Adaptive Autonomous Navigation of Multiple Optoelectronic Microrobots in Dynamic Environments

被引:3
|
作者
Mennillo, Laurent [1 ]
Bendkowski, Christopher
Elsayed, Mohamed [2 ]
Edwards, Harrison [2 ]
Zhang, Shuailong [3 ,4 ]
Pawar, Vijay
Wheeler, Aaron R. [2 ]
Stoyanov, Danail [5 ]
Shaw, Michael [1 ,6 ]
机构
[1] UCL, London WC1E 6BT, England
[2] Univ Toronto, Toronto, ON M5S 3H6, Canada
[3] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[4] Beijing Inst Technol, Beijing Adv Innovat Ctr Intelligent Robots & Syst, Beijing 100081, Peoples R China
[5] UCL, Wellcome EPSRC Ctr Intervent & Surg Sci WEISS, London W1W 7TS, England
[6] Natl Phys Lab, Teddington TW11 0LW, Middx, England
来源
基金
加拿大自然科学与工程研究理事会; 英国经济与社会研究理事会;
关键词
Micro/Nano robots; multi-robot systems; autono- mous agents; visual servoing; motion and path planning; CALIBRATION;
D O I
10.1109/LRA.2022.3194308
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The optoelectronic microrobot is an advanced light-controlled micromanipulation technology which has particular promise for collecting and transporting sensitive microscopic objects such as biological cells. However, wider application of the technology is currently limited by a reliance on manual control and a lack of methods for simultaneous manipulation of multiple microrobotic actuators. In this letter, we present a computational framework for autonomous navigation of multiple optoelectronic microrobots in dynamic environments. Combining closed-loop visual-servoing, SLAM, real-time visual detection of microrobots and obstacles, dynamic path-finding and adaptive motion behaviors, this approach allows microrobots to avoid static and moving obstacles, and perform a range of tasks in real-world dynamic environments. The capabilities of the system are demonstrated through micromanipulation experiments in simulation and in real conditions using a custom built optoelectronic tweezer system.
引用
收藏
页码:11102 / 11109
页数:8
相关论文
共 50 条
  • [31] Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction
    Amalia F. Foka
    Panos E. Trahanias
    International Journal of Social Robotics, 2010, 2 : 79 - 94
  • [32] Tracking multiple moving objects in a dynamic environment for autonomous navigation
    Almeida, Jorge
    Araujo, Rui
    AMC '08: 10TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL, VOLS 1 AND 2, PROCEEDINGS, 2008, : 21 - 26
  • [33] Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO-LOAM and OpenPlanner algorithms
    Yao, Xingbo
    Bai, Yuhao
    Zhang, Baohua
    Xu, Dahua
    Cao, Guangzheng
    Bian, Yifan
    JOURNAL OF FIELD ROBOTICS, 2024, 41 (07) : 2427 - 2440
  • [34] Autonomous navigation using an adaptive hierarchy of multiple fuzzy-behaviors
    Tunstel, E
    Danny, H
    Lippincott, T
    Jamshidi, M
    1997 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION - CIRA '97, PROCEEDINGS: TOWARDS NEW COMPUTATIONAL PRINCIPLES FOR ROBOTICS AND AUTOMATION, 1997, : 276 - 281
  • [35] Adaptive navigation for autonomous robots
    Knudson, Matt
    Tumer, Kagan
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2011, 59 (06) : 410 - 420
  • [36] Intervention Force-based Imitation Learning for Autonomous Navigation in Dynamic Environments
    Yokoyama, Tomoya
    Seiya, Shunya
    Takeuchi, Eijiro
    Takeda, Kazuya
    2020 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2020, : 1679 - 1688
  • [37] A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments
    Zhou, Zhiqian
    Ren, Junkai
    Zeng, Zhiwen
    Xiao, Junhao
    Zhang, Xinglong
    Guo, Xian
    Zhou, Zongtan
    Lu, Huimin
    CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2023,
  • [38] Deep Reinforcement Learning-Based Collision-Free Navigation for Magnetic Helical Microrobots in Dynamic Environments
    Wang, Huaping
    Qiu, Yukang
    Hou, Yaozhen
    Shi, Qing
    Huang, Hen-Wei
    Huang, Qiang
    Fukuda, Toshio
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2024,
  • [39] Evolving Autonomous Navigation: A NEAT Approach for Firefighting Rover Operations in Dynamic Environments
    Shrestha, D.
    Valles, D.
    2024 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY, EIT 2024, 2024, : 247 - 255
  • [40] RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments
    Mackay, Andrew K.
    Riazuelo, Luis
    Montano, Luis
    SENSORS, 2022, 22 (10)