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论文类型：期刊论文

发表时间：2013-12-01

发表刊物：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：11

期号：6

页面范围：1241-1252

ISSN号：1598-6446

关键字：Attention selection; GMM; mobile robots; task-specific attention; visual servo control

摘要：This paper proposes a visual attention servo control (VASC) method which uses the Gaussian mixture model (GMM) for task-specific applications of mobile robots. In particular, low dimensional bias feature template is obtained using GMM to get an efficient attention process. An image-based visual servo (IBVS) controller is used to search for a desired object in a scene through an attention system which forms a task-specific state representation of the environment. First, task definition and object representation in semantic memory (SM) are proposed, and bias feature template is obtained using GMM deduction for features from high dimension to low dimension. Second, the features intensity, color, size and orientation are extracted to build the feature set. Mean shift method is used to segment the visual scene into discrete proto-objects. Given a task-specific object, top-down bias attention is evaluated to generate the saliency map by combining with the bottom-up saliency-based attention. Third, a visual attention servo controller is developed to integrate the IBVS controller and the attention system for robotic cognitive control. A rule-based arbitrator is proposed to switch between the episodic memory (EM)-based controller and the IBVS controller depending on whether the robot obtains the desired attention point on the image. Finally, the proposed method is evaluated on task-specific object detection under different conditions and visual attention servo tasks. The obtained results validate the applicability and usefulness of the developed method for robotics.