Jens-Steffen Gutmann

Obstacle avoidance and path planning for humanoid robots using stereo vision

This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.

CS Freiburg: coordinating robots for successful soccer playing

Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. The paper presents the CS Freiburg team, the winner in the middle-size league at RoboCup 1998, 2000, and 2001. The paper focuses on multiagent coordination for both perception and action. The contributions of the paper are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment; a rich set of basic skills allowing robots to respond to a large range of situations in an appropriate way, and an action-selection method based on behavior networks, as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multirobot group which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multirobot field.

3D Perception and Environment Map Generation for Humanoid Robot Navigation

A humanoid robot that can go up and down stairs, crawl underneath obstacles or simply walk around requires reliable perceptual capabilities for obtaining accurate and useful information about its surroundings. In this work we present a system for generating three-dimensional (3D) environment maps from data taken by stereo vision. At the core is a method for precise segmentation of range data into planar segments based on the algorithm of scan-line grouping extended to cope with the noise dynamics of stereo vision. In off-line experiments we demonstrate that our extensions achieve a more precise segmentation. When compared to a previously developed patch-let method, we obtain a richer segmentation with a higher accuracy while also requiring far less computations. From the obtained segmentation we then build a 3D environment map using occupancy grid and floor height maps. The resulting representation classifies areas into one of six different types while also providing object height information. We apply our perception method for the navigation of the humanoid robot QRIO and present experiments of the robot stepping through narrow space, walking up and down stairs and crawling underneath a table.

Cooperative sensing in dynamic environments

This work presents methods for tracking objects from noisy and unreliable data taken by a team of robots. We develop a multi-object tracking algorithm based on Kalman filtering and a single-object tracking method involving a combination of Kalman filtering and Markov localization for outlier detection. We apply these methods in the context of robot soccer for robots participating in the RoboCup middle-size league and compare them to a simple averaging method. Results including situations from real competition games are presented.

Stair climbing for humanoid robots using stereo vision

For the fully autonomous navigation in a 3 dimensional world, a humanoid robot must be capable of stepping up and down staircases and other obstacle where a sufficient large flat surface can support the robots feet. This paper presents methods for the recognition of stairs and a control architecture that enable the humanoid robot QRIO to safely climb up and down in its environment The approach is based on data captured by a stereo vision system and segmented into planar surfaces. From the segmented planes, stairs that can be climbed by the robot are extracted and fed to a control system which decides the action to be taken next. Experimental results on a staircase as well as climbing up and down a sill are presented.

A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot

With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robots surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.

A fast, accurate and robust method for self-localization in polygonal environments using laser range finders

Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for participating in robotic soccer competitions, it turned out that none of the existing approaches met our requirements of being fast, accurate and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. This method is one of the key components and is probably one of the explanations for the success of our team in national and international competitions. We also present experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.

Markov-Kalman localization for mobile robots

Localization is one of the fundamental problems in mobile robot navigation. Recent experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering, while the latter can be more accurate than the former In this paper, we present a novel approach called Markov-Kalman localization (ML-EKF) which is a combination of both methods. ML-EKF is well suited for robots observing known landmarks, having a rough estimate of their movements, and which might be displaced to arbitrary positions at any time. Experimental results show that our method outperforms both of its underlying techniques by inheriting the accuracy of Kalman filtering and the robustness and relocalization speed of the Markov method.

Navigation mobiler Roboter mit Laserscans

Es wird ein Verfahren zur Erstellung einer topologischen Karte aus Laserscandaten fur die Navigation mobiler Roboter beschrieben. Aus einem Satz sich korrekt uberdeckender 360°-Scans wird ein Sichtbarkeitsgraph erstellt, wobei Knoten Scanpositionen und Kanten die relative Anzahl gemeinsamer Scanpunkte (genannt Sichtbarkeit) reprasentieren. Aus der Sichtbarkeit und der Distanz der Scanpositionen wird eine subjektive Wahrscheinlichkeit fur die Befahrbarkeit zwischen den Scanpositionen berechnet. Durch Annahme von Unabhangigkeit der berechneten Wahrscheinlichkeiten wird mittels uniformer Kostensuche ein moglichst kurzer und sicher befahrbarer Pfad bestimmt. Das Verfahren wurde auf einem Pioneer-1-Roboter mit SICK-Laserscanner implementiert und erprobt. Fur die Navigation zu jedem Zwischenziel entlang des Pfades wurde ein gitterbasierter lokaler Wegeplaner verwendet. Dadurch konnte ein hoher Grad an Robustheit erlangt werden. Das System ist in der Lage unvorhergesehenen Hindernissen auszuweichen, nicht passierbare Wege zu erkennen und alternative Wege zu finden.

The CS Freiburg Robotic Soccer Team: Reliable Self-Localization, Multirobot Sensor Integration, and Basic Soccer Skills

Robotic soccer is a challenging research domain because problems in robotics, artificial intelligence, multi-agent systems and real-time reasoning have to be solved in order to create a successful team of robotic soccer players. In this paper, we describe the key components of the CS Freiburg team. We focus on the self-localization and object recognition method based on using laser range finders and the integration of all this information into a global world model. Using the explicit model of the environment built by these components, we have implemented path planning, simple ball handling skills and basic multi-agent cooperation. The resulting system is a very successful robotic soccer team, which has not lost any game yet.