• Automation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networks

      Al-Moadhen, Ahmed; Packianather, Michael; Setchi, Rossi; Qiu, Renxi (Elsevier, 2014-09-13)
      Generating plans in real world environments by mobile robot planner is a challenging task due to the uncertainty and environment dynamics. Therefore, task-planning should take in its consideration these issues when generating plans. Semantic knowledge domain has been proposed as a source of information for deriving implicit information and generating semantic plans. This paper extends the Semantic-Knowledge Based (SKB) plan generation to take into account the uncertainty in existing of objects, with their types and properties, and proposes a new approach to construct plans based on probabilistic values which are derived from Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inferencing together with semantic information to provide a basis for plausible learning and reasoning services in supporting of robot task-planning. In addition, an algorithm has been devised to construct MLN from semantic knowledge. By providing a means of modeling uncertainty in system architecture, task-planning serves as a supporting tool for robotic applications that can benefit from probabilistic inference within a semantic domain. This approach is illustrated using test scenarios run in a domestic environment using a mobile robot.
    • Integrating robot task planner with common-sense knowledge base to improve the efficiency of planning

      Al-Moadhen, Ahmed; Qiu, Renxi; Packianather, Michael; Ji, Ze; Setchi, Rossi; Cardiff University (Elsevier, 2013)
      This paper presents a developed approach for intelligently generating symbolic plans by mobile robots acting in domestic environments, such as offices and houses. The significance of the approach lies in developing a new framework that consists of the new modeling of high-level robot actions and then their integration with common-sense knowledge in order to support a robotic task planner. This framework will enable interactions between the task planner and the semantic knowledge base directly. By using common-sense domain knowledge, the task planner will take into consideration the properties and relations of objects and places in its environment, before creating semantically related actions that will represent a plan. This plan will accomplish the user order. The robot task planner will use the available domain knowledge to check the next related actions to the current one and the action's conditions met will be chosen. Then the robot will use the immediately available knowledge information to check whether the plan outcomes are met or violated.
    • Towards automated task planning for service robots using semantic knowledge representation

      Ji, Ze; Qiu, Renxi; Noyvirt, Alex; Soroka, Anthony; Packianather, Michael; Setchi, Rossi; Li, Dayou; Xu, Shuo; Cardiff University; University of Bedfordshire; et al. (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012)
      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.