Evacuation Behaviors
supported by NSF
Samuel Rodriguez, Robert Salazar, Phillip Coleman, Jory Denny, Nancy M. Amato

In this work, we use our framework for simulating and controlling communities of characters that can interact with each other and their environment, and can dynamically react to changes. An evacuation situation, is an example where complex interaction is needed between many agents in a scene. In an evacuation scenario, agents attempt to flee an area while some agents (for example law enforcement) may attempt to direct the agents to safe areas. The direction given can either serve as a guide to assist the evacuating agents or as a command (a route the agents should not deviate from). Similar situations can be seen when traffic lights are out and law enforcement has to direct traffic. We are interested in studying is the evacuation of agents under a variety of conditions. By being able to simulate agents that are evacuating an area, we can study the effects of things such as exit placements, available exits, and agents directing the evacuating agents through the environment. In these scenarios we have some agents that are attempting to evacuate the first floor of a building. The agents have to find paths to the safe areas. We start by studying a building evacuation consisting of several hundred evacuating agents that take into account barriers and directing agents placed throughout the environment to control the evacuating agents.

Evacuation Example This is an example environment which shows many of the capabilities that we are able to simulate, some unique to our system. This environment consists of two rooms with a number of area types available, depending on the scenario. The area types available range from three exits in the main room, two safe areas (at the lower and right portions of the environment), and a potential dangerous area in the second room. Thirty evacuating agents begin the simulation clustered at the center of the lower room. In scenarios (b)-(e) a director is present to guide evacuation. A full evacuation is shown in scenario (a). (a) Full Evacuation. (b) With Director (2EXs). (c) With Director (2EXs+1SA). (d) Evacuation w/ Director (Grouping+2EXs+1SA). (e) Evacuation w/ Director (2EXs+1SA+1DA).

Evacuation Bright Here we show evacuation results for agents evacuating a building under different evacuation conditions. The test environment is the first floor of a building at Texas A&M University housing our department. Agents that will be evacuating are randomly placed in rooms throughout the building. As the simulation progresses, the agents try to evacuate the building, utilizing the roadmap to find paths to safe areas. We are able to look at many different evacuation scenarios and analyze the results. We show experimental results with 500 agents evacuating the first floor, varying the number of available exits, the amount and type of direction given during the evacuation and which exits, if any, are being prohibited or blocked. Example scenario 1 - Evacuation In the evacuation shown here, the agents attempt to evacuate by finding the nearest available exit. The agents try to find a path to this nearest safe exit and then to the safe location. Example of agents evacuating the Bright building (5 exits). Example of agents evacuating the Bright building (4 exits). Example of agents evacuating the Bright building (3 exits). Example scenario 2 - Evacuation with Direction In this scenario we have agents that are directing the evacuating agents away from one side of the environment. As the agents come within range of the directing agent, they are told of a potentially dangerous area and prevented from exiting from one side of the building. Agents that are directed away from an area then have to replan to find a route to a safe area. In the first animation, 1 directing agent is present to direct agents away from one available exit. In the second animation, 4 directing agents are placed throughout the environment and tell the evacuating agents about the area to avoid. Using this kind of simulating we can study the flow of agents and potential problems that could arise during evacuation. This could also give insight into how many agents should be present to direct and the placement of directing agents to enable a good evacuation procedure the evacuating agents. Example of agents evacuating with one barrier. Example of agents evacuating with two barriers. Example of agents evacuating with two global directors. Example of agents evacuating with four global directors. Example of agents evacuating with five global directors. Example of agents evacuating with one global director and two points of direction. Example of agents evacuating with one global director and four points of direction. Regrouping Example We show this regrouping example to show the versatility of our framework. In this scenario agents are dispersed around an environment and regroup to safe locations defined throughout the environment. The animations show four different scenarios we were able to generate evacuation plans for. In the first scenario, the agents regroup to the nearest safe area. In the second example, directing agents are placed at exits E3 and E5 to simulate a partial blockage of the corridor. The result is that the agents still use both safe areas although they do not pass through the blocked corridor. In the third and fourth scenario, two advanced directing agents are placed at the locations shown in the figure near E3 and E5. These agents, with a larger view radius, are alerting the evacuating agents to the dangerous areas present in the corridor. The result is that the agents regroup at the safe areas near the upper corridor in the environment. In the fourth scenario grouping is used as a restriction for the agents. (a) Basic Regroup. (a) Partial blockage of Lower Corridor. (a) Full Block of Lower Corridor. (a) Full Block of Lower Corridor (Grouping).

Related Projects

Our Framework
Group Behaviors We've Studied

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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