Bio-inspired Emergent Control of Locomotion Systems
World Scientific, 2004 - Technology & Engineering - 198 pages
This book deals with locomotion control of biologically inspired robots realized through an analog circuital paradigm as cellular nonlinear networks. It presents a general methodology for the control of bio-inspired robots and several case studies, as well as describes a new approach to motion control and the related circuit architecture.
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CNNbased Central Pattern Generators
CNNbased CPGs with sensory feedback and VLSI
Decentralized locomotion control
A gallery of bioinspired robots
attitude control and Motor
Turing patterns and autowaves
two tools for bio
Appendix B Design of the CNN circuit
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actuators algorithm allows analog approach attitude control autowaves behavior bio-inspired robots biological biped chaotic Chap chemical synapse chip Chua Chua's circuit circuit CNN cells CNN neuron CNN-based CPG CNNLab connections control scheme control system corresponding CPG cell CPPM Cruse decentralized locomotion control dynamical systems equation equilibrium point error example fast gait fitness function genetic algorithms given ground contact hardware HexaDyn hexapod robot implementation influences initial conditions input joint learning leg controller leg R2 limit cycle locomotion pattern modulator Moreover Motor Maps motor-neurons MTA hexbot navigation control number of neurons obstacles obtained output paradigm parameters performed phase lags phase plane Poincare-Bendixson Theorem RD-CNN return stroke saturation sensor sensory feedback shown in Fig simulation speed stable stance phase stick insect strategy suitable swing phase synchronization templates terrain tetrapod gait trajectory tripod gait Turing patterns unsupervised learning variable VLSI waveforms winner neuron