Telemanipulation enhancement through user´s motion intention recognition and fixture assistance

In telemanipulation systems, assistance of virtual fixture can improve manipulation capability and dexterity. This assistance provides aids not only for path following, but also for reaching target and avoiding obstacles. Conventionally, these assistances are based on the environment information, 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 hard virtual fixture orthogonal to the path is applied. Or if the task is to position a target, an attractive force filed is produced. In order to successfully recognize user´s motion intention, a hidden Markov model (HMM) is developed to classify human actions, such as following path, positioning target and avoiding obstacles. The algorithm is tested on the simulation platform.