Home research People General Info Seminars Resources Intranet
| Algorithms & Applcations Group | Home | Research | Publications | People | Resources | News

Algorithms & Applications Group
Composable Group Behaviors

Composable Group Behaviors
supported by NSF
Jyh-Ming Lien, Samuel Rodriguez, Xinyu Tang, Nancy M. Amato
Project Alumni: Arnaud Masciotra

Creating animations with complex and realistic group behaviors can be a difficult and time consuming task. This generally involves associating all possible behaviors of agents and associating the behaviors with all environmental events.

In this work, we investigate methods to ease the process of producing realistic group behaviors. More specifically, the main goal of this research is to simulate group behaviors by automatically combining a given set of simple composable behaviors for applications such as games, virtual reality, robotics and biological/ecological simulation. The result of this research is an easy to use, adaptive and flexible framework for simulating group behaviors.

A system overview (click to enlarge the image)

Our system is built on the top of a roadmap based system.
  • A roadmap represents the connectivity of the feasible space in a given environment and is used as an internal representation of the world for agents.
  • Agents can modify the properties of the roadmap.
  • An agent can store location-specific information, such as the action that the agent takes, in a node of a roadmap.
  • The result of an action will be evaluated by a user defined utility function. The agent will select an action based on the past performance of the action.
  • The learned information can also be shared between agents when their relative states allow communication (e.g. proximity and other values).
As shown in the above figure, the proposed framework consists of several main components, i.e., agents, roadmaps, user defined behaviors and utility functions. We studied three types of behaviors:

Pursuit/Evasion Behaviors

In this experiment, we show that an agent can adapt to its performance dramatically better by simply adding more behaviors to it.
The left figure shows the environment for this experiment. The prey are designed to only have the patrolling and evasion behaviors and normally walk back and forth along the patrolling path and start running away when predators are nearby. The predators can choose to either wait or search for prey in the whole environment if it does not see any. When they see a prey, they will start pursuing and then attacking it. Prey can run four times faster and see two times farther than the predator so that the prey becomes hard to catch.

Movie: Pursuit and Evasion (divx avi) 14.4 MB

Laser Tag

In the game of laser tag, agents work as teams in order to score points against agents from opposing teams.
An agent's goal is to shoot at opponents. After a specified number of hits against an agent, that agent can no longer participate in the game. Our laser tag environment, shown in the left figure, consists of two flock groups. The first flock group (called snipers) is equipped with a longer range of sight but a more restricted viewing angle. The viewing angle is the total angle in the heading direction that the flock member can see. The second flock group (called infantry) has a shorter range of sight but a much larger view angle.

Movie: Laser Tag (divx avi) 15.9 MB

Shepherding (see also Specialized Techniques for Shepherding Behaviors)

The idea of composable behaviors can be readily applied to compose shepherding behaviors from a set of simple primitive behaviors we call locomotions. The utility function of the shepherding behaviors is designed to encourage the shepherds to decrease the distance between the flock and the goal position and also to encourage the shepherds to keep the flock as one group. The figures below show a sequence of images captured from the herding simulation with a group of shepherd controls the motion of a group of flock.


Movie: Shepherding (divx avi) 9.3MB




Related Projects

Group Behaviors using Rule-Based Roadmaps
Shepherding Behaviors
Planning Among Moving Obstacles


Papers

Interaction Templates for Multi-Agent Systems, James Motes, Read Sandstrom, William Adams, Tobi Ogunyale, Shawna Thomas, Nancy M. Amato, Technical Report, TR18-002, Parasol Laboratory, Department of Computer Science, Texas A&M University, Jul 2018.
Technical Report(pdf, abstract)

Reciprocally-Rotating Velocity Obstacles, Andrew Giese, Daniel Latypov, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. to appear, Hong Kong, China, Jun 2014. Also, Technical Report, TR13-009, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, TX U.S.A., Oct 2013.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Reciprocally-Rotating Velocity Obstacles, Andrew Giese, Masters Thesis, Department of Computer Science and Engineering, Texas A&M University, College Station, USA, May 2014.
Masters Thesis(ps, pdf, abstract)

