Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics

In recent years, collaborative robots have become one of the main drivers of Industry 4.0. Compared to industrial robots, automated guided vehicles (AGVs) are more productive, flexible, versatile, and safer. They are used in the smart factory to transport goods. Today, many producers and developers of industrial robots have entered the AGV sector. However, they face several challenges in designing AGV systems, such as the complexity and discontinuity of the design process, as well as the difficulty of defining a decentralized system decision. In this paper, we propose a new integrated design methodology based on swarm robotics to address the challenges of functional, physical, and software integration. This methodology includes two phases: a top-down phase from requirements specification to functional and structural modeling using the systems modeling language (SysML); with a bottom-up phase for model integration and implementation in the robot operating system (ROS). A case study of an automated guided vehicle (AGV) system was chosen to validate our design methodology and illustrate its contributions to the efficient design of AGVs. The novelty of this proposed methodology is the combination of SysML and ROS to address traceability management between the different design levels of AGV systems, in order to achieve functional, physical and software integration.

Robotic industrial automation simulation-optimization for resolving conflict and deadlock

Purpose The purpose of this study is to investigate the benefits of the turning point layout; a simulation model being applicable for strategic level is designed that compares systems with and without turning points. Specifically, the avoidance of deadlocks and prevention of conflicts is substantial. Design/methodology/approach Optimization process for different layouts and configuration of autonomous guided vehicles (AGVs) are worked out using statistical methods for design parameters. Regression analysis is used to find effective design parameters and analysis of variance is applied for adjusting critical factors. Also, the optimal design is then implemented in a manufacturing system for an industrial automation and the results are reported. Findings The outputs imply the effectiveness of the proposed approach for real industrial cases. This research will combine both simulation-based method and optimization technique to improve the quality of solutions. Originality/value In AGV systems, the begin-end combinations are usually connected by using a fixed layout, which is not the optimal path. The capability of these configurations is limited and often the conflict of multiple AGVs and deadlock are inevitable. By appearing more flexible layouts and advanced technology, the positioning and dispatching of AGVs increased. A new concept would be to determine each path dynamically. This would use the free paths for AGVs leading to overcome the conflicts and deadlocks.