Geometric Computing Challenges in Micro and Nano Manipulation Using Optical Tweezers

Presenter: S.K. Gupta, University of Maryland at College Park

Collaborators: Tao Peng, University of Maryland at College Park
Tom LeBrun and Arvind Balijepalli, National Institute for Standards and Technology   

Our group is currently investigating the use of laser-based optical tweezers to perform nano and micro manipulation. Our plan is use optical tweezers to perform autonomous assembly. Basically, we are trying to trap very small components using lasers and move them into certain geometric configurations autonomously. The eventual goal is to use this method to prototype nanoscale electronic and photonic components. This method can also be used to study cell and drug interactions.

People have successfully manually operated optical tweezers to manipulate a wide variety of objects. Manual operation requires considerable expertise in running optical tweezers as well as limits the complexity of the device that can be successfully assembled. Therefore, our main emphasis is to achieve a very high degree of autonomy in manipulation tasks. The basic idea being that the human operator defines the tasks and the system actually performs in autonomous manner. In our pursuit of autonomous assembly, we have identified a number of challenges in the areas of shape modeling and representation that need to be addressed.

Assembly manipulation tasks are performed in a fluidic medium and due to the Brownian motion everything constantly moves. Therefore, to achieve autonomy we need to first build an on-line monitoring system that can construct and update the 3D workspace at least at video rate (we would like to achieve speeds of 50Hz).  This on-line monitoring system will need to (1) construct 3D shapes of components, (2) identify them, and (3) track their 3D locations and positions of components in the workspace. Optical section microscopy is a promising technique for accomplishing this. It may not be possible to image the entire cross section of the workspace simultaneously due to the limitations of the camera resolution. Therefore, there are many challenging questions that need to be answered. What is the optimal way to image the workspace?  How to use out of focus images to recover depth using an optical model? How to calibrate the imaging system? How to process information so that we achieve updates at 50Hz? How to perform recognition of highly compliant and translucent structures?

Due to constant motion of components, inherent compliance of components (e.g., cells, viruses), limited resolution of imaging techniques, and optical effects observed at small scale, the model of the workspace constructed by 3D imaging system contains significant uncertainties. We will need to figure out how to model uncertainties in shapes and locations. Constructing a very detailed and accurate model of uncertainties may be very time consuming. On the other hand a highly simplified model of uncertainties may prove to be of very little use in autonomous path planning.  Therefore, we will need to figure out the appropriate models of uncertainties. We will also need to model compliance in shapes of components to support geometric reasoning. A very detailed mechanics-based model of the shape compliance will not be suitable for real-time path planning. Therefore, we will need to develop reduced order models to represent compliance.  We will also need to develop models that can determine the error bounds on these reduced order models.

Components continuously move in the workspace. Therefore, collisions are unavoidable. Therefore, it is highly likely that during the manipulation task, the manipulator (i.e., optical tweezers) will loose the component. Therefore, path planning will have to include recovery strategies as a part of the basic planning. The trapping laser can also be time shared to move multiple components. Hence the laser can also be used to move components that are in the way of the target component and hence clear the path. The physics of trapping imposes constraints on the speed at which the laser can move a trapped particle through the space.  Moreover, there are also constraints on the shape of the trap and clearance that need to be maintained between the trap and the other components in the workspace. In order to perform planning, we will need to identify and model relevant constraints in a geometric framework.  This will require us to reformulate path-planning problem with the suitable modifications to goals and constraints. The new problem formulation may require new geometric algorithms as well.