A Shared Autonomy Approach for Wheelchair Navigation Based on Learned User Preferences
A Shared Autonomy Approach for Wheelchair Navigation Based on Learned User Preferences
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DOI：
10.1109/iccvw.2017.176
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Yizhe Chang;Mohammed Kutbi;Nikolaos Agadakos;Bo Sun;Philippos Mordohai
中科院分区：
文献类型：
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作者：
Yizhe Chang;Mohammed Kutbi;Nikolaos Agadakos;Bo Sun;Philippos Mordohai
机器人轮椅的研究范围很广，从完全自主到共享自主，再到通过操纵杆或其他方式进行手动导航。共享自主权是有价值的，因为它允许用户和机器人相互补充，纠正彼此的错误并避免碰撞。在本文中，我们提出了一种方法，可以学习复制路径选择根据轮椅用户的个人，往往是主观的，标准，以减少用户在自动导航过程中进行干预的次数。这是通过使用支持向量机学习对路径进行排名来实现的，该支持向量机是在模拟器中根据用户做出的选择进行训练的。如果分类器对排名靠前的路径的置信度高，则在不请求用户确认的情况下执行分类器。否则，选择将被推迟到用户。使用两种路径生成策略的仿真和实验室实验证明了我们的方法的有效性。
Research on robotic wheelchairs covers a broad range from complete autonomy to shared autonomy to manual navigation by a joystick or other means. Shared autonomy is valuable because it allows the user and the robot to complement each other, to correct each other's mistakes and to avoid collisions. In this paper, we present an approach that can learn to replicate path selection according to the wheelchair user's individual, often subjective, criteria in order to reduce the number of times the user has to intervene during automatic navigation. This is achieved by learning to rank paths using a support vector machine trained on selections made by the user in a simulator. If the classifier's confidence in the top ranked path is high, it is executed without requesting confirmation from the user. Otherwise, the choice is deferred to the user. Simulations and laboratory experiments using two path generation strategies demonstrate the effectiveness of our approach.