[Show abstract][Hide abstract] ABSTRACT: In recent years, there has been an increasing interest in autonomous navigation for lightweight flying robots. With regard to self-localization flying robots have several limitations compared to ground vehicles. Due to their limited payload flying vehicles possess only limited computational resources and are restricted to a few and lightweight sensors. Additionally the kinematics of flying robots is rather complex, which requires sophisticated motion models that are typically hard to calibrate. However, as the sensors provide only a limited amount of information, the motion models need to be highly accurate to reduce the potential increase of uncertainty caused by the movements of the vehicle. In this paper, we present a novel approach to simultaneous localization and estimation of motion model parameters and their adaptation in the context of a particle filter. To deal with sudden changes of parameters, our approach utilizes random sampling augmented by additional damping to avoid oscillations caused by the delayed detection of the changes. As we demonstrate in experiments with a real blimp, our method can deal with very sparse and imprecise sensor information and outperforms a standard Monte Carlo localization approach.
[Show abstract][Hide abstract] ABSTRACT: Wheeled tour guide robots have already been deployed in various museums or fairs worldwide. A key requirement for successful tour guide robots is to interact with people and to entertain them. Most of the previous tour guide robots, however, focused more on the involved navigation task than on natural interaction with humans. Humanoid robots, on the other hand, offer a great potential for investigating intuitive, multimodal interaction between humans and machines. In this paper, we present our mobile full-body humanoid tour guide robot Robotinho. We provide mechanical and electrical details and cover perception, the integration of multiple modalities for interaction, navigation control, and system integration aspects. The multimodal interaction capabilities of Robotinho have been designed and enhanced according to the questionnaires filled out by the people who interacted with the robot at previous public demonstrations. We present experiences we have made during experiments in which untrained users interacted with the robot.
[Show abstract][Hide abstract] ABSTRACT: Solving localization and navigation tasks reliably is an essential objective for autonomous mobile sys-tems and robots. A popular technique for esti-mating the robot's pose is localization with parti-cle filters, also known as Monte–Carlo localization (MCL). In this paper we present a MCL–based lo-calization system that employs informed proposal distributions to sample particles during the motion step of the filter. While the standard MCL model computes the sampling proposal distribution based only on the movement on the robot, our approach also takes the most recent sensor observation into account. Experiments using datasets gathered by real robots in an indoor environment show that our approach is able to estimate the robot's pose with less uncertainty than the standard MCL implemen-tation.