In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace In our formulation, each robot acts fully independently, and does not communicate with other robots Based on the definition of velocity obstacles [5], we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few milliseconds To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace

/pdf/reciprocal-n-body-collision-avoidance-3g7xt37qbz.pdf

Reciprocal n-Body Collision Avoidance

In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.

/pdf/reciprocal-velocity-obstacles-for-real-time-multi-agent-2cul7oillg.pdf

Reciprocal Velocity Obstacles for real-time multi-agent navigation

Agent-based Models of Geographical Systems

https://hal.archives-ouvertes.fr/hal-01120560/document

Learning cultural heritage by serious games

Building upon state-of-the-art algorithms for pedestrian detection and multi-object tracking, and inspired by sociological models of human collective behavior, we automatically detect small groups of individuals who are traveling together. These groups are discovered by bottom-up hierarchical clustering using a generalized, symmetric Hausdorff distance defined with respect to pairwise proximity and velocity. We validate our results quantitatively and qualitatively on videos of real-world pedestrian scenes. Where human-coded ground truth is available, we find substantial statistical agreement between our results and the human-perceived small group structure of the crowd. Results from our automated crowd analysis also reveal interesting patterns governing the shape of pedestrian groups. These discoveries complement current research in crowd dynamics, and may provide insights to improve evacuation planning and real-time situation awareness during public disturbances.

http://www.cse.psu.edu/~rtc12/Papers/GeCollinsRubackPAMI2011.pdf

Vision-Based Analysis of Small Groups in Pedestrian Crowds

This paper introduces a framework for real-time simulation and rendering of crowds navigating in a virtual environment The solution first consists in a specific environment preprocessing technique giving rise to navigation graphs, which are then used by the navigation and simulation tasks Second, navigation planning interactively provides various solutions to the user queries, allowing to spread a crowd by individualizing trajectories A scalable simulation model enables the management of large crowds, while saving computation time for rendering tasks Pedestrian graphical models are divided into three rendering fidelities ranging from billboards to dynamic meshes, allowing close-up views of detailed digital actors with a large variety of locomotion animations Examples illustrate our method in several environments with crowds of up to 35 000 pedestrians with real-time performance Copyright © 2006 John Wiley & Sons, Ltd

Real-time navigating crowds: scalable simulation and rendering: Research Articles

Pompeii was a Roman city, destroyed and completely buried during an eruption of the volcano Mount Vesuvius. We have revived its past by creating a 3D model of its previous appearance and populated it with crowds of Virtual Romans. In this paper, we detail the process, based on archaeological data, to simulate ancient Pompeii life in real time. In a first step, an annotated city model is generated using procedural modelling. These annotations contain semantic data, such as land usage, building age, and window/door labels. In a second phase, the semantics are automatically interpreted to populate the scene and trigger special behaviors in the crowd, depending on the location of the characters. Finally, we describe the system pipeline, which allows for the simulation of thousands of Virtual Romans in real time.

Populating ancient pompeii with crowds of virtual romans

Real-time crowd motion planning requires fast, realistic methods for path planning as well as obstacle avoidance. In a previous work (Morini et al. in Cyberworlds International Conference, pp. 144–151, 2007), we introduced a hybrid architecture to handle real-time motion planning of thousands of pedestrians. In this article, we present an extended version of our architecture, introducing two new features: an improved short-term collision avoidance algorithm, and simple efficient group behavior for crowds. Our approach allows the use of several motion planning algorithms of different precision for regions of varied interest. Pedestrian motion continuity is ensured when switching between such algorithms. To assess our architecture, several performance tests have been conducted, as well as a subjective test demonstrating the impact of using groups. Our results show that the architecture can plan motion in real time for several thousands of characters.

/pdf/real-time-crowd-motion-planning-scalable-avoidance-and-group-5cabbsxp5w.pdf

Real-time crowd motion planning: Scalable Avoidance and Group Behavior

In this paper, we show our improvements in crowd behavior, rendering, and animation : based on our navigation graph [1], we are able to add, in real time, semantic information to the different zones of the virtual world. With an intuitive GUI, the user can steer the virtual humans through the environment with high-level instructions. The rendering of virtual humans is improved in 2 ways : firstly, the number of possible joints for the virtual humans is increased by compressing their matrices before sending them to the graphics card, and secondly, static meshes are used instead of billboards for distant people. We also present the way we generate and use many different walk and run animations in order to vary the crowd’s movements, based on the work of Glardon et. al. [2, 3]. Our results are illustrated with an application where a crowd of more than 5000 virtual humans are steered in an urban environment.

Steering a Virtual Crowd Based on a Semantically Augmented Navigation Graph

Real-time crowd simulations are realistic only if each human instance looks unique. A proposed solution varies the shape of human instances by attaching accessories. It also modifies the instances' appearance with a generic technique based on segmentation maps that can generate detailed color variety and patterns.

Unique Character Instances for Crowds

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