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This paper introduces the Extensible Agent Behavior Specification Language (XABSL) as a pragmatic tool for engineering the behavior of autonomous agents in complex and dynamic environments. It is based on hierarchies of finite state machines (FSM) for action selection and supports the design of longterm and deliberative decision processes as well as of(More)
Specific behavior description languages prove to be suitable replacements to native programming language like C++ when the number and complexity of behavior patterns of an agent increases. The XML based Extensible Agent Behavior Specification Language (XABSL) also simplifies the process of specifying complex behaviors and supports the design of both very(More)
This paper presents a fast approach for edge-based self-localization in RoboCup. The vision system extracts edges between the field and field lines, borders, and goals following a grid-based approach without processing whole images. These edges are employed for the self-localization of the robot. Both image processing and self-localization work in real-time(More)
This paper presents a real-time approach for object recognition in robotic soccer. The vision system does not need any calibration and adapts to changing lighting conditions during run time. The adaptation is based on statistics which are computed when recognizing objects and leads to a segmentation of the color space to different color classes. Based on(More)
This paper explores how sensor and motion modeling can be improved to better Markov localization by exploiting deviations from expected sensor readings. Proprioception is achieved by monitoring target and actual motions of robot joints. This provides information about whether or not an action was executed as desired, yielding a quality measure of the(More)
We present a complete system for obstacle avoidance for a mobile robot. It was used in the RoboCup 2003 obstacle avoidance challenge in the Sony Four Legged League. The system enables the robot to detect unknown obstacles and reliably avoid them while advancing toward a target. It uses monocular vision data with a limited field of view. Obstacles are(More)
This paper explores how the absence of an expected sensor reading can be used to improve Markov localization. This negative information usually is not being used in localization, because it yields less information than positive information (i.e. sensing a landmark), and a sensor often fails to detect a landmark, even if it falls within its sensing range. We(More)