Control and simulation of robotic swarms in heterogeneous environments

Simulation provides a low cost method of initial testing of control for robotic swarms. The expansion of robotic swarms to heterogeneous environments drives the need to model cooperative operation in those environments. The Autonomous Control Engineering center at The University of Texas at San Antonio is investigating methods of simulation techniques and simulation environments. This paper presents results from adapting simulation tools for diverse environments.

[1]  Mo M. Jamshidi,et al.  Adaptive modelling: An statistical approach in designing a mathematical-model-based controller , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[2]  Weiliang Xu,et al.  Sensor-based fuzzy reactive navigation of a mobile robot through local target switching , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[3]  Mo M. Jamshidi,et al.  Sonar based Autonomous Automatic Guided Vehicle (AGV) navigation , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[4]  Senén Barro,et al.  Landmark detection in mobile robotics using fuzzy temporal rules , 2004, IEEE Transactions on Fuzzy Systems.

[5]  Patrick Benavidez,et al.  Multi-domain robotic swarm communication system , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[6]  Matthew A. Joordens Design of a low cost underwater robotic research platform , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[7]  P. Sridhar,et al.  Towards optimization of a real-world Robotic-Sensor System of Systems , 2006, 2006 World Automation Congress.

[8]  K. Nagothu,et al.  Communications for Underwater Robotics Research Platforms , 2008, 2008 2nd Annual IEEE Systems Conference.

[9]  Mo M. Jamshidi,et al.  A Discrete Event XML based Simulation Framework for System of Systems Architectures , 2007, 2007 IEEE International Conference on System of Systems Engineering.

[10]  Mo M. Jamshidi,et al.  Simulation of underwater robots using MS Robot Studio© , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[11]  Dong Yue,et al.  Towards net-centric system of systems robotics in air, sea and land , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[12]  K. Nagothu,et al.  RF communication between surface and underwater robotic swarms , 2008, 2008 World Automation Congress.

[13]  Jing Ren,et al.  Modified Newton's method applied to potential field navigation , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[14]  B. Benhabib,et al.  Rendezvous-Guidance Trajectory Planning for Robotic Dynamic Obstacle Avoidance and Interception , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  A.A. Jalali,et al.  Low Frequencies Optimal Control of an Inverted Pendulum , 2006, 2006 1ST IEEE International Conference on E-Learning in Industrial Electronics.

[16]  Simon X. Yang,et al.  Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Hyung Suck Cho,et al.  A Sensor-based Obstacle Avoidance Controller For A Mobile Robot Using Fuzzy Logic And Neural Network , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Tzuu-Hseng S. Li,et al.  Fuzzy target tracking control of autonomous mobile robots by using infrared sensors , 2004, IEEE Transactions on Fuzzy Systems.