Developing a Stochastic Dynamic Programming Framework for Optical Tweezer-Based Automated Particle Transport Operations

Abstract

. Developing a stochastic dynamic programming framework for optical tweezers based automated particle transport operations. IEEE Transactions on Automation Science and Engineering, 7(2), 218 -227, 2010. Readers are encouraged to get the official version from the journal's web site or by contacting Dr. S.K. Gupta (skgupta@umd.edu). Ashis Gopal Banerjee, Student Member, IEEE, Andrew Pomerance, Wolfgang Losert, and Satyandra K. GuptaAbstract-Automated particle transport using optical tweezers requires the use of motion planning to move the particle while avoiding collisions with randomly moving obstacles. This paper describes a stochastic dynamic programming based motion planning framework developed by modifying the discrete version of an infinite-horizon partially observable Markov decision process algorithm. Sample trajectories generated by this algorithm are presented to highlight effectiveness in crowded scenes and flexibility. The algorithm is tested using silica beads in a holographic tweezer set-up and data obtained from the physical experiments are reported to validate various aspects of the planning simulation framework. This framework is then used to evaluate the performance of the algorithm under a variety of operating conditions.Note to Practitioners-Micro and nano scale component-based devices are revolutionizing health care, energy, communication, and computing industry. Components need to be assembled together to create useful devices. Such assembly operations remain challenging in spite of the advancements in imaging, measurement, and fabrication at the small scales. This paper deals with directed assembly using optical fields that is useful for prototyping new design concepts, repairing devices, and creating templates for self-assembly.

Real-Time Path Planning for Coordinated Transport of Multiple Particles Using Optical Tweezers

“…The physics of the problem is modeled as described in [31]. If multiple traps are switched on, then the overall laser intensity is shared among all the traps, and consequently the maximum trap speed (v max ) is reduced proportionately.…”

“…This Q function is then used to select the control action that yields the minimum expected value. The details of this approach are presented in [31].…”

“…In this paper, we develop a prioritized and decoupled framework for coordinated transport of multiple particles based on our decision-theoretic single particle path planning algorithm discussed in [31]. It is useful to first formulate the problem mathematically before describing the framework.…”

Automated Manipulation of Biological Cells Using Gripper Formations Controlled By Optical Tweezers

“…In order to automatically transport optically-trapped objects to a desired location, Banerjee et al [37,38] modeled the OT environment as a partially observable scene to account for its dynamics. The planning problem was solved using Stochastic Dynamic Programming (SDP).…”

Optical Tweezers: Autonomous Robots for the Manipulation of Biological Cells

“…Over the past decade or so, researchers have started using OT as an autonomous microrobot [6], [7], [8], [9], [10], [11]. Like any conventional robot, an OT system comprises of an actuator, sensor, and controller.…”