The 14th International Conference on Electronics, Communications and Networks (CECNet 2024)

Abstract: What we like to present is two points. One is the automatic teaching point generation for an industrial robot considering undesirable misalignment between robot and work coordinate systems. When a peg-in-hole task with little clearance is tried to be automated by an industrial robot, there is a problem due to the misalignment between robot and work coordinate systems. Such a misalignment, for example, sometimes occurs caused by over and under tightening of screws and bolts used to fix a robot and jigs on a working table, and tends to cause serious troubles such as breakage of workpieces and end-effectors. To cope with the problem, we have developed an application software on Python that automatically generates compensated teaching points for picking and placing only by giving the four corner positions on a working table.
The other is the defective workpiece sorting robot incorporated with a Convolutional Neural Network (CNN) model. Generally, the functionality of the standard teaching interface provided by a robot maker seems to be limited to only the playback-type position control. Also, the extension of the functionality tends to be not easy for users and require much cost. To support the enhancement of the functionality, we have already proposed the Hyper Cutter Location Source (HCLS) data-based robotic interface. The HCLS data interface allows users to build a defective workpiece sorting system by multiple industrial robots incorporated with a CNN model for defect detection. The robots can collaboratively handle a single camera while targeting the sorting of industrial workpieces provided by a manufacturer.
The effectiveness and usefulness of the proposed system implementing the introduced two functions are demonstrated through cooperative peg-in-hole tasks using two small-sized industrial robots MG400s as shown in Fig. 1.

Fig. 1 Experimental setup for peg-in-hole task using two robots MG400s.

Abstract: Real samples contain abundant chemical species playing crucial roles in environmental assessments, food analysis, and diagnosis fields. Conventionally, large-sized analytical instruments have been widely applied to real-sample analysis owing to their accuracy. However, the applicability of such a well-established instrumental approach is still a concern in on-site analysis because of the complicated detection principle that requires trained personnel and time-consuming operation. Herein, the presenter introduces an approach for the development of chemical sensor devices based on organic field-effect transistors (OFETs) (Figure 1). OFETs are electronic devices showing switching characteristics by applying voltage. Owing to their beneficial device properties, OFETs functionalized with appropriate molecular recognition materials contribute to sensitive detection over conventional electrochemical sensing methods. Biological materials such as enzymes and antibodies have been employed owing to their favorable specificities to analytes based on the lock-and-key recognition principle. However, detectable analyte structures are limited by a library of these biological materials. Therefore, synthetic receptors based on molecular recognition chemistry are promising approaches in the design of recognition sites. In this study, molecularly imprinted polymers (MIPs) were applied to molecular recognition materials for selective detection. MIPs provide three-dimensional recognition networks against specific analytes because a pre-organized structure made of a template (i.e., analyte) and functional monomers can be optimized by quantum chemical calculation methods. Such optimized MIP structures contribute to selective detection even in the presence of interferents. In contrast, the inherent cross-reactivity of supramolecular receptors can be applied to simultaneous detection by using pattern recognition methods. This presentation will discuss the usability of this approach for the realization of OFET-based chemical sensor devices based on fusion technologies of organic electronics, molecular recognition chemistry, and polymer chemistry.

Figure 1. Conceptual figure of an OFET-based chemical sensor functionalized with various molecular recognition materials.