Multi-Robot Caravanning, Jory Denny, Andrew Giese, Aditya Mahadevan, Arnaud Marfaing, Rachel Glockenmeier, Colton Revia, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 5722 - 5729, Tokyo, Japan, Nov 2013.
Proceedings(pdf, abstract)

Optimizing Aspects of Pedestrian Traffic in Building Designs, Samuel Rodriguez, Yinghua Zhang, Nicholas Gans, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Nov 2013.
Proceedings(pdf, abstract)

Improving Aggregate Behavior in Parking Lots with Appropriate Local Maneuvers, Samuel Rodriguez, Andrew Giese, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Nov 2013.
Proceedings(pdf, abstract)

Environmental Effect on Egress Simulation, Samuel Rodriguez, Andrew Giese, Nancy M. Amato, Saeid Zarrinmehr, Firas Al-Douri, Mark Clayton, In Proc. of the 5th Intern. Conf. on Motion in Games (MIG), 2012, in Lecture Notes in Computer Science (LNCS), pp. to appear, Rennes, Brittany, France, Nov 2012.
Proceedings(ps, pdf, abstract)

A Sampling-Based Approach to Probabilistic Pursuit Evasion, Aditya Mahadevan, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3192 - 3199, St. Paul, Minnesota, USA, May 2012.
Proceedings(pdf, abstract)

Roadmap-Based Techniques for Modeling Group Behaviors in Multi-Agent Systems, Samuel Rodriguez, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, Jan 2012.
Ph.D. Thesis(ps, pdf, abstract)

Roadmap-Based Level Clearing of Buildings, Samuel Rodriguez, Nancy M. Amato, In Proc. of the 4th Intern. Conf. on Motion in Games (MIG), 2011, in Lecture Notes in Computer Science (LNCS), pp. 340-352, Edinburgh, UK, Oct 2011.
Proceedings(ps, pdf, abstract)

Toward Realistic Pursuit-Evasion Using a Roadmap-Based Approach, Samuel Rodriguez, Jory Denny, Juan Burgos, Aditya Mahadevan, Kasra Manavi, Luke Murray, Anton Kodochygov, Takis Zourntos, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1738-1745, May 2011.
Proceedings(ps, pdf, abstract)

Roadmap-Based Pursuit-Evasion in 3D Structures, Samuel Rodriguez, Jory Denny, Aditya Mahadevan, Jeremy (Cong-Trung) Vu, Juan Burgos, Takis Zourntos, Nancy M. Amato, In Proc. of 24th Intern. Conf. on Computer Animation and Social Agents (CASA), 2011, in Transactions on Edutainment, pp. to appear, May 2011.
Proceedings(ps, pdf, abstract)

Utilizing Roadmaps in Evacuation Planning, Samuel Rodriguez, Nancy M. Amato, In Proc. of 24th Intern. Conf. on Computer Animation and Social Agents (CASA), 2011, in Intern. J. of Virtual Reality (IJVR), pp. 67-73, May 2011.
Proceedings(ps, pdf, abstract)

Toward Simulating Realistic Pursuit-Evasion Using a Roadmap-Based Approach, Samuel Rodriguez, Jory Denny, Takis Zourntos, Nancy M. Amato, In Proc. of the 3rd Intern. Conf. on Motion in Games (MIG), 2010, in Lecture Notes in Computer Science (LNCS), pp. 82-93, Nov 2010.
Proceedings(ps, pdf, abstract)

Behavior-Based Evacuation Planning, Sam Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 350-355, Anchorage, AK, May 2010.
Proceedings(ps, pdf, abstract)

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.
Proceedings(ps, pdf, abstract)

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, 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)



Parasol Home | Research | People | General info | Seminars | Resources  

Parasol Laboratory, 425 Harvey R. Bright Bldg, 3112 TAMU, College Station, TX 77843-3112 
parasol-admin@cse.tamu.edu      Phone 979.458.0722     Fax 979.458.0718 

Department of Computer Science and Engineering | Dwight Look College of Engineering Dwight Look College of Engineering | Texas A&M University
    
Privacy statement: Computer Science and Engineering Engineering TAMU
Web Accessibility Policy and Law - Web Accessibility and Usability Standards  -   Contact Webmaster