Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers - Chen, Hao Liang; Hendrikx, Bob; Torta, Elena; Bruyninckx, Herman; van de Molengraft, René (Frontiers in Robotics and AI, 2023)
Solving Constraint-based Optimization Problem (COP) has become a mainstream technology for advanced motion control of mobile robots. Its programming still requires expert knowledge to transform the specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which to our knowledge has not yet been done. The former is offered a selected set of symbolic descriptions of the robots' capabilities (its so-called "behavior semantics"), that are translated in control actions via "templates" in a "semantic map"; the latter contains the parameters that cover contextual dependencies in an application and robot vendor independent way. The translation from semantics to control templates takes place in an "interaction layer", that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of "collision areas"), and 3) generic application knowledge (e.g., how the robots' behavior is impacted by priorities, emergency, safety, prudence, etc.). This particular design of, and interplay between, the application, interaction and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities, should result in an interaction layer with different standardized versions of semantic complexity.