Simulating Group Behaviors | Algorithms & Applications Group

Algorithms & Applications Group
Simulating Group Behaviors

Simulating Group Behaviors
supported by NSF, Texas Higher Education Coordinating Board
Samuel Rodriguez, Robert Salazar, Phillip Colemen, Roozbeh Daneshvar, Nancy M. Amato
Project Alumni: Jyh-Ming Lien, Xinyu Tang, O. Burchan Bayazit, Ross T. Sowell, Arnaud Masciotra, Jean-Phillipe Malric

Please visit our updated project page: Naturally-Inspired Exploring, Pursuit and Evasion Behaviors

The objective of our research is to develop efficient techniques for simulating group behaviors. We investigate how agents can work cooperatively to perform tasks, plan paths in dynamic environments, or influence another group of agents to locations in an environment. Group behavior can be observed everywhere. For example, birds fly in flocks, fish swim in schools, sheep move as a herd steered by a dog, agents have objectives in a crowd or game simulation, and ants explore until they find a food source relaying the information to other ants.

The behaviors we explore are typically focused on problems of interest to the robotics community. Searching and exploring behaviors are often studied in robotics. The purpose of exploring behaviors is to have a group of agents explore as much of the environment as possible. Our agents utilize an underlying roadmap to find paths through the environment.

Using our adaptive roadmaps and other motion planning techniques we are able to generate a variety of behaviors. We have successfuly simulated many advanced searching behaviors, shepherding behaviors, planning amongst moving obstacles and naturally inspired pursuit-evasion techniques. The shepherding behaviors involve one group of agents (the shepherds) influencing the movement of another group of agents (the flock). A number of motion planning techniques were used to plan among moving obstacles. We also utilized motion planning techniques to facilitate our naturally inspired pursuit-evasion behaviors.

Below is an outline of aspects of developing group behavior we consider:

Modelling Group Behavior

Objective: Our goal is to come up with a general model for various agents performing different behaviors. We are interesting in modelling robotic-based, naturally-occuring and artificial behaviors.

Rule-Based Framework: In our rule based framework, the agents have a rule set they are encoded and react throughout the simulation based on these sets of rules. While this does allow for the creating of complext behaviors, it often requires encoding many environmental scenarios.

Composable Behaviors: In a composable behavior framework, the behaviors an agent performs can evolve over time from a much more simple set of basis behaviors. We have developed some techniques for composing behaviors for a group of agents.

Scalability: Although we do focus on complex behaviors for small number of agents, we are interested in developing behaviors that can be extended to large numbers of agents. Some areas we are interested in that may required many agents include crowd simulation and control.

Behaviors

Single Group Behaviors: Agents performing these behaviors operate on their own in an environment. Examples of some of these behaviors that we have developed include: homing, covering, and various other forms of exploration.

Multiple Interacting Groups: These behaviors, when applied to agents, operate in conjunction with another set of behaviors. The behaviors often have opposing objectives. Examples of these behaviors include shepherding,laser-tag, or pursuit-evasion scenarios. In these examples, the behavior of one group of agents can effect the behavior of the other groups of agents.

Naturally Inspired: These behaviors have applications in a wide range of areas but are inspired from behaviors that are observed in nature.

In general, our goal is to come up with a representative set of behaviors that can be applied in a number of research areas, including robotics, computer graphics and games.

Challenges for Modelling Complex Societies

Various callenges arise when trying to model complex societies of agents.
Dynamic and Changing Environments: As agents more through the environment, aspects of the environment can dynamically change. For example, obstacles can be dynamically moving. Properties of the environment can also be changing throughout the environment such as areas that are considered dangerous to the agents.

Many different species, types of agents and varying behaviors: In order to model a wide range of societies, we aim to allow for many types of agents with different behavior types. Some example societies we are interested in studying include ant or insect societies, chimpanzees or simulating societies that may have lived long ago.

Papers

A Framework for Planning Motion in Environments with Moving Obstacles

Group Behaviors using Rule-Based Roadmaps

A Framework for Planning Motion in Environments with Moving Obstacles, Sam Rodriguez, Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 3309-3314, Oct 2007. Also, Technical Report, TR06-010, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2007.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Roadmap-Based Group Behaviors: Generation and Evaluation, Samuel Rodriguez, Robert Salazar, Troy McMahon, Nancy M. Amato, Technical Report, TR07-004, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2007.
Technical Report(ps, pdf, abstract)

Composable Group Behaviors, Jyh-Ming Lien, Samuel Rodriguez, Xinyu Tang, John Maffei, Arnaud Masciotra, Technical Report, TR05-006, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2005.
Technical Report(ps, pdf, abstract)

