To ensure the safety of people, it is important that mobile robots operating in populated environments are able to take the motions of humans in their vicinity into account. An especially demanding task in this respect is accompanying a person walking through an unknown and busy environment, because it requires the robot to stay close to his client and simultaneously prevent bumping into any passers-by. This paper presents a local navigation planning approach for collision avoidance, which aims at achieving this goal. The presented technique uses probabilistic roadmaps to plan collision-free paths to a given target location relative to the robot. A laser-based people tracking component is used to estimate the motions of humans in the robot's surrounding, and a potential field method is applied for predicting the humans' future trajectories based on this information. In addition to preventing collisions, the predictions enable us to choose appropriate target locations relative to the person being attended. We tested our method on real robots and in simulations. The experiments carried out in an office environment confirm that the integrated motion prediction actually improves the performance of the collision avoidance and the robot's ability to stay close to the client it accompanies.

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In this article, we present a component-based visual tracker for mobile platforms with an application to person tracking. The core of the technique is a componentbased descriptor that captures the structure and appearance of a target in a flexible way. This descriptor can be learned quickly from a single training image and is easily adaptable to different objects. It is especially well suited to represent humans since they usually do not have a uniform appearance but, due to clothing, consist of different parts with different appearance. We show how this component-based descriptor can be integrated into a visual tracker based on the well known Condensation algorithm. Several person tracking experiments carried out with a mobile robot in different laboratory environments show that the system is able to follow people autonomously and to distinguish individuals. We furthermore illustrate the advantage of our approach compared to other tracking methods.

We present a navigation system which is able to steer an electronically controlled ground vehicle to given destinations considering all obstacles in its vicinity. The approach is designed for vehicles without a velocity controlled drive-train, making it especially useful for typical remotecontrolled vehicles. The vehicle is controlled by sets of commands, each set representing a specific maneuver. These sets are combined in a tree-building procedure to form trajectories towards the given destination. While the sets of commands are executed the vehicle's behavior is measured to refine the prediction used for path generation. This enables the approach to adapt to surface alterations. We tested our system using a 400 kg EOD robot in an outdoor environment.