Abstract-Experiments showed that Hierarchical Task Network (HTN) planners are suitable to find solutions for nontrivial tasks in complex scenarios. Mobile service robots are able to execute actions which may constitute the basic building blocks to achieve high-level goals. However, only few experiments demonstrate the application of a general purpose deliberative planner in the domain of mobile service robots. One challenging problem arises from the fact that adaptive AI-based planners presume the closed-world assumption (CWA) and are therefore unable to deal with incomplete information. Unknown objects which are not represented in the planning domain, for example, cannot be integrated into the planning process. Since mobile service robots act in a real dynamic environment and construct or adapt their world model autonomously based on sensory data, they are inevitably confronted with uncertain and incomplete information about the world. This conflict between simplified assumptions for planning on the one hand and the complexity of the real world on the other constitutes a major problem of modern robotics. This paper describes two approaches to dealing with incomplete world knowledge in the context of HTN robot planning. Several experiments demonstrate that the approaches can successfully be applied in a dynamic and unstructured environment.

Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment. We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER.

Generating plans in order to perform high-level tasks is difficult for agents that act in open-ended domains where it is unreasonable to assume that all necessary information is available a priori. This paper addresses this challenge by presenting a planning-based control system that is able to perform tasks in open-ended domains. The control system is based on a new HTN planning approach that additionally considers decompositions that would be applicable with respect to a consistent extension of the domain model at hand. The proposed control system constitutes a continual planning and acting system that interleaves planning and acting so that missing information can be acquired by means of active information gathering. Experimental results demonstrate that this control architecture can perform tasks in several domains even if the agent initially has no factual knowledge.

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