Automated transport of multiple particles using optical tweezers requires the use of motion planning to move them simultaneously while avoiding collisions amongst themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized stochastic dynamic programming based motion planning framework by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. An iterative version of a maximum bipartite graph matching algorithm is used to assign given goal locations to such particles. The algorithm for individual particle transport is validated using silica beads in a holographic tweezer set-up. Once the individual plans are computed, a three-step method consisting of clustering, classification, and branch and bound optimization is employed to determine the final collision-free paths. Simulation results in the form of sample trajectories and performance characterization plots are presented to illustrate the usefulness of the developed approach.
- Design Engineering Division and Computers in Engineering Division
A Decoupled and Prioritized Stochastic Dynamic Programming Approach for Automated Transport of Multiple Particles Using Optical Tweezers
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Banerjee, AG, Losert, W, & Gupta, SK. "A Decoupled and Prioritized Stochastic Dynamic Programming Approach for Automated Transport of Multiple Particles Using Optical Tweezers." Proceedings of the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 6: ASME Power Transmission and Gearing Conference; 3rd International Conference on Micro- and Nanosystems; 11th International Conference on Advanced Vehicle and Tire Technologies. San Diego, California, USA. August 30–September 2, 2009. pp. 785-796. ASME. https://doi.org/10.1115/DETC2009-87113
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