This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is design...This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass （CoM） online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the gen- erated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on ir- regular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.展开更多
A bionic neural network for fish-robot locomotion is presented. The bionic neural network inspired from fish neural net- work consists of one high level controller and one chain of central pattern generators （CPGs）....A bionic neural network for fish-robot locomotion is presented. The bionic neural network inspired from fish neural net- work consists of one high level controller and one chain of central pattern generators （CPGs）. Each CPG contains a nonlinear neural Zhang oscillator which shows properties similar to sine-cosine model. Simulation re, suits show that the bionic neural network presents a good performance in controlling the fish-robot to execute various motions such as startup, stop, forward swimming, backward swimming, turn right and turn left.展开更多
基金National Natural Science Foundation （Nos. 61673300, 61573260） and Funda- mental Research Funds for the Central Universities, and Natural Science Foundation of Shanghai （No. 16JC 1401200）.
文摘This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass （CoM） online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the gen- erated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on ir- regular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.
文摘A bionic neural network for fish-robot locomotion is presented. The bionic neural network inspired from fish neural net- work consists of one high level controller and one chain of central pattern generators （CPGs）. Each CPG contains a nonlinear neural Zhang oscillator which shows properties similar to sine-cosine model. Simulation re, suits show that the bionic neural network presents a good performance in controlling the fish-robot to execute various motions such as startup, stop, forward swimming, backward swimming, turn right and turn left.
