Context-aware planning by refinement for personal robots in smart homes

The idea of integrating robots and smart environments is becoming more popular. An important challenge for robotics and large advanced applications, such as Ambient Assisted Living, is to enable robots to seamlessly operate as part of smart spaces.They evolve in an unpredictable and highly dynamic environment where context information change quickly and where smart devices can join or leave it at anytime. In this situation, context-aware task planning is a key enabler. Actually, as the environment evolves, plans can become outdated, putting robots in blocking situations. Currently, few planners are able to consider smart spaces constraints during execution phase. In this paper, we propose a novel planning approach called DHTN (Dynamic HTN) based on HTN (Hierarchical Task Networks) planner. DHTN is able to generate and adapt the plan at execution and has the capability to smartly probe the environment by exclusively querying devices that provide useful data. Our approach was implemented and evaluated through simulation and a real life scenario using Nao robot in a smart office.