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首页> 外文会议>International Conference on Artificial Intelligence Planning and Scheduling; 2000414-17; Breckenridge,CO(US) >Vision-Servoed Localization and Behavior-Based Planning for an Autonomous Quadruped Legged Robot
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Vision-Servoed Localization and Behavior-Based Planning for an Autonomous Quadruped Legged Robot

机译：视觉伺服的本地化和基于行为的四足有腿机器人的计划

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摘要

Planning actions for real robots in dynamic and uncertain environments is a challenging problem. It is not viable to use a complete model of the world; it is most appropriate to achieve goals and handle uncertainty by integrating deliberation and behavior-based reactive planning. We successfully developed a system integrating perception and action for the RoboCup-99 Sony legged robot league. The quadruped legged robots are fully autonomous and thus must have onboard vision, localization and action selection. We briefly present our perception algorithm that automatically classifies and tracks colored blobs in real time. We then briefly introduce our Sensor Resetting Localization (SRL) algorithm which is an extension of Monte Carlo Localization. Vision and localization provide the state input for action selection. Our robust and sensible behavior scheme handles dynamic changes in information accuracy. We developed a utility-based system for using and acquiring location information. Finally, we have devised several special built-in plans to deal with times when urgent action is needed and the robot cannot afford to collect accurate location information. We present results using the real robots, which demonstrate the success of our approach. Our team of Sony quadruped legged robots, CMTrio-99, won all but one of its games in RoboCup-99, and was awarded third place in the competition.
机译：在动态和不确定的环境中为实际机器人计划动作是一个具有挑战性的问题。使用完整的世界模型是不可行的。通过整合审议和基于行为的被动计划，最适合实现目标和应对不确定性。我们为RoboCup-99索尼有腿机器人联盟成功开发了一种将感知和动作融为一体的系统。四足腿机器人是完全自主的，因此必须具有车载视觉，定位和动作选择。我们简要介绍了一种感知算法，该算法可以实时自动分类和跟踪彩色斑点。然后，我们简要介绍我们的传感器重置定位（SRL）算法，该算法是Monte Carlo定位的扩展。视觉和本地化为操作选择提供了状态输入。我们强大而明智的行为方案可处理信息准确性的动态变化。我们开发了一个基于实用程序的系统来使用和获取位置信息。最后，我们设计了一些特殊的内置计划，以处理需要采取紧急措施且机器人无力收集准确的位置信息的时间。我们使用真实的机器人展示了结果，证明了我们方法的成功。我们的Sony四足腿机器人CMTrio-99团队在RoboCup-99上赢得了除一场比赛以外的所有比赛，并在比赛中获得第三名。

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