Fuzzy logic path tracking control for autonomous non-holonomic mobile robots: Design of System on a Chip

This paper presents a System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots. The SoC consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core. The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors. The FPGA board hosting the SoC was attached to an actual differential-drive Pioneer 3-DX8 robot, which was used in field experiments in order to assess the overall performance of the tracking scheme. Moreover, quantization problems and limitations imposed by the system configuration are also discussed

Design of a teleoperation scheme with a wearable master for minimally invasive surgery

Minimally invasive surgery is increasingly being preferred over conventional surgery, however many problems still persist in longer surgeries such as pituitary surgeries, where surgeons are still required to hold an endoscope in their hand for prolonged periods of time. Many modern approaches have recently been proposed in literature to reduce the surgeon’s effort. In this paper we extended upon these previous attempts and presented a promising solution; a real time teleoperation scheme with 3 different modes of operation, composed of a wearable ring system that captures and transmits voluntary hand motions over a wireless connection to a slave system. Accordingly, this slave system processes the received data to generate velocity demands for the robot endoscope controller. Finally, the feasibility of the proposed modes of operation are demonstrated and compared by measuring their learning curve and effort by running a set of training simulations on human subjects.The Scientific and Technological Research Council of Turke

Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study

This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process, a Decision Support System (DSS) collects all information, processes it and labels it with additional defect type categories, in order to provide recommendations to the optoelectronical engineer. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. Analysis shows that chips validated through the proposed process have a probability to lase at a specific frequency six times higher than the fully rejected ones.European Union’s Horizon 2020—the Framework Programme for Research and Innovation (2014–2020) under grant agreement No 820677—Innovative strategies, sensing and process Chains for increased Quality, re-configurability, and recyclability of Manufacturing Optolectronics (iQonic