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Enhancing physical human-robot interaction requires the improvement in the tactile perception of physical touch. Robot skin sensors exhibiting piezoresistive behavior can be used in conjunction with collaborative robots. In past work, fabrication of these tactile arrays was done using cleanroom techniques such as spin coating, photolithography, sputtering, wet and dry etching onto flexible polymers. In this paper, we present an addictive, non-cleanroom improved process of depositing PEDOT: PSS, which is the organic polymer responsible for the piezoresistive phenomenon of the robot skin sensor arrays. This publication details the patterning of the robot skin sensor structures and the adaptation of the inkjet printing technology to the fabrication process. This increases the possibility of scaling the production output while reducing the cleanroom fabrication cost and time from an approximately five-hour PEDOT: PSS deposition process to five minutes. Furthermore, the testing of these skin sensor arrays is carried out on a testing station equipped with a force plunger and an integrated circuit designed to provide perception feedback on various force load profiles controlled in an automated process. The results show uniform deposition of the PEDOT: PSS, consistent resistance measurement, and appropriate tactile response across an array of 16 sensors.

 

Advanced applications for human-robot interaction require perception of physical touch in a manner that imitates the human tactile perception. Feedback generated from tactile sensor arrays can be used to control the interaction of a robot with their environment and other humans. In this paper, we present our efforts to fabricate piezoresistive organic polymer sensor arrays using PEDOT: PSS or poly (3,4-ethylenedioxythiophene)-poly(styrenesulfonate). Sensors are realized as strain-gauges on Kapton substrates with thermal and electrical response characteristics to human touch. In this paper, we detail fabrication processes associated with a Gold etching technique combined with a wet lift-off photolithographic process to implement a circular tree designed sensor microstructure in our cleanroom. The testing of this microstructure is done on a load testing apparatus facilitated by an integrated circuit design. Furthermore, a lamination process is employed to compensate for temperature drift while measuring pressure for double-sided sensor substrates. Experiments carried out to evaluate the performance of the fabricated structure, indicates 100% sensor yields with the updated technique implemented. 

The sAFAM is a novel mm-size microrobot built using MicroElectroMechanical Systems (MEMS) technology. It consists of a monolithically fabricated microrobotic arm assembled onto four in-plane actuators, capable of moving along four degrees of freedom, including translational movement in X and Y axes as well as pitch and yaw. In this paper, several design modifications were proposed to increase movement precision, stability, and controllability to the sAFAM tip. An interface is developed to assist a human operator accurately position the microrobot tip during nano-object handling. A Python-based graphical user interface (GUI) was programmed to make it intuitive for an operator to use and obtain required tip precision under a microscope. Experimental results demonstrate the functionality of the proposed control solution, and the tip motion resolution using microscope images of the microrobot tip under 20x magnification during operation. The hardware and software requirements for the proposed experimental setup and control platform are discussed in detail. 

In this paper, we propose a method for tracking a microrobot’s three-dimensional position using microscope machine vision. The microrobot, theSolid Articulated Four Axis Microrobot (sAFAM), is being developed to enable the assembly and manipulation of micro and nanoscale objects. In the future, arrays of sAFAMS working together can be integrated into a wafer-scale nanofactory, Prior to use, microrobots in this microfactory need calibration, which can be achieved using the proposed measurement technique. Our approach enables faster and more accurate mapping of microrobot translations and rotations, and orders of magnitude larger datasets can be created by automation. Cameras feeds on a custom microscopy system is fed into a data processing pipeline that enables tracking of the microrobot in real-time. This particular machine vision method was implemented with a help of OpenCV and Python and can be used to track the movement of other micrometer-sized features. Additionally, a script was created to enable automated repeatability tests for each of the six trajectories traversable by the robot. A more precise microrobot workable area was also determined thanks to the significantly larger datasets enabled by the combined automation and machine vision approaches. Keywords: Micro robotics, machine vision, nano microscale manufacturing.