An autonomous and guided crowd in panic situations

This paper describes a model for simulating crowds in real time. We deal with the hierarchy of the crowd, groups and individuals. The groups are the most complex structure that can be controlled in different degrees of autonomy. The autonomy means that the virtual agents are independent of the user intervention. Depending on the complexity of the simulation, some simple behaviors can be sufficient to simulate crowds. Otherwise, more complicated behaviors rules can be necessary in order to improve the realism of the animation. We present two different ways for controlling crowd behaviors: - by defining behavior rules, to give intelligence to the agent. By providing an external control to guide crowd behaviors, this control is done by the user or by an autonomous agent called the guide. The main contribution of our approach is to combine these two ways of behaviors (autonomous, guide) in order to simulate the evacuation of a crowd in emergency situations. Many strategies of evacuation have been implemented and we will demonstrate that in most situations, the guided method decrease the average escape time and increase the chance of survival in emergency situations.Facultad de Informátic

Path Finding and Collision Avoidance in Crowd Simulation

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

Decentralized Approach to Evolve the Structure of Metamorphic Robots

International audienceMetamorphic robots are robots that can change their shape by reorganizing the connectivity of their modules to adapt to new environments, perform new tasks, or recover from damages. In this paper we present a decentralized method for structural evolving of a class of lattice-based simulated metamorphic robots in a static environment. These robots are considered as a set of crystalline (compressible) modules that are able to connect or disconnect one from each another or even exchange information and energy with the neighbor modules in order to form various structures/patterns dynamically. Our approach is splitted in two layers: in the first layer a genetic algorithm is used to generate a number of well suited target configurations based on current information perceived from environment, while in the second layer a PacMan-like algorithm is used to make a plan for modules movement to transform the robot from its current pattern to the target pattern emerged in first layer

Modeling a bacterial ecosystem through chemotaxis simulation of a single cell

International audienceWe present in this paper an artificial life ecosystem in which bacteria are evolved to perform chemotaxis. In this system, surviving bacteria have to overcome the problems of detecting resources (or sensing the environment), modulating their motion to generate a foraging behavior, and communicating with their kin to produce more sophisticated behaviors. A cell’s chemotactic pathway is modulated by a hybrid approach that uses an algebraic model for the receptor clusters activity, an ordinary differential equation for the adaptation dynamics, and a metabolic model that converts nutrients into biomass. The results show some analysis of the motion obtained from some bacteria and their effects on the evolved population behavior. The evolutionary process improves the bacteria’s ability to react to their environment, enhancing their growth and allowing them to better survive. As future work, we propose to investigate the effect of emergent bacterial communication as new species arise, and to explore the dynamics of colonies

Modelisation en synthese d'images: utilisation d'une methodologie declarative

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : TD 81570 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Damage Recovery for Simulated Modular Robots Through Joint Evolution of Morphologies and Controllers

In order to be fully autonomous, robots have to be resilient so that they can recover from damages and operate for a long period of time with no human assistance. To be resilient, existing approaches propose to change the robots’ behavior using a different control system when a hardware fault or damage occurs. These approaches are used for robots which have fixed morphologies. However, we cannot assume which morphology would be optimal for a given problem and which morphology allows resilience. In the present paper, we introduce a new approach that generates resilient artificial modular robots by evolving the robot morphology along with its controller. We used a multi-objective evolutionary algorithm to optimize two objectives at a time, which are the traveled distance of a damage-free robot and the traveled distance of the same robot with damaged parts. The result of preliminary experiments demonstrates that during evaluation, when robots are deliberately faced to motor failures, the evolution process can optimize and generate new morphologies for which the robot’s behavior is less affected by damage. This makes the robot capable to recover its ability to move forward

UNE METHODE D’INTEGRATION EFFICACE POUR LA SIMULATION DE TISSUS PAR UN MODELE PHYSIQUE EFFICIENT

La simulation de tissu était basée dans la majorité des travaux sur les systèmes masse-ressorts. Malheureusement, ces systèmes sont incapables de modéliser avec précision l’élasticité d’une surface, et ils restent particulièrement imprécis pour des modèles anisotropes et non linéaires comme le tissu. Actuellement, les modèles basés sur la méthode des éléments finis du premier ordre sont utilisés dans ce genre de simulation, ils fonctionnent sur des maillages de triangles arbitraires, pas forcément réguliers. Afin de contribuer à la résolution du problème de la divergence des simulations nous avons proposé une exploitation efficace de la méthode d’intégration Euler implicite, cette dernière nous a permis d’une part d’assurer la convergence du modèle et d’autre part d’éviter les calculs additionnels tels que l’utilisation du Jacobien ou la résolution d’un grand système linéaire