Towards automated task planning for service robots using semantic knowledge representation
Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.
Ze Ji Renxi Qiu Dayou Li Shuo Xu
Alex Noyvirt, Anthony Soroka,Michael Packianather, Rossi SetchiSchool of EngineeringCardiff Universi School of Computer ScienceBedfordshire University Shanghai Key Laboratory of Manufacturing Automation and Robotics School of Mechatronic Engineering a
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
1194-1201
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）