Telemanipulation Assistance Based on Motion Intention Recognition
Telemanipulation Assistance Based on Motion Intention Recognition
复制标题
基于运动意图识别的遥控辅助
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
N. Pernalete
中科院分区：
文献类型：
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作者：
Wentao Yu;Redwan Alqasemi;R. Dubey;N. Pernalete
在远程操作系统中，通过可变位置/速度映射或虚拟夹具的辅助可以提高操作能力和灵活性[3，5，6，7，8]。传统上，这种辅助是基于环境的传感数据，而不知道用户的运动意图。在本文中，用户的运动意图与实时环境信息相结合，适用于适当的援助。如果当前任务遵循路径，则应用虚拟夹具。如果任务是将末端执行器与目标对准，则产生吸引力场。类似地，如果任务是避开阻挡路径的障碍物，则会生成排斥力场。为了成功地识别用户的运动意图，隐马尔可夫模型（HMM）为基础的算法来分类人类的行动，如以下的路径，对齐目标和避免障碍物。
In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, user’s motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.