Simulating Group Behaviors | Algorithms & Applications Group

Algorithms & Applications Group
Simulating Group Behaviors

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

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.

In our research, we investigate how to plan motion in dynamic/uncertain environments along with integrating adaptive roadmaps with traditional flocking techniques to generate complex global behaviors that are difficult to generate using traditional emergent approaches such as flocking. An adaptive roadmap is a roadmap (graph) containing representative paths in the environment whose edge values can be updated according to information gathered by the flock members. Our shepherding behaviors investigate how one group of agents can influence another group of agents, the herd, to locations in the environment.

A Framework for Planning Motion in Environments with Moving Obstacles
We present a heuristic approach to planning in an environment with moving obstacles. Our approach assumes that the robot has no knowledge of the future trajectory of the moving objects. Our framework also distinguishes between two types of moving objects:hard and soft objects in the environment. We distinguish between the two types of objects in the environment as varying application domains could allow for some collision between moving objects.
A Framework for Planning Motion in Environments with Moving Obstacles Publications

Group Behaviors using Rule-Based Roadmaps
The first behavior we study is homing behavior when the goal is to move the flock from a starting position to a goal position. The second behavior we study is exploring. We consider two kinds of exploring, covering and goal searching. Our third behavior is shepherding, where a flock is steered by an outside agent. Finally, Narrow passage demostrates group behaviors that depend on surrounding environment using rule based roadmap.
Generation and Evaluation of Roadmap-Based Group Behaviors
We study in depth the generation of roadmap-based searching and hiding. We first propose a variety of these behaviors and then show how these behaviors can be evaluated.

Group Behaviors using Rule-Based Roadmaps Publications

Specialized Techniques for Shepherding Behaviors
Techniques for a single shepherd
Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. In this work, we investigate ways to simulate these types of behaviors.
Techniques for multiple shepherds
When the size of the flock gets large or if the flock's behavior makes it difficult to influence, a single shepherd cannot adequately control the flock. In this work, we study how a group of shepherds can work cooperatively without communication to efficiently control the flock.

Composable Group Behaviors
In this research, we investigate techniques to ease the process of generating realistic and complex group behaviors. Moreover, we provide a "learning" framework that allows these behaviors to adapt to new environments and new tasks and that allows users to make modifications easily.

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 | Dwight Look College of Engineering | Texas A&M University
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	A Framework for Planning Motion in Environments with Moving Obstacles We present a heuristic approach to planning in an environment with moving obstacles. Our approach assumes that the robot has no knowledge of the future trajectory of the moving objects. Our framework also distinguishes between two types of moving objects:hard and soft objects in the environment. We distinguish between the two types of objects in the environment as varying application domains could allow for some collision between moving objects. A Framework for Planning Motion in Environments with Moving Obstacles Publications
	Group Behaviors using Rule-Based Roadmaps The first behavior we study is homing behavior when the goal is to move the flock from a starting position to a goal position. The second behavior we study is exploring. We consider two kinds of exploring, covering and goal searching. Our third behavior is shepherding, where a flock is steered by an outside agent. Finally, Narrow passage demostrates group behaviors that depend on surrounding environment using rule based roadmap. Generation and Evaluation of Roadmap-Based Group Behaviors We study in depth the generation of roadmap-based searching and hiding. We first propose a variety of these behaviors and then show how these behaviors can be evaluated. Group Behaviors using Rule-Based Roadmaps Publications
	Specialized Techniques for Shepherding Behaviors Techniques for a single shepherd Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. In this work, we investigate ways to simulate these types of behaviors. Techniques for multiple shepherds When the size of the flock gets large or if the flock's behavior makes it difficult to influence, a single shepherd cannot adequately control the flock. In this work, we study how a group of shepherds can work cooperatively without communication to efficiently control the flock.
	Composable Group Behaviors In this research, we investigate techniques to ease the process of generating realistic and complex group behaviors. Moreover, we provide a "learning" framework that allows these behaviors to adapt to new environments and new tasks and that allows users to make modifications easily.