A Knowledge and Probabilistic Based Task Planning Architecture for Service Robotics

Service robots have to face task diversity, large, uncertain and partially observable environments, which are inherent aspects of the domestic domain and increase the task planning problem’s complexity. Thus, in an attempt to overcome these challenges, in this work a task planning architecture for service robotics is proposed, which integrates a knowledge base approach with partially observable Markov decision processes (POMDP), and is constituted by three main components: (a) a knowledge base, (b) a POMDP construction module and (c) a task controller. Through a knowledge representation scheme, domain relevant information is exploited to define useful sub-regions in the planning search space. Once the search space is segmented, local POMDP policies are computed for each sub-region, then, a graph-based policy for the main task is built as a collection of these policies, for which the controller will determine the order in which they will be executed. Additionally, our architecture is able to integrate new functionalities as the robot is endowed with them. For evaluation purposes, a mobile robot navigation problem was used as case study to test our architecture, which shows the advantages of using domain specific knowledge in a task planning problem.