Scalable nonlinear dynamical systems for agent steering and crowd simulation

Abstract

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments. r

The user experience of crowds -- A human factors challenge

“…Considerable attention has been devoted to the development of mathematical computer models, to replicate crowd flows and dynamics (Goldenstein et al, 2001;Musse & Thalmann, 2001;Zhen et al, 2008;Smith et al, 2009). In 2008, Zhen et al utilized a new model to describe processes associated with crowd gatherings during disasters.…”

A hybrid dynamical systems approach to intelligent low-level navigation

“…We base our animations primarily on the agent steering method presented in [15]. Below, we review the tools we employed to create our animations and discuss issues particular to implementing an animation system in CHARON.…”

“…For individual entities, logicist, artificial intelligence-based techniques have been successfully used for cognitively empowered agents [21] and animated actors [14]; perception and dynamicsbased techniques [10,24,28] are often more readily able to adapt to dynamic environments. Our particular approach to low-level agent navigation is based on the method in [15,16], a scalable, adaptive approach to modeling multiple autonomous agents in dynamic virtual environments.…”

“…Like treatments of similar issues in the field of behavioral robotics [20,22], we consider only two-dimensional motion, although the mathematical foundations for threedimensional navigation already exist [15].…”

“…Our system augments the agent steering and crowd simulation approach of [15,16], retaining its reactive, scalable nature while expanding its behavioral intelligence. Our extensions include two that are highlighted in the above motivating example: selective repeller response, which allows the actor to distinguish among various actors and obstacles around it; and navigation mode switching, which allows the actor to autonomously alter its general navigation behavior in response to its dynamically changing comfort.…”