In an integrated automation system a well-designed data model and computation model are required. For the engineering of such a system one often faces a challenge of finding the suitable computation elements defined in the computation model for the data defined in the data model, especially when the data model and the computation model are complex. In this paper we will use the ontology technology and Web services to address the challenge in an intelligent way. We will present the required ontology model including the formal expression, the binding conditions, the object model, XML representation and the corresponding system architecture for implementation. Evaluation is also given to show the usefulness of the approach.

Mobile robots for advanced applications have to act in environments which contain moving obstacles. Motions of obstacles (e.g. humans) usually are not precisely predictable, but neither they are completely random. Long-term observation of obstacle behavior may yield knowledge about prevailing motion patterns. This paper presents concepts and results of an experimental system. Using cameras mounted at the ceiling, the workspace is observed. The image data is processed and transformed into a compact statistical representation of motion patterns. Mobile robot motion planning benefits from such additional knowledge: trajectories are rated in terms of collision probability and expected time for reaching the goal; planning yields efficient paths which are adapted to obstacle behavior.

The more the robot knows about its environment, the more efficiently it can operate. This is the basic idea of the system presented in this paper. By employing an external camera-based sensing system, a global model of the robot's environment is maintained and updated continuously. Thus, motion planning and collision avoidance in a dynamic environment (i.e. an environment which contains moving obstacles) can be done efficiently. For many indoor applications this concept is inherently superior compared to mobile robots which act completely autonomously. The paper presents concepts and components of an experimental system: Sensing is done with CCD cameras; the processed data is furnished to different modules regarding robot localization, obstacle detection and motion planning.

In recent papers we (1996, 1997) have proposed a new path planning approach for mobile robots: statistical motion planning with respect to typical obstacle behavior in order to improve pre-planning in dynamic environments. In this paper, we present our experimental system: in a real environment, cameras observe the workspace in order to detect obstacle motions and to derive statistical data. We have developed new techniques based on stochastic trajectories to model obstacle behavior. Collision probabilities are calculated for polygonal objects moving on piecewise linear trajectories. The statistical data can be applied directly, thus the entire chain from raw sensor data to a stochastic assessment of robot trajectories is closed. Finally, some new work regarding different applications of statistical motion planning is outlined, including road-map approaches for pre-planning, expected time to reach the goal, and reactive behaviors.

As recent research is directed towards flexible and autonomous robot behaviour, a priori knowledge is increasingly replaced by data obtained automatically by external sensors of the robot. Much effort already has been made regarding the integration of different types of sensor data into reliable world representations. Yet, the aspect of guiding robot perception in a systematic manner often has been neglected. Closing this gap, we present a framework addressing the efficient exploration of an unknown three-dimensional environment. A natural approach to this problem is a repeated "planning-sensing-updating"-cycle. In this paper special attention is paid on planning: Based on the current, incomplete world model and on constraints respecting the robot's motions, rating functions in configuration space are defined. When planning sensing actions, configurations with high ratings are chosen. Thus, a fast and efficient exploration of the workspace is guaranteed. Our approach works with miscellaneous robot architectures, including mobile systems as well as manipulators with many degrees of freedom and redundancies. The inverse kinematic solution of the robot has not to be known. The sensors to be used have to provide range measurements, like laser scanners, stereo or sonar systems.

To plan collision-free trajectories for mobile robots among moving obstacles most approaches presume the obstacles' trajectories to be known. This is contradictory to practice where motions generally are known-at best-as average behaviours. Thus, we proposed to use statistical information for path planning. This paper focuses on the derivation of formulae to estimate collision probabilities. By generalizing elementary collision patterns we develop a method to calculate a collision probability field. For any given goal position, it estimates the collision probabilities for paths starting at arbitrary points in the workspace. This provides a powerful tool for path planning in dynamic environments. Using an algorithm similar to path planning based on potential fields, we can calculate paths with (approximately) minimum collision probability.