{"title":"基于光诱导介质电泳的粒子自动输运动态控制框架","authors":"Jiaxin Liu, Huaping Wang, Qing Shi, Xinyi Dong, Kaijun Lin, Tao Sun, Qiang Huang, Toshio Fukuda","doi":"10.1109/RCAR54675.2022.9872252","DOIUrl":null,"url":null,"abstract":"As a high-throughput and highly flexible technique, optically induced dielectrophoresis (ODEP) is one of the most promising micromanipulation techniques applied for biomedical studies. However, most ODEP-based manipulation methods have not been explored deeply in terms of accurate control under unstructured environments with multiple interference. This paper reports a dynamic control framework for automatically transporting single particle to goal position in a complex environment with an optically induced dielectrophoresis platform. The POMDP-based path planner periodically provides the optimal motion strategy based on the real-time environmental information and current position of the particle to avoid collisions with randomly moving obstacles. The optimal motion strategies are smoothly expanded to short-distance trajectories, which are dynamically followed by the target particle with proxy-based sliding mode control (PSMC) closed-loop controller. Experimental results indicated that compared with traditional controllers such as PID, our control method possesses higher accuracy and stability in path following. In addition, the performance of the path planner was demonstrated by transporting a NIH/3T3 cell to the desired position within a relatively crowded environment.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic Control Framework for Automated Particle Transport Based on Optically Induced Dielectrophoresis\",\"authors\":\"Jiaxin Liu, Huaping Wang, Qing Shi, Xinyi Dong, Kaijun Lin, Tao Sun, Qiang Huang, Toshio Fukuda\",\"doi\":\"10.1109/RCAR54675.2022.9872252\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a high-throughput and highly flexible technique, optically induced dielectrophoresis (ODEP) is one of the most promising micromanipulation techniques applied for biomedical studies. However, most ODEP-based manipulation methods have not been explored deeply in terms of accurate control under unstructured environments with multiple interference. This paper reports a dynamic control framework for automatically transporting single particle to goal position in a complex environment with an optically induced dielectrophoresis platform. The POMDP-based path planner periodically provides the optimal motion strategy based on the real-time environmental information and current position of the particle to avoid collisions with randomly moving obstacles. The optimal motion strategies are smoothly expanded to short-distance trajectories, which are dynamically followed by the target particle with proxy-based sliding mode control (PSMC) closed-loop controller. Experimental results indicated that compared with traditional controllers such as PID, our control method possesses higher accuracy and stability in path following. In addition, the performance of the path planner was demonstrated by transporting a NIH/3T3 cell to the desired position within a relatively crowded environment.\",\"PeriodicalId\":304963,\"journal\":{\"name\":\"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RCAR54675.2022.9872252\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RCAR54675.2022.9872252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic Control Framework for Automated Particle Transport Based on Optically Induced Dielectrophoresis
As a high-throughput and highly flexible technique, optically induced dielectrophoresis (ODEP) is one of the most promising micromanipulation techniques applied for biomedical studies. However, most ODEP-based manipulation methods have not been explored deeply in terms of accurate control under unstructured environments with multiple interference. This paper reports a dynamic control framework for automatically transporting single particle to goal position in a complex environment with an optically induced dielectrophoresis platform. The POMDP-based path planner periodically provides the optimal motion strategy based on the real-time environmental information and current position of the particle to avoid collisions with randomly moving obstacles. The optimal motion strategies are smoothly expanded to short-distance trajectories, which are dynamically followed by the target particle with proxy-based sliding mode control (PSMC) closed-loop controller. Experimental results indicated that compared with traditional controllers such as PID, our control method possesses higher accuracy and stability in path following. In addition, the performance of the path planner was demonstrated by transporting a NIH/3T3 cell to the desired position within a relatively crowded environment.