Nonlinear Circuits And Systems For Neuro Inspired Robot Control

This book guides readers along a path that proceeds from neurobiology to nonlinear-dynamical circuits, to nonlinear neuro-controllers and to bio-inspired robots. It provides a concise exploration of the essence of neural processing in simple animal brains and its adaptation and extrapolation to modeling, implementation, and realization of the analogous emergent features in artificial but bio-inspired robots: an emerging research field. The book starts with a short presentation of the main areas of the Drosophila brain. These are modeled as nonlinear dynamical structures, which are then used to showcase key features like locomotion, motor learning, memory formation, and exploitation. It also discusses additional complex behaviors, such as sequence learning and perception, which have recently been discovered to exist in insects. Much of the material presented has been tested in biorobotics classes for the Master’s degree in Automation Engineering and Control of Complex Systems at the University of Catania. Reporting on the work fostered by several national and international research projects, the book offers researchers novel ideas on how neuro-inspired dynamics can be used in developing the autonomous machines of the future.

This volume is a special Issue on "Dynamical Systems, Wave based computation and neuro inspired robots'^ based on a Course carried out at the CISM in Udine (Italy), the last week of September, 2003. From the topics treated within that Course, several new ideas were f- mulated, which led to a new kind of approach to locomotion and p- ception, grounded both on biologically inspired issues and on nonlinear dynamics. The Course was characterised by a high degree of multi disciplinarity. In fact, in order to conceive, design and build neuro inspired machines, it is necessary to deeply scan into different d- ciplines, including neuroscience. Artificial Intelligence, Biorobotics, Dynamical Systems theory and Electronics. New types of moving machines should be more closely related to the biological rules, not discarding the real implementation issues. The recipe has to include neurobiological paradigms as well as behavioral aspects from the one hand, new circuit paradigms, able of real time control of multi joint robots on the other hand. These new circuit paradigms are based on the theory of complex nonlinear dynamical systems, where aggregates of simple non linear units into ensembles of lattices, have the pr- erty that the solution set is much richer than that one shown by the single units. As a consequence, new solutions ^'emerge'\ which are often characterized by order and harmony.

There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

This Research Topic presents bio-inspired and neurological insights for the development of intelligent robotic control algorithms. This aims to bridge the inter-disciplinary gaps between neuroscience and robotics to accelerate the pace of research and development.

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. Bio-inspired Emergent Control of Locomotion Systems provides researchers with a guide to the fundamentals of the topics. Moreover, neuro-biologists and physiologists can use the book as a starting point to design artificial structures for testing their biological hypotheses on the animal model.