Multi-tasking arbitration and behaviour design for human-interactive robots
Robots that interact with humans in household environments are required to handle multiple real-time tasks simultaneously, such as carrying objects, collision avoidance and conversation with human. This article presents a design framework for the control and recognition processes to meet these requirements taking into account stochastic human behaviour. The proposed design method first introduces a Petri net for synchronisation of multiple tasks. The Petri net formulation is converted to Markov decision processes and processed in an optimal control framework. Three tasks (safety confirmation, object conveyance and conversation) interact and are expressed by the Petri net. Using the proposed framework, tasks that normally tend to be designed by integrating many ifthen rules can be designed in a systematic manner in a state estimation and optimisation framework from the viewpoint of the shortest time optimal control. The proposed arbitration method was verified by simulations and experiments using RI-MAN, which was developed for interactive tasks with humans.
multi-tasking arbitration behaviour design petri net human interaction