This paper identifies high-performing Open Motion Planning Library (OMPL) planners for grasp execution and simultaneously presents useful benchmark data. Four grasp executions were defined using a UR5 manipulator. The performance was measured by means of solved runs, computing time and path length. Based on the results, planners are recommended and the reasons are discussed.

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

This paper identifies high-performing Open Motion Planning Library (OMPL) planners for grasp execution and simultaneously presents useful benchmark data. Four grasp executions were defined using a UR5 manipulator. The performance was measured by means of solved runs, computing time and path length. Based on the results, planners are recommended and the reasons are discussed.

The current research trends of object grasping can be summarized as caging grasping and force closure grasping. The motivation of this paper is to combine the advantage of caging grasping and force closure grasping to enable under-actuated grippers like the Lacquey gripper and the parallel grippers like the PR2 gripper to quickly grasp the flat unknown objects. Inspired by the idea that caging grasping generates finger points along the object's boundary and considering the geometry property of the grippers, we propose to allocate a discrete set of finger candidates along the object's boundary. Any two of the finger candidates can form a grasp candidate, which is analyzed by using force closure to choose the best grasp candidate as the final grasp execution. The grasp quality during the manipulation of the object is guaranteed by considering the gravity of the object. Simulations and experiments on an Universal arm UR5 and an under-actuated Lacquey Fetch gripper are used to examine the performance of this algorithm, and successful results are obtained.

Reducing the grasp candidates for unknown object grasping while maintaining grasp stability is the goal of this paper. In this paper, we propose an efficient and straight forward unknown object grasping method by using concavities of the unknown objects to significantly reduce the grasp candidates. Shortest path concavity is first employed to work out the concavity value for every vertex of the unknown objects followed by concavity extraction to obtain the most salient concave areas. Grasp candidates are then generated on the most salient concave areas and evaluated by using force balance computation. Grasp candidates are ranked according to the result of force balance computation and the manipulability of every grasp candidate. The grasp with the best force balance and manipulability is chosen as the final grasp. In order to verify the effectiveness of our algorithm, some unknown objects commonly used by other papers about unknown object grasping are used to do simulations and favorable performance is obtained.