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Home Page for Andrew Giese | Parasol Laboratory


Picture Andrew Giese
MS Student
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~awg4619/
Department of Computer Science and Engineering email:
Texas A&M University office: 407A HRBB
College Station, TX 77843-3112 tel:
USA fax: (979) 458-0718


Hi, my name is Andy Giese and I'm a Master's of Science in Computer Science student here at Texas A&M. I completed my undergraduate degree in Computer Science at the University of Dayton in Dayton, OH in 2010. For more in-depth information about my background and experience, feel free to check out my resume.

I'm interested in multi-agent systems, particularly large scale simulated behaviors. How do groups of people interact in densely packed environments? How do they avoid collision? Where and how does congestion form? How do we alleviate congestion? How do we make teams of robots more efficient? I've published a few papers regarding these topics.

I'm also interested in a lot of other things, like machine learning, motion planning, behavior modelling with finite state machines, and geometry. I detail many of my programming projects (personal and academic) at my blog.


Multi-Agent Systems:

Group 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. Our goal is create a framework for simulating and controlling communities of characters that can dynamically interact with each other and their environment. There are many important applications of this system, ranging from civil crowd control (e.g., planning exit strategies from buildings or sporting event venues), to education and training (e.g., providing museum exhibits or training systems), to entertainment (e.g., interactive games). While there are existing methods that focus on the simulation aspect, there is a lack of methods that support the interaction and control (or steering) of multiple groups of agents.
Evacuation Planning with Direction and Ingress/Egress
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.
Reciprocally-Rotating Velocity Obstacles
Reciprocally-Rotating Velocity Obstacles (RRVO) is a scalable collision avoidance technique for multi-agent systems that generalizes and extends Optimal Reciprocal Collision Avoidance (ORCA). In this work, we empower agents to actively rotate in order to avoid collision with each other. Whereas before, agents were generally assumed to be represented as circles, RRVO relaxes this assumption to allow for any convex polygon. The resulting method allows one to more accurately model their agents, and permits more realistic motion in the form of rotation. RRVO has application in any multi-agent simulation, including evacuation, pursuit-evasion, urban planning, and more.


Publications


  1. Andrew Giese, Daniel Latypov, Nancy M. Amato. Reciprocally-Rotating Velocity Obstacles. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. to appear, Hong Kong, China, Jun 2014. pdf
  2. Denny, Jory, Andrew Giese, Aditya Mahadevan, Arnaud Marfaing, Rachel Glockenmeier, Colton Revia, Samuel Rodriguez, and Nancy M. Amato. "Multi-robot caravanning." In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, pp. 5722-5729. IEEE, 2013. pdf
  3. Rodriguez, Samuel, Andrew Giese, and Nancy Amato. "Improving Aggregate Behavior in Parking Lots with Appropriate Local Maneuvers." In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS) (2013). pdf
  4. Rodriguez, Samuel, Andrew Giese, Nancy Amato, Saied Zarrinmehr, Firas Al-Douri, and Mark Clayton. "Environmental Effect on Egress Simulation." Motion in Games (2012): 7-18. pdf
  5. Rapoch, Terry, James Hooker, Andrew Giese, Andrew Hamilton, Valerie Shalin, Jeffrey Cowgill, and Robert Gilkey. "Evaluating the Use of Auditory Systems to Improve Performance in Combat Search and Rescue." WRIGHT STATE APPLIED RESEARCH CORP DAYTON OH, 2012. pdf
  6. Giese, Andrew, and Jennifer Seitzer. "Using a Genetic Algorithm to Evolve a D* Search Heuristic." In Midwest Artificial Intelligence and Cognitive Science Conference, p. 67. 2011. pdf



Bright and Langford Models


Models of the Bright and Langford Buildings for motion planning problems.

Bright and Langford 1 Bright and Langford 2 Bright and Langford 3 Bright and Langford 4 Bright and Langford 5