Shepherding Behaviors with Multiple Shepherds, Jyh-Ming Lien, Samuel Rodriguez, Jean-Philippe Malric, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Apr 2005. Also, Technical Report, TR04-003, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2004.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf)

Swarming Behavior Using Probabilistic Roadmap Techniques, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, Lecture Notes in Computer Science, 3342/2005:112-125, Jan 2005.
Journal(ps, pdf, abstract)

Shepherding Behaviors, Jyh-Ming Lien, O. Burchan Bayazit, Ross T. Sowell, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4159-4164, New Orleans, Apr 2004. Also, Technical Report, TR03-006, Parasol Laboratory, Department of Computer Science, Texas A&M University, Nov 2003.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf)

Better Shepherding Behaviors Using Improved Shepherd Locomotion, Ross T. Sowell, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, Technical Report, TR03-009, Parasol Laboratory, Department of Computer Science, Texas A&M University, Aug 2003.
Technical Report(ps, pdf, abstract)

Better Group Behaviors in Complex Environments with Global Roadmaps, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, In Proc. Int. Conf. on the Sim. and Syn. of Living Sys. (Alife), pp. 362-370, Sydney, Australia, Dec 2002.
Proceedings(ps, pdf, abstract)

Better Group Behaviors using Rule-Based Roadmaps, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), pp. 95-111, Nice, France, Dec 2002.
Proceedings(ps, pdf, abstract)

Roadmap-Based Flocking for Complex Environments, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, In Proc. Pacific Conf. on Computer Graphics and App. (PG), pp. 104-113, Beijing, China, Oct 2002. Also, Technical Report, TR02-003, Parasol Laboratory, Department of Computer Science, Texas A&M University, Apr 2002.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Specialized Techniques for Shepherding Behaviors

Shepherding Behaviors with Multiple Shepherds, Jyh-Ming Lien, Samuel Rodriguez, Jean-Philippe Malric, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Apr 2005. Also, Technical Report, TR04-003, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2004.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf)

Shepherding Behaviors, Jyh-Ming Lien, O. Burchan Bayazit, Ross T. Sowell, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4159-4164, New Orleans, Apr 2004. Also, Technical Report, TR03-006, Parasol Laboratory, Department of Computer Science, Texas A&M University, Nov 2003.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf)

Better Shepherding Behaviors Using Improved Shepherd Locomotion, Ross T. Sowell, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato, Technical Report, TR03-009, Parasol Laboratory, Department of Computer Science, Texas A&M University, Aug 2003.
Technical Report(ps, pdf, abstract)

Department of Computer Science and Engineering | Dwight Look College of Engineering | Texas A&M University
Privacy statement: Computer Science and Engineering Engineering TAMU

	Modelling Group Behavior Objective: Our goal is to come up with a general model for various agents performing different behaviors. We are interesting in modelling robotic-based, naturally-occuring and artificial behaviors. Rule-Based Framework: In our rule based framework, the agents have a rule set they are encoded and react throughout the simulation based on these sets of rules. While this does allow for the creating of complext behaviors, it often requires encoding many environmental scenarios. Composable Behaviors: In a composable behavior framework, the behaviors an agent performs can evolve over time from a much more simple set of basis behaviors. We have developed some techniques for composing behaviors for a group of agents. Scalability: Although we do focus on complex behaviors for small number of agents, we are interested in developing behaviors that can be extended to large numbers of agents. Some areas we are interested in that may required many agents include crowd simulation and control.
	Behaviors Single Group Behaviors: Agents performing these behaviors operate on their own in an environment. Examples of some of these behaviors that we have developed include: homing, covering, and various other forms of exploration. Multiple Interacting Groups: These behaviors, when applied to agents, operate in conjunction with another set of behaviors. The behaviors often have opposing objectives. Examples of these behaviors include shepherding,laser-tag, or pursuit-evasion scenarios. In these examples, the behavior of one group of agents can effect the behavior of the other groups of agents. Naturally Inspired: These behaviors have applications in a wide range of areas but are inspired from behaviors that are observed in nature. In general, our goal is to come up with a representative set of behaviors that can be applied in a number of research areas, including robotics, computer graphics and games.
	Challenges for Modelling Complex Societies Various callenges arise when trying to model complex societies of agents. Dynamic and Changing Environments: As agents more through the environment, aspects of the environment can dynamically change. For example, obstacles can be dynamically moving. Properties of the environment can also be changing throughout the environment such as areas that are considered dangerous to the agents. Many different species, types of agents and varying behaviors: In order to model a wide range of societies, we aim to allow for many types of agents with different behavior types. Some example societies we are interested in studying include ant or insect societies, chimpanzees or simulating societies that may have lived long ago.