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Multi-Representation Multi-Heuristic A* Motion Planning for a Dual-Arm Underwater Vehicle Manipulation System

机译:用于双臂水下车辆操作系统的多模特多启发式A *运动规划

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Recently, Intervention-Autonomous Underwater Vehicles (I-AUVs), started to grab researchers attention. While prior systems rely on variations of the task-priority redundancy control framework, our recent research demonstrated experimental results using search-based motion planning for a single arm floating-based intervention in known and unknown environments. In this work, we present a search-based motion planning algorithm applied to the coordinated manipulation problem of a dual-arm Intervention AUV, in an object transportation mission (e.g. pipe transportation). Two Challenges are considered: the high-dimensionality of the system, and the motion coordination between the mobile base and both working arms. The latter challenge is a major one if accurate execution is required, especially considering the floating nature of the AUV. Our approach relies on exploiting the loose coupling between the different system components. We show for the first time the use of a search-based planner on a dual-arm underwater manipulator. In addition, we support our claims with a simulated demonstration, successfully performing a coordinated dual-arm underwater intervention.
机译:最近,干预自主水下车(I-AUV)开始抓取研究人员的注意。虽然先前的系统依赖于任务优先级冗余控制框架的变体,但我们最近的研究表明,使用基于搜索的运动规划在已知和未知环境中使用基于搜索的运动规划的实验结果。在这项工作中,我们介绍了一种基于搜索的运动规划算法,其应用于双臂干预AUV的协调操作问题,在对象运输任务(例如管道运输)中。考虑了两个挑战:系统的高度,以及移动基地与工作臂之间的运动协调。如果需要准确执行,后一种挑战是主要的挑战,特别是考虑到AUV的浮动性质。我们的方法依赖于利用不同系统组件之间的松散耦合。我们首次展示在双臂水下机械手上使用基于搜索的计划员。此外,我们还支持模拟演示的索赔,成功执行协调双臂水下干预。

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