In robotic micromanipulation, end-effector tips must be first located under microscopy imaging before manipulation is performed. The tip of micromanipulation tools is typically a few micrometers in size and highly delicate. In all existing micromanipulation systems, the process of locating the end-effector tip is conducted by a skilled operator, and the automation of this task has not been attempted. This paper presents a technique to automatically locate end-effector tips. The technique consists of programmed sweeping patterns, motion history image end-effector detection, active contour to estimate end-effector positions, autofocusing and quad-tree search to locate an end-effector tip, and, finally, visual servoing to position the tip to the center of the field of view. Two types of micromanipulation tools (micropipette that represents single-ended tools and microgripper that represents multiended tools) were used in experiments for testing. Quantitative results are reported in the speed and success rate of the autolocating technique, based on over 500 trials. Furthermore, the effect of factors such as imaging mode and image processing parameter selections was also quantitatively discussed. Guidelines are provided for the implementation of the technique in order to achieve high efficiency and success rates.

Locating end-effector tips is a prerequisite step in micromanipulation. The tip of micromanipulation tools is typically a few micrometers in size and highly delicate. In all existing automated micromanipulation systems, the process of locating the end-effector tip is conducted by a skilled operator, and the automation of this task has not been attempted. This paper presents a technique for automatically locating end-effector tips. The technique consists of programmed sweeping patterns, MHI (motion history image) end-effector detection, active contour for estimating end-effector positions, autofocusing and quad-tree search for locating end-effector tip, and finally visual servoing to position the tip to the center of the field of view. Two types of micromanipulation tools (micropipette representing single-ended tools and microgripper representing multi-ended tools) were used in experiments for testing. Quantitative results were reported in the speed and success rate of the auto-locating technique, based on over 500 trials. Furthermore, the effect of factors such as imaging mode and image processing parameter selections was also quantitatively discussed. Guidelines are provided for the implementation of the technique in order to achieve high efficiency and success rates.