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Algorithms & Applications Group
Simulating Group Behaviors

Simulating Group Behaviors
supported by NSF
Sam Rodriguez, Robert Salazar, Phillip Coleman, Jory Denny, Roozbeh Daneshvar, Xinyu Tange, O. Burchan Bayazit, Jyh-Ming Lien, Nancy M. Amato

All Behaviors

Homing
Basic Covering
Scan Explore
Rendezvous Explore
Territorial Explore
Patrolling
Hiding Behaviors
Pursuing Behaviors
Shepherding

Exploration

Homing (see also Simulating Flocking Behaviors in Complex Environments)

At any given time there is one goal, and when all flock members reach it, a new goal is randomly generated; this process continues until eight goals have been generated and reached. The experiment involves 40 flock members, which are initially placed according to a Gaussian distribution around the center of the square environment.

Movie: 2D Homing (mpeg 10.5MB), 3D Homing (mpeg 1.5MB)

Basic Covering (see also Naturally-Inspired Group Behaviors and Roadmap-Based Group Behaviors)

In this experiment, we compare basic flocking behavior, roadmap-based behavior, and an ideal variant of the roadmap-based behavior which has dynamic knowledge of the undiscovered regions. In the movie, the flock starts at the center of the environment. As the members visit new regions, the covered areas are highlighted. When a roadmap based approach is used, more of the environment is covered as shown in the animations.

Movie: Covering1 (mpeg 10.9MB), Covering2 (mpeg 3.3MB), Covering3 (mpeg 3.3MB)

Scan Explore (see also Naturally-Inspired Group Behaviors)

The agents use the roadmap for finding paths to unexplored regions in the environment. The agents first generate points that are unobservable to the agent’s current location. Next the agents attempt to make scan points visible by moving toward unobserved locations. Communication can be employed to make the behavior more effective and improve coverage.

Movie: Scan Explore (mpeg 4.7MB),

Rendezvous Explore (see also Naturally-Inspired Group Behaviors)

This behavior is inherently group based. In groups, a goal location is generated. Paths are found through the environment using the roadmap. The agents adapt the roadmap so that paths extracted are biased to unexplored areas. This has the effect of having the agents taking separate paths to arrive at the goal location.

Movie: Rendezvous Explore in a Maze (mpeg 2.8MB), Rendezvous Explore in a Forest (mpeg 1.3MB)

Territorial Explore (see also Roadmap-Based Group Behaviors)

This behavior attempts to partition the environment into distinct regions. The agents then randomly explore their territory. The roadmap is utilized when partitioning the environment.

Movie: Territorial Explore (mpeg 2.4MB),

Patrolling Behaviors (see also Naturally-Inspired Group Behaviors)

Based off behavior seen in chimpanzees and wolves, the patrolling behavior attempts to periodically cover the area around the border of a territory. This allows the agents to effectively protect a territory. The roadmap is used to form the paths along the border.

Movie: Patrolling with 4 agents (mpeg 1.9MB), Patrolling with 20 agents (mpeg 3.5MB), Patrolling with 40 agents (mpeg 5.8MB)

Evasion

Hiding Behaviors (see also Naturally-Inspired Group Behaviors)

We have studied a number of different hiding techniques. In a basic hiding behavior an agent will stay at a hiding location until it is discovered. When discovered, the agent will then determine the next hiding location. More advanced versions of this behavior will select paths to the new hiding location based on how "hidden" the paths are considered. Agents can also maintain an idea of potentially dangerous zones where searching had been observed before.

Movie: Flee-and-Freeze (mpeg 1.5MB), Flee-and-Hide (mpeg 3.6MB),

Pursuing

Pursuing Behaviors (see also Naturally-Inspired Group Behaviors)

Pursuit behaviors are used to track and attempt to apprehend a target. These occur in nature when predators hunt their prey, such as lions, dogs, or dolphins. Their behaviors range from a basic chase to assuming formations and driving the prey towards the other hunters. Pursuit-Evasion games are also studied in robotics. We have studied a number of naturally inspired pursuit behaviors in conjunction with our searching behaviors.

Movie: Pursuit in a Maze (mpeg 3.0MB), Pursuit in a Forest (mpeg 1.8MB)

Others

Shepherding (see also Specialized Techniques for Shepherding Behaviors)

In this experiment, one set of agents(the shepherds), steers a herd of sheep. The sheep show basic flocking behavior (they tend to stay together) while trying to avoid the shepherd. We have studied advanced shepherding techniques for a single shepherd and multiple shepherds.

Movie: Shepherding (mpeg 10.8MB)

Narrow Passage


This experiment shows that flock's behavior depends on the surrounding environment. Different group formations may be used in relatively open areas than when passing through narrow regions.

A naive way to achieve narrow passage traversal by the flock is to use the homing behavior. One drawback of this approach is that flock members may bunch up and conflict with each other as they try to move through the passage. Using rule based roadmap, we first assemble the flock in front of the narrow passage, and then select the closest flock member to the entrance to the narrow passage as the leader. Then, the remaining flock members are arranged into a queue that follows the leader.

Movie:

  • Narrow Passage (Rule Based) (mpeg 3.7MB)
  • Narrow Passage (Homing, without rules) (mpeg 1.9MB)


  • Flocking behavior with rule based roadmap


    Flocking behavior without rule based roadmap (homing behavior)





    Related Projects

    Planning Motion Among Moving Obstacles
    Shepherding Behaviors
    Composable Group Behaviors


    Papers

    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)



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