Product design is a promising field for the application of new technologies and methodologies emerging from the digital evolution of Industry 4.0. A great number of tools have been developed in order to accentuate the use of modern Computer-Aided Design (CAD) systems and computational design techniques for design customization in product applications. The present paper deals with the development of two different applications for designing furniture based on the Computational-based Visual Brand Identity (CbVBI) design methodology. For the first case study, the Application Programming Interface (API) Solidworks^TM (VBA event-driven programming language) is used. The second case study focuses on the visual programming language of Grasshopper^TM, which is incorporated within Rhinoceros3D^TM. The proposed case studies offer a great deal of flexibility in both design and manufacturing, while many design alternatives could become available in a very short period.

Path planning optimization of mechatronic systems is a very important field of research that has been growing rapidly in recent years. Coordinate measuring machines (CMM), robotic arms, CNC machines, are often using a big amount of points to control the path planning. Within these efforts, some encouraging results are presented in this work on the optimization of path planning. By integrating ant colony techniques into genetic algorithm, path optimization can be reached up to 50% instead of the simple genetic algorithm. At the same time, the calculation of the optimal solution that makes this technique even more efficient is accelerated. From measurements made in a simulation model, reductions in overall trajectories were recorded up to 40% in cases where the number of points exceeded 500 while the optimal path planning time was reduced by up to 20%. In real-system implementation these values are slightly reduced due to the real-time execution of the movements. This integration makes the proposed technique particularly attractive in cases where are path planning between a large number of points and the calculation time is required to be small.

Path planning is a vital challenge in robot navigation. In the real world, robots operate in 3D environments with various obstacles and restrictions. An improved artificial fish swarm algorithm (AFSA) is proposed to solve 3D path planning problems in environments with obstacles. The improved AFSA incorporates a 3D model of 24 possible movement points to more realistically simulate real-world robot movement capabilities. Several improvements are adopted, such as methods of simple and advanced 3D elimination. The 3D implementation of an agent’s navigation model, called an “obstacle heatmap”, is also presented. The use of a safety value factor and a total movement point factor in the AFSA’s objective function are introduced. The combination of improved AFSA and a ray-casting algorithm is also presented. Finally, a method called “multiple laser activation” is introduced to overcome both the main disadvantage of the application of AFSAs in path planning and the limitation of the finite number of possible movement points that appear when bio-inspired algorithms are applied to generate the optimal path in a grid environment. The resulting path is applied to real-world challenges with drones, coordinate measuring machines, and industrial robotic arms.

Path planning is an intertemporal problem in the robotics industry. Over the years, several algorithms have been proposed to solve it, but weaknesses are constantly identified by researchers, especially in creating an optimal path in a three-dimensional (3D) environment with obstacles. In this paper, a method to reduce the lengths of optimal 3D paths and correct errors in path planning algorithms is proposed. Optimization is achieved by combining the information of a generated two-dimensional (2D) path with the input 3D path. The 2D path is created by a proposed improved artificial fish swarm algorithm (AFSA) that contains several improvements, such as replacing the random behavior of the fish with a proposed one incorporating the model of the 24 possible movement points and utilizing an introduced model to assist the agent’s navigation called obstacles heatmap. Moreover, a simplified ray casting algorithm is integrated with the improved AFSA to further reduce the length of the final path. The improved algorithm effectually managed to find the optimal path in complex environments and significantly reduce the length of the formed path compared with other state-of-the-art methods. The path was implemented in real-world scenarios of drone and industrial robotic arm applications.

In recent years, the utilization of virtual environments in industry 4.0 has witnessed significant growth, particularly in the design, implementation, and management of robotic systems. This paper addresses the need for enhanced control in robotic arms by presenting the design and implementation of a 5DoF robotic arm transformed into a digital platform through specialized software. The methods employed involve detailed direct and inverse kinematic modeling to replicate the physical arm in a digital environment. Our measurements indicate an impressive accuracy ranging from 97% to 100% in the movements of the digital model, closely mirroring its physical counterpart. This research not only contributes to the development of simulation systems but also holds promise for the broader adoption of digital twins. The paper discusses the background, outlines the methodology, highlights key findings, and concludes with the potential future impact of this work on the advancement of robotic systems and simulation technologies.

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of collaborative robots, this paper presents the development of a digital twin (DT) for the design, monitoring, optimization and simulation of robots’ deployment in assembly cells. The DT integrates information from both the physical and virtual worlds to design the trajectory of collaborative robots. The physical information about the industrial environment is replicated within the DT in a computationally efficient way that aligns with the requirements of the path planning algorithm and the DT’s objectives. An enhanced artificial fish swarm algorithm (AFSA) is utilized for the 4D path planning optimization, taking into account dynamic and static obstacles. Finally, the proposed framework is utilized for the examination of a case in which four industrial robotic arms are collaborating for the assembly of an industrial component.