NOTICE: DRUM will be down for scheduled maintenance on Tuesday, 23 May 2017, from 5:00 AM to 8:00 AM EDT.
Real-Time Path Planning for Automating Optical Tweezers based Particle Transport Operations
Banerjee, Ashis Gopal
Gupta, Satyandra K
MetadataShow full item record
Optical tweezers (OT) have been developed to successfully trap, orient, and transport micro and nano scale components of many different sizes and shapes in a fluid medium. They can be viewed as robots made out of light. Components can be simply released from optical traps by switching off laser beams. By utilizing the principle of time sharing or holograms, multiple optical traps can perform several operations in parallel. These characteristics make optical tweezers a very promising technology for creating directed micro and nano scale assemblies. In the infra-red regime, they are useful in a large number of biological applications as well. This dissertation explores the problem of real-time path planning for autonomous OT based transport operations. Such operations pose interesting challenges as the environment is uncertain and dynamic due to the random Brownian motion of the particles and noise in the imaging based measurements. Silica microspheres having diameters between (1-20) µm are selected as model components. Offline simulations are performed to gather trapping probability data that serves as a measure of trap strength and reliability as a function of relative position of the particle under consideration with respect to the trap focus, and trap velocity. Simplified models are generated using Gaussian Radial Basis Functions to represent the data in a compact form. These metamodels can be queried at run-time to obtain estimated probability values accurately and efficiently. Simple trapping probability models are then utilized in a stochastic dynamic programming framework to compute optimum trap locations and velocities that minimizes the total, expected transport time by incorporating collision avoidance and recovery steps. A discrete version of an approximate partially observable Markov decision process algorithm, called the QMDP_NLTDV algorithm, is developed. Real-time performance is ensured by pruning the search space and enhancing convergence rates by introducing a non-linear value function. The algorithm is validated both using a simulator as well as a physical holographic tweezer set-up. Successful runs show that the automated planner is flexible, works well in reasonably crowded scenes, and is capable of transporting a specific particle to a given goal location by avoiding collisions either by circumventing or by trapping other freely diffusing particles. This technique for transporting individual particles is utilized within a decoupled and prioritized approach to move multiple particles simultaneously. An iterative version of a bipartite graph matching algorithm is also used to assign goal locations to target objects optimally. As in the case of single particle transport, simulation and some physical experiments are performed to validate the multi-particle planning approach.