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

Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers. Note to Practitioners-Manipulation of biological and biomimetic objects are revolutionizing the health care and communication industry, and achieving fundamental scientific breakthroughs. For example, cell sorting in optical tweezers-assisted fluidic chambers is aiding tumor immunology and chemotherapy, multicellular arrangements are being used to study intercell signaling, drug transport and binding, and artificial biomimetic machines like rotary DNA actuators and viral linear rotary motors are being assembled to form electronic switches. This paper provides a step toward automating such manipulation operations by developing an intelligent planning framework that is geared for the unique characteristics of the microscopic environment and is capable of transporting specific particles to desired goal locations concurrently, which can then be used as handles to push, orient, or deform the objects of interest.