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Agent Base Control of a Robotic Swarm with Sensor Noise Effects

Mahpour, Aidin | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 40748 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Vossoughi, Gholamreza
  7. Abstract:
  8. The main objective of this project is to investigate modeling for a robotic swarm with suitable sensors and to analyze effect of sensory noise on the control and unity of the swarm. This project is an extension of analytical study done by S. Etemadi, A. Alasty and G. Vossoughi in Sharif University of technology. The flocking frame assumed to have a leader robot that controls over all behavior of the swarm made of agents’ robots with limited equipments and low intelligence that only obey some basic attraction and repulsion laws that will be explained. As an initial step of the analysis, we investigate proper physical model, sensors and navigation systems suitable to the model. Next, the sensitivity of the model to the sensory noise is addressed and effect of a low pass filter to minimize the effect of sensor noise is investigated. So we seek an appropriate physical model, sensor type and array for proposed algorithm and develop Kalman filter based estimation control by rearranging the proposed system control law. Since the leader is supposed to be aware of its position in global coordination system, so it is assumed to be equipped with the different type of sensors, like infrared range sensors, sonar sensors, GPS, odometer and any other type as seen necessary. Agents are assumed to be equipped with minimum number of sensors that will be required to view other agents nearby however, agents are expected not to be aware of their position in global coordinate system. Effect of sensory noise on the coordination control algorithm is studied by simulation and illustrated statically. Taking into consideration of sensory noise, controlling the motion of the swarm will be more realistic and the coordination algorithm is shown remain robust
  9. Keywords:
  10. Robotic Swarm ; Kalman Filters ; Sensor Fusion

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