Flocks, Herds, and Schools: A Distributed Behavioral Model (original) (raw)

Constrained animation of flocks

Proceedings of the 2003 ACM …, 2003

Group behaviors are widely used in animation, yet it is difficult to impose hard constraints on their behavior. We describe a new technique for the generation of constrained group animations that improves on existing approaches in two ways: the agents in our simulations meet exact constraints at specific times, and our simulations retain the global properties present in unconstrained motion. Users can position constraints on agents' positions at any time in the animation, or constrain the entire group to meet center of mass or shape constraints. Animations are generated in a two stage process. The first step finds an initial set of trajectories that exactly meet the constraints, but which may violate the behavior rules. The second stage samples new animations that maintain the constraints while improving the motion with respect to the underlying behavioral model. We present a range of animations created with our system.

Motion swarms

Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06, 2006

We create interactive art that can be enjoyed by groups such as audiences at public events with the intent to encourage communication with those around us as we play with the art. Video systems are an attractive mechanism to provide interaction with artwork. However, public spaces are complex environments for video analysis systems. Interaction becomes even more difficult when the art is viewed by large groups of people. We describe a video system for interaction with art in public spaces and with large audiences using a model-free, appearance-based approach. Our system extracts parameters that describe the field of motion seen by a camera, and then imposes structure on the scene by introducing a swarm of particles that moves in reaction to the motion field. Constraints placed on the particle movement impose further structure on the motion field. The artistic display reacts to the particles in a manner that is interesting and predictable for participants. We demonstrate our video interaction system with a series of interactive art installations tested with the assistance of a volunteer audience.

Autonomous multi-agents in flexible flock formation

2010

In this paper, we present a flocking model where agents are equipped with navigational and obstacle avoidance capabilities that conform to user defined paths and formation shape requirements. In particular, we adopt an agent-based paradigm to achieve flexible formation handling at both the individual and flock level. The proposed model is studied under three different scenarios where flexible flock formations are produced automatically via algorithmic means to: 1) navigate around dynamically emerging obstacles, 2) navigate through narrow space and 3) navigate along path with sharp curvatures, hence minimizing the manual effort of human animators. Simulation results showed that the proposed model leads to highly realistic, flexible and real-time reactive flock formations.

Simulating the movement of the crowd in an environment using flocking

2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, 2011

One aspect of the animation is how to model something similar to the original in the real world. Crowd behavior in accordance with the original in the real world can be shown to improve the quality of the animation itself has become a major trend in the world of cinema and gaming. In the present paper [1]. In the present paper in applying a multi robot system, flocking, swarming and obstacle avoidance. The results of the paper states that the algorithm used to ensure no collisions will occur among robots or with obstacles that exist. In Research [2], using the method for path planning (potential field)-based grid. this method can be used to simulate the movement of the human crowd simulation. Research results showed that in a variety of different populations contained in the mall made it out to the main door with no one left inside room. But the time it takes to be longer if the population is grows larger. And the time to arrive at the destination also depends on the initial velocity. With reference to previous studies of excess flocking algorithm, this paper will apply the algorithm to simulate the movement of crowds flocking people out onto the main targets with the aim to analyze the speed and level of time spent in the movement of the crowd in an environment. Simulation is applied using the python programming language and based on two dimensions.

Universal Patterns of Collective Motion from Minimal Models of Flocking

2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2008

This paper is concerned with the basic laws describing the essential aspects of collective motion being one of the most common and spectacular manifestation of coordinated actions. Our purpose is to discuss models that are both simple and realistic enough to reproduce the observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many organisms as well as such non-living objects as interacting robots. Understanding the interrelation of these systems has the potential of improving the interpretation of collective behavioral patterns in both living and nonliving systems by learning about similar phenomena in the two domains of nature.

Illustration of Centralized Command and Control for Flocking Behavior

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘’Boids’’- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids’ properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles’ avoiding rule was utilized to ensure that the flock didn’t collide with obstacles in each of the boids’ paths. Keywords: 2D/3D flocking, Boids Properties, Constraints, flocking, Quad Rotors And Path Planning, Robotic Vehicles, Rules Of flocking, flight, Swarm Robots

The computational beauty of flocking: boids revisited

Mathematical and Computer Modelling of Dynamical Systems, 2007

Artificial-life research was founded in the mid-1980s. It promotes the idea of the bottom-up research approach, where only the basic units of a situation and their local interaction are modelled, and then the system is left to evolve. However, the notable progress of the processing power of personal computers, evident in the last two decades, has had little influence on the ways the basic units (artificial animals or animats) are constructed. This impacts largely on the applicability of the methods in other research fields. Our field of choice is the modelling of bird flocks. This area was at its peak in the late 1980s when Craig W. Reynolds presented the first and most influential model -the boids. In spite of his many following works no formal definition has ever been presented. This might be the reason why a second generation of flocking models is still awaited. In this article we make a step forward, all in view of allowing for the development of the second-generation models. We present an artificial animal construction framework that has been obtained as a generalization of the existing bird flocking models, but is not limited to them. The article thus presents a formal definition of the framework and gives an example of its use. In the latter the framework is employed to present a formalization of Reynolds's boids.

Open-ended Evolution in Flocking Behaviour Simulation

2007

In this research we tried to apply open-ended evolution to breed controllers for artificial organism which would be able to manifest flocking behaviours. In comparison to the previous work in this area the artificial world created in our system is characterized by significant diversity of organisms. Animals were equipped with double and dynamic sight, which were suggested by other authors in their works. In experiments, many different behaviours were observed, which were very similar to those in nature, for instance: the escape of herbivore from predators, making herbivores route towards plants or a pursuit of predators after herbivores. Another interesting behaviour was grouping of predators around plants, where the probability of meeting herbivore is greater than in other places. The most advanced behaviour was creation of flocks, which was the goal of experiments. The observed motion of animals looked natural. However, to the full success it was necessary to apply steered evolution.

Scalable behaviors for crowd simulation

Computer Graphics Forum, 2004

Crowd simulation for virtual environments offers many challenges centered on the trade-offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation-based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation-specific behaviors are composed on the fly to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater.