{"title":"基于细菌的生物混合微型机器人的混合集中/分散控制","authors":"Eric J. Leaman, Brian Geuther, B. Behkam","doi":"10.1109/MARSS.2018.8481144","DOIUrl":null,"url":null,"abstract":"Engineering microrobotic systems using a bio-hybrid approach that couples synthetic materials with live cells is a powerful approach to address some of the challenges in micro/nanotechnology such as providing an on-board power source and efficient means of locomotion. In the last decade, a number of centralized control strategies dependent on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has not been shown before. In this work, we impart bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of bacteria-based Biohybrid microrobots. We show a hybrid control strategy wherein a centralized control scheme is used to direct migration and a decentralized control scheme enables the agents to independently coordinate a desired behavior (fluorescent protein expression). We use an experimentally-validated agent-based computational model of the system to demonstrate the utility of the approach. We show that spatial organization plays a significant role in the response dynamics and explore how the system could be tuned for particular applications. The model will serve as an essential tool for predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.","PeriodicalId":118389,"journal":{"name":"2018 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Hybrid Centralized/Decentralized Control of Bacteria-Based Bio-Hybrid Microrobots\",\"authors\":\"Eric J. Leaman, Brian Geuther, B. Behkam\",\"doi\":\"10.1109/MARSS.2018.8481144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Engineering microrobotic systems using a bio-hybrid approach that couples synthetic materials with live cells is a powerful approach to address some of the challenges in micro/nanotechnology such as providing an on-board power source and efficient means of locomotion. In the last decade, a number of centralized control strategies dependent on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has not been shown before. In this work, we impart bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of bacteria-based Biohybrid microrobots. We show a hybrid control strategy wherein a centralized control scheme is used to direct migration and a decentralized control scheme enables the agents to independently coordinate a desired behavior (fluorescent protein expression). We use an experimentally-validated agent-based computational model of the system to demonstrate the utility of the approach. We show that spatial organization plays a significant role in the response dynamics and explore how the system could be tuned for particular applications. The model will serve as an essential tool for predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.\",\"PeriodicalId\":118389,\"journal\":{\"name\":\"2018 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MARSS.2018.8481144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MARSS.2018.8481144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid Centralized/Decentralized Control of Bacteria-Based Bio-Hybrid Microrobots
Engineering microrobotic systems using a bio-hybrid approach that couples synthetic materials with live cells is a powerful approach to address some of the challenges in micro/nanotechnology such as providing an on-board power source and efficient means of locomotion. In the last decade, a number of centralized control strategies dependent on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has not been shown before. In this work, we impart bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of bacteria-based Biohybrid microrobots. We show a hybrid control strategy wherein a centralized control scheme is used to direct migration and a decentralized control scheme enables the agents to independently coordinate a desired behavior (fluorescent protein expression). We use an experimentally-validated agent-based computational model of the system to demonstrate the utility of the approach. We show that spatial organization plays a significant role in the response dynamics and explore how the system could be tuned for particular applications. The model will serve as an essential tool for predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.