Takayuki Matsuo

Biomimetic Motion Control System Based on a CPG for an Amphibious Multi-Link Mobile Robot

Robots and robotics technologies are expected to provide new tools for inspection and manipulation, especially in extreme environments that are dangerous for human beings to access directly, such as underwater environments, volcanic areas, or nuclear power plants. Robots designed for such extreme environments should be sufficiently robust and strong to cope with disturbance and breakdowns. We focus on the movement of animals to realize robust robot systems. One approach is to mimic the nervous systems of animals. The central pattern generator of a nervous system has been shown to control motion patterns, such as walking, respiration and flapping. In this paper, a robot motion control system using a central pattern generator is proposed and applied to an amphibious multi-link mobile robot.

Development of neural oscillator based motion control system and applied to snake-like robot

Robot are expect to be new tools for the operations and observations in the extreme environments where human has difficulties for accessing directly, deep ocean, space, nuclear plants and so on. One of the important matter to realize mobile robots for extreme environments are to establish systems in tbe movement and their structures which are strong enough to disturbance and breakdowns. A solution to develop robust robot systems are to learn and imitate biological systems. For example, in the spinal cord of animals, neural oscillator systems called Central Pattern Generator (CPG) are proven to exist and investigated that CPG control rhythmical signals such as swimming pattern, walking locomotion, heart beats, etc. In this paper, neural oscillator based motion control system was developed using micro-computer PIC, and applied to motion control of snake-like robot. The robot can move forwards, backwards by changing weights and change direction by adjusting command parameters from the upper layer.

A Design Method of CPG Network Using Energy Efficiency to Control a Snake-Like Robot

As increasing the processor performance, new various applications of mobile robots are proposed and evaluated, e.g., the manipulations and observations in the extreme environments where human has difficulties for accessing directly, deep ocean, space, nuclear plants, rescue and service tasks to assist and collaborate human. One of the important issues to be solved is the adaptability to their working environments. In this paper, we propose an adaptive control system using Central Pattern Generator (CPG), which generates rhythmical signals to control periodical motion such as swimming pattern, walking locomotion, heartbeats. The adaptive control system has been developed to control motion of a snake-like robot., and evaluated by changing working environments.

Neural Oscillator Based Motion Control System for Snake-like Robot

Robustness in the motion and structure is an important issue for mobile robots in order to realize specially, practical robots in open fields. A robust system which is strong enough to disturbance and breakdowns should be introduced into the robot system. As a control algorithm for a robust control system, a bio-inspired system imitating a nervous network system is considered as a candidate for the issue. In spiral cord of animal, a rhythm generator mechanism called the Central Pattern Generator (CPG) exists. The CPG consists of many neural oscillators with inhibitory connection, and neural oscillators influence each other and produce make rhythmical patterns. In this paper, a neural oscillator based motion control system is developed and applied to the motion control of a snake-like robot. The robot can move forwards, backwards by changing weights and change the heading direction by adjusting command parameters from the upper layer.

Adaptative motion control system of a snake-like robot using a neural oscillator netowork

This paper presents a design method for a phase lag adjustment system using neural oscillators for a snake-like robot. The neural oscillator or central pattern generator (CPG), one of biomimetic behavior control system has attract attentions as we can see the similar rhythmical system in the natural creatures like fish swimming, bird flapping, etc, and these behaviors are considered to be optimized to their working environments. We found that the neural oscillator group had the possibility to adjust the robot behavior into working environment by using physical feedback signals from the working environment. The neural oscillator network adjusts their phase lag between neural oscillators to adapt their motor torques and trajectory to the working environment. In this paper, we present an phase lag auto-adjustment system using neural oscillator and its application into motion control of a snake-like robot, and the efficiency is evaluated through simulations and experiments using a 8-links snake robot.

Development of a Biomimetic Motion Control System Using CPG

It is very important for mobile robots to realize a robust system in the motion and the structure which is strong enough to disturbance and breakdowns. As one of method for development of robust robot system, there is an approach following architecture of nervous system. In spiral cord of animal rhythm generator mechanism called the Central Pattern Generator (CPG) exists. CPG consists of many neural oscillators with inhibitory connection. Neural oscillators influence each other, and can make rhythmical pattern. In this paper, neural oscillator based motion control system was developed using micro-computer PIC, and applied to motion control of snake-like robot. The robot can move forwards, backwards by changing weights and change direction by adjusting command parameters from the upper layer.

Tomato Harvesting Robot Competiton

Tomato is one of important fruit vegetables and most tomatoes are produced in the greenhouses, or large-scale farms, where the high temperature and humidity, and long harvest age force the farmers heavy works. With an aim to promote the automation of tomato harvesting, we have organized the tomato harvesting robot competition. In this paper, we report on the results of tomato harvesting robot competition.

The improving method of energy efficiency for a snake-like robot using neural oscillators

Robot are expected to be new tools for the operations and observations in the extreme environments where human has difficulties for accessing directly, deep ocean, space, nuclear plants and so on. One of the important matters to realize mobile robots for extreme environments are to establish systems which are strong enough to disturbance in the movement and their structures. A solution for realization an adaptive control system is to learn and imitate biological systems. For example, in the spinal cord of animals, neural oscillator systems called Central Pattern Generator (CPG) are proven to exist and investigated that CPG control rhythmical signals such as swimming pattern, walking locomotion, heart beats, etc. In this paper, an adaptive control system based on the entrainment feature of neural oscillators was developed and applied to motion control of a snake-like robot.

A VLSI Spiking Neural Network with Symmetric STDP and Associative Memory Operation

This paper proposes an analog CMOS VLSI circuit which implements integrate-and-fire spiking neural networks with spike-timing dependent synaptic plasticity (STDP). The designed VLSI chip includes 25 neurons and 600 synapse circuits with symmetric all-to-all connection STDP. Using the fabricated VLSI chip, we implement a Hopfield-type feedback network, and demonstrate its associative memory operation. In our chip, analog information is represented by the relative timing of spike firing events. Symmetric STDP provides an auto-correlation learning function depending on relative timing between spikes consisting of a learning pattern. Each learning and test pattern consists of 20 spike pulses each of which has a relative delay corresponding to a gray-scale pixel intensity. The chip has successfully associated from an input pattern the most similar learning pattern.

Biomimetic motion control system using CPG for a multi link mobile robot

Robots and robotics technologies are expected as new tools, especially in the extreme environments where are very dangerous to access directly by human beings such as underwater environments, volcanic areas, nuclear power plants, etc. Robots for such the extreme environments should be robust and strong enough to disturbance and breakdowns. We can find out a solution to realize robust robot system from interesting approaches in creatures. Animals adapt well to environmental changes in both short and long time periods. On nervous systems of animals, it becomes clear that motion patterns, such as walking, respiration, flap, etc, are controlled by rhythm generator mechanisms called the central pattern generator (CPG). In this paper, a robot motion control system using CPG is proposed and applied to a multi link mobile robot.