This paper presents a novel approach to high-resolution ultrasonic environment imaging for autonomous mobile systems. It describes the fusion of sensor data from multiple wide-angled ultrasonic transducers with overlapping detection cones for creating local environment models. A fitting technique defined as tangential regression is introduced that considers sensor properties as well as physical reflection properties of ultrasound. This allows a reliable detection and localization of straight-line segments which describe the boundary of geometric objects. Experimental results show that straight-line segments extracted at different robot locations can be aggregated and merged in order to obtain a global environment map

In this paper we describe an approach to coordinating the motion of a human with a mobile robot moving in a populated, continuously changing, natural environment. The approach is based on a method for motion planning amongst moving obstacles known as the velocity obstacle approach. We extend this method by a method for tracking a virtual target which allows us to vary the robot's heading and velocity with the locomotion of the accompanied person and the state of the surrounding environment. Our test application is a wheelchair accompanying a person through the concourse of a railway station.