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Intuitive programming of robot manipulators (INTROP)

Intuitive programming of robot manipulators (INTROP)
机器人操纵器的直观编程 (INTROP)
批准号:
250579273
负责人:
Professor Dr. Dominik Henrich
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2014
资助国家:
德国
项目状态:
已结题
起止时间:
2013-12-31 至 2018-12-31

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中文摘要
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英文摘要
Robot manipulators have several advantages, such as strength, speed, endurance, accuracy, and reproducibility. Up to date, these advantages could not be harnessed for a variety of potential applications. These applications are - in addition to the classical field of production - particularly in the areas of service, entertainment and household. They are characterized by relatively few repetitions of the task execution and by a changing work environment. Therefore, the robots will only be rational usable if the current effort for setting-up is significantly reduced and if the execution is supported by environment perception. The setup efforts are mainly influenced by time- and cost-consuming programming of the robot, which for many potential applications is still too expensive (even for experts). In addition, the programming required for sensor-based task executions is particularly high.The aim of the project INTROP is the investigation of new, intuitive programming concepts that enable users without prior programming knowledge to program sensor-based robot manipulators for their applications quickly and easily. The user shall give the robot intuitively "the hand" and lead him through the desired task. He may also activate the robot's tool (e.g. a gripper) or activate environmental sensors (e.g. a camera). Here not only simple tasks are regarded, consisting of a sequence of actions, but also complex tasks with situational variations and repetitions of executions. Compared to previous approaches, the focus here is on intuitively programming imperative control structures (e.g. commands, conditionals, and repetitions) and on intuitively programming declarative behaviours (such as moving, dodging, searching, and sorting). The programming requires only one task demonstration, may subsequently be corrected by the user, and does not need additional hardware (e.g. VR glasses or gloves).
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Flexible human-robot cooperation with shared task representation (FlexCobot)
Verbal instructing of sensor-based robots (VerbBot)
Online CAD reconstruction with hand-held depth cameras (HandCAD-2)
国内基金
海外基金
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  • 项目类别:
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