paper-with-me

Papers

Autonomous object harvesting using synchronized optoelectronic microrobots

2021-03-08 · Christopher Bendkowski, Laurent Mennillo, Tao Xu, Mohamed Elsayed, Filip Stojic, Harrison Edwards, Shuailong Zhang, Cindi Morshead, Vijay Pawar, Aaron R. Wheeler, Danail Stoyanov, Michael Shaw

Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate. The microrobots in turn can be used to exert forces on secondary objects and carry out a wide range of micromanipulation operations, including collecting, transporting and depositing microscopic cargos. In contrast to alternative (direct) micromanipulation techniques, OETdMs are relatively gentle, making them particularly well suited to interacting with sensitive objects such as biological cells. However, at present such systems are used exclusively under manual control by a human operator. This limits the capacity for simultaneous control of multiple microrobots, reducing both experimental throughput and the possibility of cooperative multi-robot operations. In this article, we describe an approach to automated targeting and path planning to enable open-loop control of multiple microrobots. We demonstrate the performance of the method in practice, using microrobots to simultaneously collect, transport and deposit silica microspheres. Using computational simulations based on real microscopic image data, we investigate the capacity of microrobots to collect target cells from within a dissociated tissue culture. Our results indicate the feasibility of using OETdMs to autonomously carry out micromanipulation tasks within complex, unstructured environments.

📄 PDF Abstract BibTeX arXiv:2103.04912

Code (0)

등록된 구현이 없습니다.

Tasks

Cultural Vocal Bursts Intensity PredictionObject

Similar Papers 제목 키워드 기반

Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning

2022-01-14 · Michael R. Behrens, Warren C. Ruder

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of …

Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

Real-time windrow detection from onboard tractor sensors for automated following

2026-04-27 · Lorenz Gunreben, Nico Heider, Sebastian Zürner, Martin Schieck 외 arxiv

Proprietary design in commercial windrow-detection systems restricts transparency and limits progress in open autonomous forage-harvesting research. We present a multi-modal dataset combining stereo vision and LiDAR from…

Contrast-Free Autonomous Navigation of Untethered Endovascular Microrobots Using Single-Plane Fluoroscopy

2026-08-31 · Husnu Halid Alabay, Tuan-Anh Le, Ping Wang, Hakan Ceylan arxiv

Reliable three-dimensional (3D) navigation of magnetically actuated untethered microrobots remains a major barrier to clinical translation. X-ray fluoroscopy is the standard real-time imaging modality for endovascular pr…

Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning

2019-03-05 · Jonathon Sather, Xiaozheng Jane Zhang

Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States's strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and comme…

Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

Programmable Control of Ultrasound Swarmbots through Reinforcement Learning

2022-09-30 · Matthijs Schrage, Mahmoud Medany, Daniel Ahmed

Powered by acoustics, existing therapeutic and diagnostic procedures will become less invasive and new methods will become available that have never been available before. Acoustically driven microrobot navigation based …

DiagnosticNavigatereinforcement-learningReinforcement Learning+1