Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning  

作　　者：Mochammad Ariyanto Chowdhury Mohammad Masum Refat Kazuyoshi Hirao Keisuke Morishima 

机构地区：[1]Department of Mechanical Engineering,Graduate School of Engineering,Osaka University,Suita 565-0871,Japan [2]Department of Mechanical Engineering,Faculty of Engineering,Diponegoro University,Semarang,50275,Indonesia

出　　处：《Cyborg and Bionic Systems》2023年第1期427-440,共14页类生命系统（英文）

基　　金：supported by JSPS KAKENHI grant number 21K18700;JST(Moonshot R&D)(grant number JPMJMS223A)。

摘　　要：Cockroaches can traverse unknown obstacle-terrain,self-right on the ground and climb above the obstacle.However,they have limited motion,such as less activity in light/bright areas and lower temperatures.Therefore,the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect.This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning.Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals.Ten time-domain features were chosen and applied as the classifier inputs.The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach.A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision,data acquisition,feature extraction,automatic stimulation,and machine learning using a multithreading algorithm.On the basis of the experiment results,we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation.This system increased the search rate and traveled distance by 68%and 70%,respectively,while the stop time was reduced by 78%.

关 键 词：computer CLASSIFIER OBSTACLE 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]