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PROJECT TITLE :

Accuracy Quantification and Improvement of Serial Micropositioning Robots for In-Plane Motions

ABSTRACT:

High positioning accuracy with micropositioning robots (MPRs) is required to successfully perform several complex tasks, like microassembly, manipulation, and characterization of biological tissues and minimally invasive inspection and surgery. Despite the widespread use of high-resolution micro- and nanopositioning robots, there is terribly very little data concerning the real positioning accuracy that may be obtained and what the main influential factors are. Indeed, terribly few notable strategies are out there to measure multi-degree-of-freedom motions with tailored vary, resolution, and dynamic capabilities. The most objective of this paper is to quantify the positioning accuracy of serial MPRs and to identify the most influential factors (a typical $XY$ $Theta$ serial robot is chosen as a case study). To reach this goal, a measuring system that mixes vision and pseudoperiodic patterns with an extraordinarily massive range-to-resolution ratio is introduced as a new manner to quantify the positioning accuracy of MPRs for in-plane motions. Then, an open-loop control approach primarily based on MPR calibration is chosen for many reasons: the utilization of various models to identify influential factors, the quantification of the positioning accuracy, and the need of the strategy when sensor integration is just too advanced. Experiments using five different calibration models were conducted to classify factors influencing the positioning accuracy of MPRs. The results show that positioning accuracy can be improved by a lot of than thirty five times from 96 $mu textm$ with no imperfection compensation to a pair of.five $mu textm$ by compensating for geometric, position-dependent, and angle-dependent errors through the MPR calibration approach.


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