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Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented.

Background: This article provides a scoping review on the current status of Image-Guided Navigation with various forms of digital technologies, including Extended Reality, Augmented Reality Head-Mounted Displays (AR–HMDs) and Robot-Assisted Surgery (RAS) for Pedicle Screw Placement in orthopedics and spine surgery. Methods: A scoping literature review was performed in the PubMed, Scopus, Embase, Web of Science, Google Scholar and IEEE Xplore databases to collect clinical and user satisfaction data on AR–HMDs and compare those with RAS outcomes. In vivo patient, cadaver and phantom trial accuracy data reports were identified and grouped through the analysis. Over the past two years, 14 publications were retrieved and analyzed. Pedicle screw placement accuracy was described with Linear Tip Error (LTE), Angular Trajectory Error (ATE) and Gertzbein–Robbins Scale (GRS) outcomes. Results: The Pedicle Screw Placement accuracy was seen to increase in the in vivo, cadaver and phantom model groups using AR-HMD compared to the Free-Hand insertion technique. User experience and satisfaction data were limited; however, a clear advantage for the operative results was described when it was added. RAS screwing showed similar accuracy outcomes. The need for benchmarking and quantified situation awareness for AR–HMDs is recognizable. The authors present a method for standardized scoring and visualization of surgical navigation technologies, based on measurements of the surgeon (as the end-users) user satisfaction, clinical accuracy and operation time. Conclusions: computer-technology driven support for spine surgery is well-established and efficient for certain procedures. As a more affordable option next to RAS, AR–HMD navigation has reached technological readiness for surgical use. Ergonomics and usability improvements are needed to match the potential of RAS/XR in human surgeries.