Biomorphic rhythmic movement controller

Abstract

An artificial Central Pattern Generator (CPG) based on the naturally-occuring central pattern generator locomotor controller for walking, running, swimming, and flying animals may be constructed to be self-adaptive, by providing for the artificial CPG, which may be a chip, to tune its behavior based on sensory feedback. It is believed that this is the first instance of an adaptive CPG chip. Such a sensory feedback-using system with an artificial CPG may be used in mechanical applications such as a running robotic leg, in walking, flying and swimming machines, and in miniature and larger robots, and also in biological systems, such as a surrogate neural system for patients with spinal damage.

Description

RELATED APPLICATION [0001] This is a PCT application claiming priority based on U.S. application 60 / 201,748 filed May 4, 2000, which is incorporated by reference herein.[0002] Statement Regarding Government Funding [0003] This invention was made under NSF grant number 9896362. The Government may have certain rights in this invention.FIELD OF THE INVENTION [0004] The invention generally relates to robotics and movement control, and more particularly, to rhythmic movement control systems that are sensitive and adaptive to the environment in which they are used. BACKGROUND OF THE INVENTION [0005] Basic rhythmic movements in animals are generated by a network of neurons in the spinal cord called the Central Pattern Generator (“CPG”). Walking, running, swimming, and flying animals have a biological locomotor controller system based on a CPG, which is an autonomous neural circuit generating sustained oscillations needed for locomotion. Naturally-occurring CPGs have been studied and are be...

AI Technical Summary

Benefits of technology

"The present invention provides a CPG-based system that can adapt and respond to its environment, making it suitable for use in biological and non-biological applications. The system can control the movement of mechanical and biological systems using a non-biological CPG chip. It can also include advanced features such as phase adjustment and responsiveness to sensory feedback. The invention also provides a system for rhythmic movement control and a chip that contains electronic analogues of biological neurons, synapses, and time-constraints. The CPG-based system can be a non-linear oscillator or a distributed system of non-linear oscillators. It can also include at least one sensor for collecting sensory feedback. Overall, the invention provides a flexible and adaptable platform for controlling movement and behavior in biological and non-biological systems."

Problems solved by technology

However, Still's system has no motoneuron output stage, and cannot respond to or adapt based on sensory input.

Their system is not a robotic system and cannot respond to or use feedback from sensors.

This system of DeWeerth's cannot easily be applied to control a robot, primarily because parameter sensitivity makes such circuits difficult to tune.

The adapted DeWeerth system, however, is not adaptive and does not use external inputs from sensors.

's neural-based approach, using software, is relatively basic and does not teach, for example, VLSI implementation, self-adaptation to an environment, low-power compact implementation using one chip, or silicon learning.

Such systems have limitations, such as being unable to provide self-adaptive features.

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