Petri nets for modeling and coordination of robotic tasks

Abstract

Petri nets have been widely used to model dynamic systems, namely manufacturing systems. In this paper we introduce the use of Petri nets to model robotic tasks. Different views of the robotic task model can be modeled by distinct Petri net types: interpreted Petri nets for task design and execution, generalized stochastic Petri nets for task quantitative performance evaluation and ordinary Petri nets for task qualitative performance evaluation. Quantitative performance evaluation and improvement based on reinforcement learning from feedback are detailed in the paper. Examples of applications to visual servoing and catching of moving objects by a robotic arm and to mobile robot tasks are presented.