The research objective of the subproject is to develop a general-purpose autonomous robotic operation sequence planner and a multi-agent execution mechanism.
Tasks:
When planning various robotic assembly, machining, or material handling operations, a critical optimization task—affecting solution efficiency—is determining the sequence and execution method of individual sub-operations. While planning tasks across different applications share many similarities, their details can differ significantly. The goal of the research is to develop a general representation, solving algorithms, and software tools that enable these planning tasks to be efficiently solved in typical robotic applications.
The research aims to develop a digital twin model that, using collision detection and material removal simulation modules, can reproduce an accurate digital counterpart of a real robotic machining cell in real time. This digital twin enables the creation of virtual sensors and supports the development of an autonomous real-time cell control system that significantly enhances the robustness, efficiency, and safety of the cell’s operation.
An open, flexible, and robust resource model for discrete manufacturing and its supporting internal logistics system can be created using multi-agent models. The operation of such a system emerges from the decisions and supervision of autonomously organizing agents. The goal of the research is to develop a general process modeling methodology that ensures the time-evolving execution of production plans and schedules while respecting constraints related to operations and resources.
