A mobile robot network is said to be easily scalable to any number of robots if its performance is kept almost fixed after these robots are added or some fail in the network. An interaction dynamics model based on mot...A mobile robot network is said to be easily scalable to any number of robots if its performance is kept almost fixed after these robots are added or some fail in the network. An interaction dynamics model based on motion synchronization is first established. Considering the mobility of mobile robot networks, we propose a relay switched, distributed topology control for the scalable network to drive neMy added robots to the most suitable positions with more neighbors as well as self-heal the blank positions of failed robots, and give a metric of the topology structure for evaluating the performance of network topologies. Then, we prove the stability of motion synchronization with the individual control based on Lyapunov exponent. Finally, the results of simulations have demonstrated the validity of the proposed modeling and control methods.展开更多
In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural n...In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.展开更多
基金Supported by the National High Technology Research and Development Programme of China ( No. 2006AA040203 )the National Natural Science Foundation of China (No. 60775062)the Program for New Century Excellent Talents in University (No. NCET-07-0538).
文摘A mobile robot network is said to be easily scalable to any number of robots if its performance is kept almost fixed after these robots are added or some fail in the network. An interaction dynamics model based on motion synchronization is first established. Considering the mobility of mobile robot networks, we propose a relay switched, distributed topology control for the scalable network to drive neMy added robots to the most suitable positions with more neighbors as well as self-heal the blank positions of failed robots, and give a metric of the topology structure for evaluating the performance of network topologies. Then, we prove the stability of motion synchronization with the individual control based on Lyapunov exponent. Finally, the results of simulations have demonstrated the validity of the proposed modeling and control methods.
文摘In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.
