In recent years, autonomous miniature airships have gained increased interest in the robotics community. This is due to their ability to move safely and hover for extended periods of time. The major constraints of miniature airships come from their limited payload which introduces substantial constraints on their perceptional capabilities. In this paper, we consider the problem of localizing a miniature blimp with lightweight ultrasound sensors. Since the opening angle of the sound cone emitted by a sonar sensor depends on the diameter of the membrane, small-size sonar devices introduce the problem of high uncertainty about which object has been perceived. We present a novel sensor model for ultrasound sensors with large opening angles that allows an autonomous blimp to robustly localize itself in a known environment using Monte Carlo localization. As we demonstrate in experiments with a real blimp, our novel sensor model outperforms a popular sensor model that has in the past been shown to work reliably on wheeled platforms.

In this paper, we present a novel approach to controlling a robotic system online from scratch based on the reinforcement learning principle. In contrast to other approaches, our method learns the system dynamics and the value function separately, which permits to identify the individual characteristics and is, therefore, easily adaptable to changing conditions. The major problem in the context of learning control policies lies in high-dimensional state and action spaces, that needs to be explored in order to identify the optimal policy. In this paper, we propose an approach that learns the system dynamics and the value function in an alternating fashion based on Gaussian process models. Additionally, to reduce computation time and to make the system applicable to online learning, we present an efficient sparsification method. In experiments carried out with a real miniature blimp we demonstrate that our approach can learn height control online. Further results obtained with an inverted pendulum show that our method requires less data to achieve the same performance as an off-line learning approach.

In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to previous approaches, our method does not require sophisticated hand- tuned models, but rather learns the policy online, which makes the system easily adaptable to changing conditions. The blimp we apply our approach to is a small-scale vehicle equipped with an ultrasound sensor that measures its elevation relative to the ground. The major problem in the context of learning control policies lies in the high-dimensional state-action space that needs to be explored in order to identify the values of all state-action pairs. In this paper, we propose a solution to learning continuous control policies based on the Gaussian process model. In practical experiments carried out on a real robot we demonstrate that the system is able to learn a policy online within a few minutes only.

In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp.