The Brownian motion of micro-objects in fluid mediums is a fundamental distinction between optical manipulation and robotic manipulation in the macro-world. Besides, current control techniques for optical manipulation generally assume that the manipulated micro-objects are initially trapped prior to the manipulation processes. This letter proposes a robotic control technique for fully automated optical trapping and manipulation of multiple micro-objects with stochastic perturbations. Cooperative control of robotic stage and optical traps is performed to achieve the control objective, in which multiple micro-objects are trapped in sequence by using the robotic stage, and the trapped micro-objects are then manipulated toward a desired region by using laser-steering system. The transition from the trapping operation to manipulation of the trapped micro-objects is fully automated. In this letter, a closed-loop control approach of the optical traps is formulated, and thus ensuring the completeness of the manipulation tasks. The stability of the control system is investigated from a stochastic perspective, and the performance of the proposed control technique is illustrated with experimental results.
Showing posts with label IEEE Robotics and Automation Letters. Show all posts
Showing posts with label IEEE Robotics and Automation Letters. Show all posts
Friday, March 13, 2020
Coordinated Optical Tweezing and Manipulation of Multiple Microscopic Objects With Stochastic Perturbations
Quang Minh Ta; Chien Chern Cheah
The Brownian motion of micro-objects in fluid mediums is a fundamental distinction between optical manipulation and robotic manipulation in the macro-world. Besides, current control techniques for optical manipulation generally assume that the manipulated micro-objects are initially trapped prior to the manipulation processes. This letter proposes a robotic control technique for fully automated optical trapping and manipulation of multiple micro-objects with stochastic perturbations. Cooperative control of robotic stage and optical traps is performed to achieve the control objective, in which multiple micro-objects are trapped in sequence by using the robotic stage, and the trapped micro-objects are then manipulated toward a desired region by using laser-steering system. The transition from the trapping operation to manipulation of the trapped micro-objects is fully automated. In this letter, a closed-loop control approach of the optical traps is formulated, and thus ensuring the completeness of the manipulation tasks. The stability of the control system is investigated from a stochastic perspective, and the performance of the proposed control technique is illustrated with experimental results.
The Brownian motion of micro-objects in fluid mediums is a fundamental distinction between optical manipulation and robotic manipulation in the macro-world. Besides, current control techniques for optical manipulation generally assume that the manipulated micro-objects are initially trapped prior to the manipulation processes. This letter proposes a robotic control technique for fully automated optical trapping and manipulation of multiple micro-objects with stochastic perturbations. Cooperative control of robotic stage and optical traps is performed to achieve the control objective, in which multiple micro-objects are trapped in sequence by using the robotic stage, and the trapped micro-objects are then manipulated toward a desired region by using laser-steering system. The transition from the trapping operation to manipulation of the trapped micro-objects is fully automated. In this letter, a closed-loop control approach of the optical traps is formulated, and thus ensuring the completeness of the manipulation tasks. The stability of the control system is investigated from a stochastic perspective, and the performance of the proposed control technique is illustrated with experimental results.
Wednesday, November 13, 2019
Three-Dimensional Pose Estimation of Optically Transparent Microrobots
Maria Grammatikopoulou, Guang-Zhong Yang
The use of microrobots for cell manipulation has a range of applications in biomedical research. Direct sensing of microrobot three-dimensional (3D) position and orientation, however, is practically challenging due to the small scale involved. This letter proposes a vision-based method for estimating the 3D pose of optically transparent microrobots based on Optical Tweezers (OT) manipulation by using Convolutional Neural Networks (CNNs). A model-based approach is used to generate the large training set required for CNNs. Detailed validation is performed to demonstrate the experimental use of the technique.
DOI
The use of microrobots for cell manipulation has a range of applications in biomedical research. Direct sensing of microrobot three-dimensional (3D) position and orientation, however, is practically challenging due to the small scale involved. This letter proposes a vision-based method for estimating the 3D pose of optically transparent microrobots based on Optical Tweezers (OT) manipulation by using Convolutional Neural Networks (CNNs). A model-based approach is used to generate the large training set required for CNNs. Detailed validation is performed to demonstrate the experimental use of the technique.
DOI
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