Combined task and motion planning system for the service robot using hierarchical action decomposition

Abstract

A service robot needs integration of symbolic reasoning for high-level task planning and geometric computation for low-level motion planning to provide services in an everyday human-living environment. For this purpose, one may develop individual modules for object recognition, knowledge inference, task planning, and motion planning, which requires a system that integrates them to provide services autonomously. In this paper, we propose a combined task-motion planning system implemented using existing open-source libraries. We implemented an action library module that specifies the relationship between the compound actions that are modeled in an AI planning language to enable abstract reasoning and the primitive actions that can be geometrically verified its feasibility. This serves as an interface between the two levels by providing a rule in which a compound action sequence obtained from task planning at the symbolic level is decomposed into a primitive action sequence capable of motion planning at the geometric level. In addition, we defined the relationship between the two types of actions in a hierarchical structure and added conditional clauses according to the task states, so that primitive actions that need to be additionally performed are automatically added to the action sequence during decomposition. This procedure enables the robot to successfully perform the task in response to an unintended change in the environment. We establish a task domain where a robot delivers an object to a human user in unexpected situations and verify the proposed method under dynamic simulation environments. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.