Johann Marius Zöllner

Evaluation of the Dynamic Model of Fluidic Muscles using Quick-Release

By using artificial muscles in robotics one can use the analogy of the biological motor for locomotion or manipulation. There are a lot of advantages like the passive damping, good power-weight ratio and usage in rough environments. The main drawback of this muscle is that their dynamic behavior is highly nonlinear. Due to this a deep knowledge of the muscle properties and behaviors is needed to use the artificial muscle in robotics. By using the different published muscle models and our own experience we developed an advanced model for the muscle force. To validate this model a set-up like the well known quick-release test for biological muscles is used. In the future the advanced model of the fluidic muscle will help to improve the behavior of the robot PANTER and is the first step in building a biomechanical inspired two-legged robot that is able to run and walk elastically

Localization of Walking Robots

Proper navigation of walking machines in unstructured terrain requires the knowledge of the spatial position and orientation of the robot. There are many approaches for localization of mobile robots in outdoor environment, but their application to walking robots is rather rare. In particular, middle sized robots like LAURON III don’t provide the possibility to carry large or heavy sensors. Due to many degrees of freedom of walking robots the localization task becomes a even more complex challenge. This paper discusses the problem, presents a method of resolution and describes the first steps towards a localization system for the six-legged walking robot LAURON III.

Situation classification for cognitive automobiles using case-based reasoning

Driving a car in urban areas autonomously requires the ability of an in-depth analysis of the current situation. For understanding the current situation and deducing consequences for the execution of behaviors (maneuvers), higher-level reasoning about the situation has to take place. In this paper, an approach for situation interpretation for cognitive automobiles is presented. The approach relies on case-based reasoning to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

Using case-based reasoning for autonomous vehicle guidance

Vehicle guidance in complex scenarios such as inner-city traffic requires an in-depth understanding of the current situation. In order to select the appropriate behavior for an autonomous vehicle, an analysis of the situation is needed. The analysis consists of an estimation of the situations development with respect to the selected behavior. This can only be done using higher-level reasoning techniques. In this paper, an approach for situation interpretation for autonomous vehicles is presented. The approach relies on case-based reasoning in order to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

A region-based SLAM algorithm capturing metric, topological, and semantic properties

This paper proposes a SLAM algorithm based on FastSLAM 2.0 that maps features representing regions with a semantic type, topological properties, and an approximative geometric extent. The resulting maps enable spatial reasoning on a semantic level and provide abstract information allowing efficient semantic planning and a convenient interface for human-machine interaction. We present novel region features and an algorithm for estimating the feature parameters from uncertain measurements. In particular, we provide a means of estimating parameters even if the region feature is considerably larger than the robots sensor range. Finally, we adapt the FastSLAM 2.0 algorithm to map the proposed features and show simulation-based results illustrating the capabilities of the proposed algorithm.

Dexterous manipulation planning of objects with surface of revolution

In this paper, we propose a novel method for dexterous manipulation planning problem of rotating object with surface of revolution using a robotic multi-fingered hand. This method finds contact point trajectories from contact points between the robotic hand and the object with task-orientated manipulation quality measurement. Based on the defined manipulation quality, the pose for robotic hand relative to object can also be optimized by random sample. Experiments using Schunk anthropomorphic hand with 13 degrees of freedom screwing a light bulb into holder with screw thread demonstrates the feasibility and efficiency of the introduced method.

Compliant motion of a multi-segmented inspection robot

This paper presents a method to potentate the multi-segmented inspection robot Kairo-II to navigate in unstructured and dynamic environment. Previous methods for motion planning for such robots come from driving scenarios in highly structured areas. The virtual tube algorithm is introduced which enables a multi-segmented robot to range in such complex environment. Precise force feedback is required. Therefore, we present a sensor system which is based on strain-gauges technology. Information extracted by this sensor enables the trajectory planning algorithm to adapt its curve. Thus, the proposed system provides and evaluates key functions for compliant motion of a multi-segmented robot within unstructured environment.

Recognition and attribution of variable message signs and lanes

Knowledge of the position of traffic signs, lanes and the own vehicle is needed to decide whether a sign applies to oneself. This paper describes a system capable of tracking lanes and recognizing speed limit signs, both static ones and variable message signs. In addition a probabilistic approach mapping detected signs to lanes is presented which is able to correctly attribute signs to lanes even at great distance. All information is extracted from a monocular video camera.

Adaptation of a six-legged walking robot to its local environment

Walking in rough and unstructured terrain with a legged robot remains to be a challenging task, not only regarding the construction of a robust walking machine, but also looking at the control of such a complex system. Walking robots have the ability to climb over obstacles and can also walk fast in flat terrain. This is possible because the control parameters are adapted to the surrounding terrain. For example, the swing height has to correspond with the obstacle height, elsewise the robot cannot walk over these obstacles. An adaptation of the control parameters according to the environment can be realised in different ways. If there is no environment model present, the robot can only rely on its sensor systems trying to react to influences from the environment. Only the use of an environment model can enable the robot to interact more intelligently with the environment and adapt its parameters in advance. In this way, the robot can avoid many collisions, which speeds up the walking process. In the example, this would mean to increase the swing height and therefore modify the footpoint trajectory with the aim to overstep the obstacle. Because the swing height is only one parameter among many others, it is obvious how expedient an environment model is for a complex walking robot like LAURON.

Navigation of Walking Robots: Localisation by Odometry

Proper navigation of walking machines in unstructured terrain requires the knowledge of the spatial position and orientation of the robot. There are many approaches for localisation of mobile robots in outdoor environment, but their application to walking robots is rather rare. In particular, middle sized robots like Lauron III don’t provide the possibility to carry large or heavy sensors. Due to many degrees of freedom of walking robots the localisation task becomes a even more complex challenge. This paper discusses the problem, presents a method of resolution and describes the first steps towards a localisation system for the six-legged walking robot Lauron III.