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Crowd and Multi-agent Simulation
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Virtualized TrafficJur van den Berg, Jason Sewall, Ming C. Lin, and Dinesh Manocha We present the concept of virtualized traffic to reconstruct and visualize continuous traffic flows from discrete spatial and temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Our approach can reconstruct the traffic flows in between the two locations along the highway for immersive visualization of virtual cities or other environments. Virtualized traffic is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. |
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Aggregate Dynamics for Dense Crowd SimulationRahul Narain, Abhinav Golas, Sean Curtis, and Ming C. Lin We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint called unilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. |
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Highly Parallel Collision Avoidance for Multi-agent SimulationStephen J. Guy, Jur van den Berg, Ming C. Lin, and Dinesh Manocha We present the ClearPath collision avoidance algorithm between multiple agents for real-time simulations. ClearPath extends the velocity obstacle (VO) from robotics and formulates the conditions for collision-free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent and parallelize the algorithm by exploiting data and thread-level parallelism. ClearPath can robustly handle dense scenarios with hundreds of thousands of heterogeneous agents in a few milliseconds. |
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Directing Crowd Simulations Using Navigation FieldsSachin Patil, Jur van den Berg, Sean Curtis, Ming C. Lin, and Dinesh Manocha We present an approach based on navigation fields to direct and control virtual crowds. Our method directs agents towards their desired goals based on guidance trajectories. The system allows the user to sketch the paths directly in the scene or import motion fields extracted from video footage. The input guidance fields are blended together to form a goal-directed navigation field to direct virtual crowds. |
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Composite AgentsHengchin (Yero) Yeh, Sean Curtis, Sachin Patil, Jur van den Berg, Dinesh Manocha, and Ming C. Lin We introduce composite agents to effectively model complex agent interactions for agent-based crowd simulation. Each composite agent consists of a basic agent that is associated with one or more proxy agents. The composite agent formulation allows an agent to exercise influence over other agents greater than that implied by its physical properties. Composite agents can be added to most agent-based simulation systems and used to model emergent behaviors amongst individuals. |
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Interactive Navigation of Individual Agents in Crowded EnvironmentsJur van den Berg, Sachin Patil, Jason Sewall, Dinesh Manocha, and Ming C. Lin We present an approach for interactive navigation and planning of multiple agents in crowded scenes with moving obstacles. Our formulation uses a pre-computed roadmap that provides macroscopic, global connectivity for way-finding and combines it with fast and localized navigation for each agent. At runtime, each agent senses the environment independently and computes a collision-free path based on an extended velocity obstacle (VO) and smoothness constraints. Furthermore, our algorithm ensures that each agent exhibits no oscillatory behaviors or gets trapped at a local minimum in crowded environments. |
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Navigating Virtual Agents in Online Virtual WorldsRussell Gayle and Dinesh Manocha We present an approach for navigating autonomous virtual agents in online virtual worlds that are based on a centralized server network topology. The motion of each virtual agent is controlled through local and global navigation. Our local navigation model is based on artificial social forces that has been extended to account for inaccurate sensing from network latency. Global navigation for each virtual agent is based on cell decomposition and computes high level paths. The overall computation is balanced by performing local navigation on client machines and global navigation on the server. |
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Real-time Navigation of Independent Agents Using Adaptive RoadmapsAvneesh Sud, Russell Gayle, Erik Andersen, Stephen J. Guy, Ming C. Lin, and Dinesh Manocha We present an algorithm for navigating a large number of independent agents in complex and dynamic environments. We compute adaptive roadmaps to perform global path planning for each agent simultaneously. We take into account dynamic obstacles and inter-agent interaction forces to continuously update the roadmap by using a physically-based agent dynamics simulator. We also introduce link bands for resolving collisions among multiple agents. |
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Real-time Path Planning for Virtual Agents in Dynamic EnvironmentsAvneesh Sud, Erik Andersen, Sean Curtis, Ming C. Lin, and Dinesh Manocha We present an approach for real-time path planning of multiple virtual agents in complex dynamic scenes. We introduce the multi-agent navigation graph (MaNG), which is constructed from the first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. |
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