Automatic optimal motion generation for robotic manufacturing processes: Optimal collision avoidance in robotic welding

Optimal, efficient and intuitive robotic programming is still a challenge in robotic manufacturing and one of the main reasons why robots are not widely implemented in small and medium-sized enterprises (SME). In order to effectively and efficiently respond to the current product variability requirements, SMEs require easy and optimal programmable robotic manufacturing systems in order to achieve profitable and rapid changeover. To make up for this deficiency, this paper proposes a solution approach for computing optimal motions for manufacturing processes based on the interpretation of the manufacturing process and an automatic configuration of a state of the art sample-based algorithm, the Rapidly-exploring Random Tree RRT* which is provably asymptotically optimal, using as inputs the semantic and mathematical descriptions of the product, process and resource components. The approach is simulated on the example of collision avoidance for different scenarios in robotic welding revealing its functionality and outlining future potentials for the optimal motion generation for robotic manufacturing processes.