for the HRI, utilizing digital technologies such as Mixed Reality (MR). Specifically, the framework can be implemented as a methodology for the remote and safe manipulation of the robotic arm in near real-time while simultaneously displaying safety zones in the field of view of the shop-floor technician. The approach is based on creating a Digital Twin of the robotic arm and establishing a suitable communication framework for continuous and seamless communication between the user interface, the physical robot, and the Digital Twin. The development of the method involves using ROS (Robot Operating System) for Digital Twin modeling, a Cloud database for data handling, and Mixed Reality (MR) for the Human-Machine Interface (HMI). The MR application developed is tested in a laboratory-based machine shop that incorporates collaborative cells. [PERSON] et al. in [3] proposed a modified cubic Hermite interpolation for joint motion planning in order to address issues related to point-to-point joint motion planning of industrial robots with constrained velocity, in which cubic polynomial planning exhibits discontinuous acceleration, while quintic polynomial planning requires pre-specified acceleration may lead to significant velocity fluctuations. The proposed methodology involves reconfiguring knots of the cubic Hermite interpolation according to the initial knots, and the formulas for building new knots are derived. Using the newly built knots in place of the initial knots for cubic Hermite interpolation, joint motion planning is conducted to ensure C\({}^{2}\) continuity and minimize velocity fluctuations while meeting the displacement and velocity constraints at the initial knots. For the validation of the proposed method, a case study is presented, which proves that the presented approach can effectively resolve the issues associated with the other two planning methods. With the implementation of this methodology, the improvement of the working performance and service life of industrial robots is expected. In the fourth paper of the SI, [PERSON] et al. [4] presented a robotic cell that is capable of handling geometrically complex products by utilizing cognitive control and actuation systems for manipulation, assembly, and packaging. In order to achieve this, the mechatronic components, including a 6-degree-of-freedom (DoF) gripper and a flexible assembly mechanism, were designed through functional decomposition of the handling and assembly tasks. The flexibility of these mechanisms is leveraged through control modules which are capable of performing a wide variety of cognitive functions at the cell, resource, and device levels. The applied design approach can be applied to several tasks requiring dexterity and adaptation to different product types. A case study from the consumer goods industry is presented to demonstrate the reconfigurability and efficiency of the system. In the context of robotic flexibility in industry, particularly with the emergence of the Industry 4.0 concept, [PERSON] et al. in [5], have proposed a framework for smart execution control, aimed at facilitating the independent operation of flexible mobile robots in a manufacturing environment. These robots are capable of navigating the shopfloor independently and performing multiple tasks, while also serving as assistants to human operators. In order to facilitate such autonomous functionalities, the framework incorporates robot perception functions, which provide a real-time understanding of the shopfloor and the manufacturing process, while also coordinating the execution of tasks. A Digital World Model is deployed to combine sensor data from various 2D, and 3D sensors installed throughout the shopfloor. This model is used by the perception functions for enabling real-time perception of the shopfloor and manufacturing process by the robots. The effectiveness of this smart control system has been verified through a case study in the automotive industry. [PERSON] et al.