Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling

Abstract

Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when conditions trigger. The approach exploits the additive property of motion generators to superpose multiple contributions. We demonstrate the applicability on a real assembly use-case and discuss the gained benefits.