AI based combined scheduling and motion planning in flexible robotic assembly lines

Abstract

One of the latest trends in EU manufacturing research, is to increase reconfigurability of production systems by introducing mobile dual arm workers able to collaborate with humans in dynamic changing environments. The flexibility of the system is increased by the ability of the mobile robots to easily re-locate themselves inside the shopfloor for performing various assembly operations. This paper presents a multi – level decision making framework targeting on the dynamic work balancing among the human operators and robot resources. The proposed mechanism exploits Artificial Intelligence techniques for generating efficient task sequences while ensuring optimal and collision free path generation for the assigned resources. The focus is given on the integration of multi- criteria decision-making mechanisms with mobile robots’ low-level planning modules for deriving effective reconfiguration plans considering the shopfloors real time status. The developments have been tested in a case study from the automotive industry.