In this paper we compare multi hypothesis localisation (MHL)-which is a mobile robot localisation method based on multi hypothesis tracking - with six other methods reported in the literature. The comparison is performed using a standard set of test data and corresponding evaluation tools, thus facilitating a direct comparison of the obtained results. The experiments show that MHL compares favourably to all other methods in terms of recovering when the robot has been kidnapped. When using a validation gate for filtering out noisy measurements, MHL and the standard extended Kalman filter both perform as well as all other reported methods in terms of accuracy while being faster to compute.

In this paper, research towards a learning, cooperative robotic assistance is presented. The aim of this research is to develop a robot which can easily be instructed how to either perform task autonomously or in cooperation with humans. We describe the underlying representations and methods developed for teaching new tasks and environments. The functionality has been demonstrated in a number of factory and office settings. In this paper, an example from a service scenario in an office environment is presented.

We present a probabilistic approach for mobile robot localization using an incomplete topological world model. The method, called the multi-hypothesis localization (MHL), uses multi-hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses online. Apart from a lower computational complexity, this approach has the advantage over traditional grid based methods that incomplete and topological world model information can be utilized. Furthermore, the method generates movement commands for the platform to enhance the gathering of information for the pose estimation process. Extensive experiments are presented from two different environments, a typical office environment and an old hospital building.

In this paper we present a method for calculating optimal, collisionfree movements for nonholonomic mobile robots. This is formulated as a nonlinear optimal control problem and solved using advanced numerical methods. The concept of artifical potential fields is used for accounting for the obstacles in the environment. Apart from taking advantage of the large body of knowledge from the numerical methods community this has the virtue of facilitating a simple and general problem description. Experiments show that using this method, complex movements can be calculated in a timely fashion.