Describes an approach to mobile robot position estimation based on principal component analysis of laser range data. An eigenspace is constructed from the principal components of a large number of range data sets. The structure of an environment, as seen by a range sensor, is represented as a family of surfaces in this space. Subsequent range data sets from the environment project as a point in this space. Associating this point to the family of surfaces gives a set of candidate positions and orientations (poses) for the sensor. These candidate poses correspond to positions and orientations in the environment which have similar range profiles. A Kalman filter can used to select the most likely candidate pose based on coherence with small movements. The first part of this paper describes how a relatively small number of depth profiles of an environment can be used to generate a complete eigenspace. This space is used to build a representation of the range scan profiles obtained from a regular grid of positions and orientations (poses). This representation has the form of a family of surfaces (a manifold). This representation converts the problem of associating a range profile to possible positions and orientations into a table lookup. As a side benefit, the method provides a simple means to detect obstacles in a range profile. The final section of the paper reviews the use of estimation theory to determine the correct pose hypothesis by tracking.

While distributed control architectures have many advantages over centralized ones, such as their inherent modularity and fault tolerance, a major problem of such architectures is to ensure the goal-oriented behaviour of the controlled system. This paper presents a framework within which the coordination skills required for goal-orientedness are learned from user demonstrations. The framework is based on a state-space model of the single agents building the system and a corresponding model of the coordination mechanism. Our mobile robot PRIAMOS provides an application example.

A good map of its environment is essential for efficient task execution of a mobile robot. Real time map update, especially in dynamic scenes is a difficult problem due to noisy sensor data and limited observation time. The paper describes a mapping procedure which identifies new obstacles in a scene and constructs a 3D surface model of it. This description is included in the geometrical map which robot navigation relies on. The mapping procedure is based on sonar range information and scenes reconstructed from stereo vision. The combination of sonar and stereo vision is advantageous, due to a complementary error characteristic concerning range and angular resolution. For sonar data integration the idea of local probability grids is proposed. Local grids which only cover areas where new obstacles are expected, reduce the complexity of grid based sonar data integration and can be applied to a dynamic environment. The partial models of an object that has been observed from different viewpoints are fused to a homogeneous description in a later step. A complex example shows the mapping procedure work robustly in dynamic indoor environments.

The problem of adapting mobile robot navigation to changes in the environment is usually approached by modifying an internal world model. Descriptions on different levels of abstraction provide the information necessary for navigation and therefore influence the robot's behaviour. The effect of such indirect adaptation is limited. The approach presented in this paper describes a new technique for direct integration of navigation experience in path planning. Thus, not only the world knowledge, but also the planning behaviour is improved over time. Experiments are carried out on a robot which is controlled by a layered architecture. It is integrated in a multirobot control environment which is described. The focus of the article is towards improving the higher navigation levels. The main idea being presented is the realization of adaptive behaviour not only on the level of reflexes, but also with respect to the planning capabilities of the robot. The application of learning techniques allows to continuously improve the estimation of plan costs and therefore the inherent strategy of the topological planner. It is illustrated that a combined learning of world description and navigation allows fast and sophisticated reaction to new environmental conditions.>