Abstract-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 nanoscale 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. Index Terms-Automated planning, microsphere, optical tweezer (OT), partially observable Markov decision process, simulation, stochastic dynamic programming.
Simulations of the optical traps play an important role in utilizing optical tweezers as assembly tools at the micro and nanoscale REF .
Developing a Stochastic Dynamic Programming Framework for Optical Tweezer-Based Automated Particle Transport Operations
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering
Computer Science