environments. We describe in this paper a system designed to simulate pedestrian behaviour in crowds in real time, concentrating particularity on collision avoidance. On-line planning is also referred as the navigation problem. Additional difficulties in approaching navigation problem are that some environments are dynamic. In our model we adopted a popular methodology in computer games, namely A* algorithm. The idea behind A* is to look for the shortest possible routes to the destination not through exploring exhaustively all the possible combination but utilizing all the possible directions at any given point. The environment is formed in regions and the algorithm is used to find a path only in visual region. In order to deal with collision avoidance, priority rules are given to some entities as well as some social behaviour

    Hybrid long-range collision avoidance for crowd simulation

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    Biomechanical Locomotion Heterogeneity in Synthetic Crowds

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    Synthetic crowd simulation combines rule sets at different conceptual layers to represent the dynamic nature of crowds while adhering to basic principles of human steering, such as collision avoidance and goal completion. In this dissertation, I explore synthetic crowd simulation at the steering layer using a critical approach to define the central theme of the work, the impact of model representation and agent diversity in crowds. At the steering layer, simulated agents make regular decisions, or actions, related to steering which are often responsible for the emergent behaviours found in the macro-scale crowd. Because of this bottom-up impact of a steering model's defining rule-set, I postulate that biomechanics and diverse biomechanics may alter the outcomes of dynamic synthetic-crowds-based outcomes. This would mean that an assumption of normativity and/or homogeneity among simulated agents and their mobility would provide an inaccurate representation of a scenario. If these results are then used to make real world decisions, say via policy or design, then those populations not represented in the simulated scenario may experience a lack of representation in the actualization of those decisions. A focused literature review shows that applications of both biomechanics and diverse locomotion representation at this layer of modelling are very narrow and often not present. I respond to the narrowness of this representation by addressing both biomechanics and heterogeneity separately. To address the question of performance and importance of locomotion biomechanics in crowd simulation, I use a large scale comparative approach. The industry standard synthetic crowd models are tested under a battery of benchmarks derived from prior work in comparative analysis of synthetic crowds as well as new scenarios derived from built environments. To address the question of the importance of heterogeneity in locomotion biomechanics, I define tiers of impact in the multi-agent crowds model at the steering layer--from the action space, to the agent space, to the crowds space. To this end, additional models and layers are developed to address the modelling and application of heterogeneous locomotion biomechanics in synthetic crowds. The results of both studies form a research arc which shows that the biomechanics in steering models provides important fidelity in several applications and that heterogeneity in the model of locomotion biomechanics directly impacts both qualitative and quantitative synthetic crowds outcomes. As well, systems, approaches, and pitfalls regarding the analysis of steering model and human mobility diversity are described

    Multi-Domain Real-Time Planning in Dynamic Environments

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    This paper presents a real-time planning framework for multicharacter navigation that enables the use of multiple heterogeneous problem domains of differing complexities for navigation in large, complex, dynamic virtual environments. The original navigation problem is decomposed into a set of smaller problems that are distributed across planning tasks working in these different domains. An anytime dynamic planner is used to efficiently compute and repair plans for each of these tasks, while using plans in one domain to focus and accelerate searches in more complex domains. We demonstrate the benefits of our framework by solving many challenging multi-agent scenarios in complex dynamic environments requiring space-time precision and explicit coordination between interacting agents, by accounting for dynamic information at all stages of the decision-making process

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

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    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

    Computational Methods for Cognitive and Cooperative Robotics

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    In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese. Diese Dissertation prĂ€sentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung fĂŒr Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden fĂŒr die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu ĂŒbertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese fĂŒr Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von rĂ€umlichen und rĂ€umlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil prĂ€sentiert neue mathematische Werkzeuge fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

    Synthesizing Human Motion From Intuitive Constraints

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    Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. Motion graphs and their variations have proven to be a powerful tool for synthesizing human motion when only a rough sketch is given. Motion graphs are simple to implement, and the synthesis can be fully automatic. When unrolled into the environment, motion graphs, however, grow drastically in size. The major challenge is then searching these large graphs for motions that satisfy user constraints. A number of sub-optimal algorithms that do not provide guarantees on the optimality of the solution have been proposed. In this paper, we argue that in many situations to get natural results an optimal or nearly-optimal search is required. We show how to use the well-known A* search to find solutions that are optimal or of bounded sub-optimality. We achieve this goal for large motion graphs by performing a lossless compression of the motion graph and implementing a heuristic function that significantly accelerates the search for the domain of human motion. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion. These experiments show that motions become more natural as the optimality improves

    Global search for occlusion minimisation in virtual camera control

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    This paper presents a fast and reliable global-search approach to the problem of virtual camera positioning when multiple objects that need to be within the reach of the camera are fully occluded. For this purpose, a comparative analysis of global-search algorithms is presented for the problem of maximising camera visibility across different tasks of varying complexity and within different real-time windows. A custom-designed genetic algorithm is compared to octree-based search and random search and results showcase the advantages of the genetic algorithm proposed with respect to efficiency, robustness and computational effort.peer-reviewe
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