Joachim Schröder

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

In this paper, we present a new humanoid robot currently being developed for applications in human-centered environments. In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. We introduce the different subsystems of the robot. We present the kinematics, sensors, and the hardware and software architecture. We propose a hierarchically organized architecture and introduce the mapping of the functional features in this architecture into hardware and software modules. We also describe different skills related to real-time object localization and motor control, which have been realized and integrated into the entire control architecture

Toward humanoid manipulation in human-centred environments

In order for humanoid robots to enter human-centred environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. In this paper, we present a new humanoid robot currently being developed for applications in human-centred environments. In addition, we present an integrated grasping and manipulation system consisting of a motion planner for the generation of collision-free paths and a vision system for the recognition and localization of a subset of household objects as well as a grasp analysis component which provides the most feasible grasp configurations for each object.

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

Building a model of the environment is essential for mobile robotics. It allows the robot to reason about its sourroundings and plan actions according to its intentions. To enable safe motion planning it is vital to anticipate object movements. This paper presents an improved formulation for occupancy filtering. Our approach is closely related to the Bayesian Occupancy Filter (BOF) presented in [4]. The basic idea of occupancy filters is to represent the environment as a 2-dimensional grid of cells holding information about their state of occupancy and velocity. To improve the accuracy of predictions, prior knowledge about the motion preferences is used, derived from map data that can be obtained from navigation systems. In combination with a physically accurate transition model, it is possible to estimate the environment dynamics. Experiments show that this yields reliable estimates even for occluded regions.

Navigating car-like robots in unstructured environments using an obstacle sensitive cost function

We propose a method for navigating a car-like vehicle within an unstructured environment. Path planning is posed as a graph search problem. The search graph is set up in a way that implies derivation of a feed forward term for a downstream closed loop controller. An informed search algorithm is used that is guided by a heuristic cost function that accounts for both kinematic constraints of the vehicle and the topology of the vehiclepsilas free space. Configuration space obstacles are computed from an obstacle map acquired from a high definition laser range scanner and search is restricted to the collision free subset of the configuration space. The algorithm allows for solving all of the following problems: Precise parking maneuvers, narrow turns, long distance navigation. The system has been used successfully on board the autonomous car ANNIEWAY in the DARPA urban challenge competition of 2007.

Obstacle detection with a Photonic Mixing Device-camera in autonomous vehicles

In autonomous vehicles as well as in modern driver assistance systems, obstacle detection shows to be the most important task to be achieved. This paper presents a collision avoidance system, based on a modern Time-Of-Flight camera. These cameras allow a 3D perception of the environment, in which obstacles can be detected, independent of special features. Thus, the system is capable of all kinds of objects, including pedestrians as well as bicycles or vehicles. The used Photonic Mixing Device (PMD) camera has a measurement range of up to 50 m. The system is integrated into an autonomous vehicle, on which detected obstacles are investigated in detail. The vehicle steering commands are then generated by a behaviour network, depending on the presence of obstacles in the driving lane.

Path planning for cognitive vehicles using risk maps

This paper addresses the problem of path planning for a non-holonomic vehicle in fast changing urban environments. The developed method is flexible in utilization and generates collision-free paths with continuous curvature for a wide range of maneuvers such as regular lane-driving, rectangular or parallel parking and K-Turns. An A* algorithm is used as incremental planning method in combination with a kinematic model to follow non-holonomic constraints. A shortest-path estimate is used as optimization criteria. In the context of collision-free driving, the idea of using risk maps for representation of obstacles and other environmental influences is presented. Simulation and experiments show the applicability of this approach for lane driving as well as narrow navigation in parking spots. Further ideas of enhancements are discussed in the outlook of the paper.

Behavior Decision and Path Planning for Cognitive Vehicles using Behavior Networks

Behavior decision in human brains is a process which involves different regions of the brain, each one considering specific driving aspects. These regions interact with each other and are able to stimulate or suppress other areas. The executed behavior is a result of a fusion process and depends on the structure of the network and the motivations of individual behaviors. At the Institute of Interactive Diagnosis-and Servicesystems in Karlsruhe, a behavior based architecture, called Behavior Network, was developed and is used since several years to control walking machines. The used approach of coupling and modularization of behaviors on different layers with reactive or deliberative character can be transferred to cognitive vehicles, since the complexity of behavior interaction is also given here. This biologically-oriented method seems adequate to derive behaviors for driving and perception, which are often combinations of several sub-behaviors with different motivations (road-following, lane-keeping, speed control, collision avoidance). The attempt to control the vehicle with human-like behaviors has the advantage of good traceability of the executed manoeuvres, as well as the use of humans as teachers to parameterize behaviors. This paper describes how to implement a behavior network for road traffic with basic behaviors to execute safe driving manoeuvres, and how to ensure safety in certain situations by using fusion nodes.

COLLISION AVOIDANCE FOR COGNITIVE AUTOMOBILES USING A 3D PMD CAMERA

Abstract Collision avoidance is one of the most important capabilities for autonomous vehicles. During driving, collisions must be avoided in all situations. With the availability of 3d cameras which rely on the time-of-flight principle, it is possible to get a very rich perception of the environment. This paper shows, how obstacles can be detected in the vehicles surrounding using a 3d PMD-camera (photonic mixing device). The obstacle detection is composed of two separated steps. First, a segmentation and a clustering of pixels takes place. Secondly, each group of pixels is analyzed in order to decide whether it is an obstacle or not. The result of the detection is a list of obstacles which is then used for behavior execution. The execution is done with a behavior network and it generates recommendations for path planning.

Nonlinear model based control of a piezoelectric actuated hydraulic valve

Abstract In the last years piezoelectric actuators have proven to be more and more attractive for many applications with demands for very high speed, very high forces and minimal actuator size. Significant improvements in manufacturing technology of piezoelectric actuators and the electronic components necessary for their operation, are only some of the reasons for this trend. In this paper a nonlinear model based control of a piezoelectric actuated hydraulic valve is presented. The controller structure is developed on the basis of a very realistic nonlinear mathematical model of the actuators and valves physical behavior.

Inter-IC-Bus-Komponenten

Nachdem im vorigen Kapitel die Grundlagen des I2C-Busses erklart und einfache Routinen fur Lese- und Schreibzugriffe vorgestellt wurden, soll nun in diesem Kapitel auf die Ansteuerung spezieller I2C-Bausteine eingegangen werden.