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.

General purpose service robots are expected to deal with many different tasks in unknown environments. The number of possible tasks and changing situations prevent developers from writing control programs for all tasks and possible situations. Complex robot tasks are thus accomplished by sequential execution of less complex robot actions that are triggered and configured by a task planner. The question of the appropriate abstraction level of robot actions is still being researched and not discussed conclusively. In this paper, we address the problem of atomicity of robot actions and provide some key properties that have to be considered while designing plan-based robot control systems. Based on these properties, we define and implement atomic skills of different abstraction level for the service robot TASER. The HTN planner JShop2 is used to complete the plan-based control architecture which is evaluated in a set of experiments.

Robot perception still lacks reliability in complex natural environments. A commonly used method to improve perception is to incorporate more sensors with different modalities. This leads to increased computational requirements due to the parallel processing of huge amounts of sensor data. Appropriate sensor fusion methods are needed if contradictory information is provided by different sensors. We propose a feature-based technique to fuse multimodal sensor data using fuzzy rules. Probabilistic methods are avoided by applying fuzzyfication at the feature level. We propose a higher information gain of the available sensors by utilizing robot actions to focus sensors on objects of interest. Therefore sensor readings, algorithms and robot actions are combined into feature detectors. A goal-directed activation of these feature detectors renders parallel processing of all sensor data unnecessary.