Research on Autonomous Grasping Technology Based on Vision The grasping operation is limited by visual observational conditions, operator level, etc., in the process of robot teleoperation. In response to this problem, this paper proposes a method of autonomous grasping based on vision. A depth camera is carried by the robot to obtain the color and depth information of the environment. The operator selects the target with a box on the monitor to determine the approximate range of the target. Combining the target box, depth information and color information, the image foreground is extracted to obtain the plane contour and depth information of the target. Then, the pose data of the target are calculated, and the grasping position is estimated and converted into a position in the robot coordinate system. After the grasping position is determined, the grasping trajectory of the manipulator is planned and optimized, combined with the environmental obstacle information. Finally, the manipulator is controlled to complete the grasping process. Experimental results show that the autonomous trajectory planning of the robot is continuous and controllable and that the grasping process can be completed efficiently with high accuracy in the recognition and positioning processes.

An assisted telemanipulation approach: combining autonomous grasp planning with haptic cues This paper presents an assisted telemanipulation approach with integrated grasp planning. It also studies how the human teleoperation performance benefits from the incorporated visual and haptic cues while manipulating objects in cluttered environments. The developed system combines the widely used masterslave teleoperation with our previous modelfree and learningfree grasping algorithm by means of a dynamic grasp reranking strategy and a semiautonomous reachtograsptrajectory guidance. The proposed reranking metric helps in dynamically updating the stable grasps based on the current state of the slave device. The trajectory guidance system assists in maintaining smooth trajectory by controlling the haptic forces. A virtual pose controller has been integrated with the guidance scheme to automatically correct the endeffector orientation while reaching towards the grasp. Various experiments are conducted evaluating the proposed method using a six degrees of freedom (dof) haptic master and a seven dof slave robot. Results obtained with these tests along with the results gathered from the performed humanfactor trials demonstrate the efficiency of our method in terms of objective metrics of task completion, and also subjective metrics of user experience.