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Distributed Coadaptive Control and Coadaptive Behavior Analysis for Swarm Robots System

Author: YangMao
Tutor: TianYanTao
School: Jilin University
Course: Control Theory and Control Engineering
Keywords: Swarm robotic system Distributed control Synchronous movement Distributed reinforcement learning Foraging
CLC: TP242
Type: PhD thesis
Year: 2010
Downloads: 635
Quote: 4
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This paper studies the group collaborative robot system adaptability . Distributed control based on local information exchange , optimization and learning groups robot system to adapt to the dynamic and complex environment , and reveal the regularity and controllability of emergent behaviors in swarm intelligence system . From groups kinematic model , the dynamic model and distributed reinforcement learning four angles of the three aspects of a more in-depth study . 1, the kinematic model of collaboration Vicsek model and artificial potential field method to achieve a synchronized movement of groups of robots in complex environments , with the artificial coordinated field groups robot obstacle avoidance movement . For synergistic kinetic model by combining the virtual force , the external environmental factors as well as the connection matrix method , respectively for static and dynamic environment is designed based on local information interaction of the distributed controllers , and the corresponding stability analysis , to achieve the groups synchronized motion of the robot system , improved particle swarm optimization algorithm to optimize the controller parameters , reducing the energy loss of the group robotic systems . 3 , the average kinetic energy distributed based on the internal controller design method and stability analysis groups robot foraging in a complex environment . 4 for Distributed reinforcement learning, the state space is too large , a large number of robots , leading to the convergence rate of reinforcement learning is too slow, the right amount of communication can accelerate your learning curve presented here several reinforcement learning algorithm based on the collaboration of the blackboard architecture communication , takes full advantage of multi-robot systems , distributed sensing ability to explore the learning space and collect experiences , and to improve the convergence rate of reinforcement learning . This article research was supported by the National Natural Science Foundation of swarm robots in complex environments the collaborative adaptive system theory and methods \.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
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