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Titlebook: RoboCup 2002: Robot Soccer World Cup VI; Gal A. Kaminka,Pedro U. Lima,Raúl Rojas Conference proceedings 2003 Springer-Verlag Berlin Heidel

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51#
發(fā)表于 2025-3-30 09:11:23 | 只看該作者
Improving Vision-Based Self-localizationses how removal of the wall affects the localization task in ., both for vision-based and non-visual approaches, and argues that vision-based Monte Carlo localization based on landmark features seems to cope well with the changed field setup. An innovative approach for landmark feature detection for
52#
發(fā)表于 2025-3-30 14:18:45 | 只看該作者
Evaluation of Self-localization Performance for a Local Vision Robot in the Small Size Leaguee. Using an omni-directional vision system on a mobile robot, we originally developed a self-localization method based on imaging of the floor region. However, we could not explore its full potential because of a quantization error in the images. Therefore, we developed a self-localization method th
53#
發(fā)表于 2025-3-30 20:33:54 | 只看該作者
Fast Image Processing and Flexible Path Generation System for RoboCup Small Size League the goal point and generate the action under the team strategy. This paper proposes two new methods. One is a fast image processing method, which is coped with the spatial variance of color parameters in the field, to extract the positions of robots and ball in 1/30 sec. The separation problem in t
54#
發(fā)表于 2025-3-30 21:37:55 | 只看該作者
55#
發(fā)表于 2025-3-31 03:36:56 | 只看該作者
56#
發(fā)表于 2025-3-31 06:51:48 | 只看該作者
57#
發(fā)表于 2025-3-31 10:56:45 | 只看該作者
Learning the Sequential Coordinated Behavior of Teams from Observationsinate with them, assist them, or counter their actions. Typically, agent modeling techniques assume the availability of a plan- or behavior-library, which encodes the full repertoire of expected observed behavior. However, recent applications areas of agent modeling raise challenges to the assumptio
58#
發(fā)表于 2025-3-31 14:32:41 | 只看該作者
Towards a Life-Long Learning Soccer Agentvements. We applied hierarchical reinforcement learning in an SMDP framework learning on all levels simultaneously. As our experiments show, learning simultaneously on the skill level and on the skill selection level is advantageous since it allows for a smooth adaption to a changing environment. Fu
59#
發(fā)表于 2025-3-31 21:01:50 | 只看該作者
Adaptive Synchronisation for a RoboCup Agentes. The method balances its reliance upon noisy evidence with internal representations, making it robust to interaction faults caused by both communication and timing. The notion of action correctness is developed and used to analyse the new method as well as two special cases: Internal and External
60#
發(fā)表于 2025-4-1 01:20:31 | 只看該作者
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