Qujiang Lei

Fast grasping of unknown objects using force balance optimization.

Grasping of unknown objects (neither appearance data nor object models are given in advance) is very important for robots that work in an unfamiliar environment. In this paper, in order to make grasping of unknown objects more reliable and faster, we propose a novel grasping algorithm which does not require to build a 3D model of the object. For most objects, one point cloud is enough. For other objects, at most two point clouds are enough to synthesize reliable grasp. Taking grasping range and width of robot hand into consideration, the most suitable grasping region can be calculated on the contour of the point cloud of unknown object by maximizing the coefficient of force balance. Further analysis of the point cloud in the best grasping region can obtain the grasping position and orientation of robot hand. The point cloud information is processed on line, the grasping algorithm can quickly get the grasping position and orientation and then drive robot to the grasping point to execute grasping action. Simulations and experiments on an Universal arm UR5 and an underactuated Lacquey Fetch gripper are used to examine the performance of the algorithm, and successful results are obtained.

Unknown object grasping using force balance exploration on a partial point cloud

Reducing the computing time for unknown object grasping while maintaining grasp stability is the goal of this paper. Inspired by the camera sensor distribution of the PR2 and Baxter robots, as well as active exploration for unknown object grasping, a novel unknown object grasping algorithm is proposed. This algorithm is based on two 3D sensors distributed like the PR2 and Baxter robots. Using the inputs from the two 3D sensors, a partial point cloud is constructed. Series of virtual viewpoints are allocated at intervals surround the principal component axis to build interval virtual object coordinate systems, from which force balance computation is carried out. The force balance is examined both in the XOY plane and the XOZ plane to guarantee the grasping stability. The hand configuration with the best force balance is returned as the final grasp configuration. Simulations based on a Universal robot arm and a Lacquey fetch gripper demonstrated favorable performance. Our algorithm can quickly process the partial point cloud and output the final grasp within 1 or 2 seconds (varying according to the point sets). The simulations demonstrated the effectiveness of our grasping algorithm.

Unknown object grasping by using concavity

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.

Object grasping by combining caging and force closure

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 objects boundary and considering the geometry property of the grippers, we propose to allocate a discrete set of finger candidates along the objects 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.

Fast grasping of unknown objects using cylinder searching on a single point cloud

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. The goal of this paper is to quickly synthesize an executable grasp for one unknown object by using cylinder searching on a single point cloud. Specifically, a 3D camera is first used to obtain a partial point cloud of the target unknown object. An original method is then employed to do post treatment on the partial point cloud to minimize the uncertainty which may lead to grasp failure. In order to accelerate the grasp searching, surface normal of the target object is then used to constrain the synthetization of the cylinder grasp candidates. Operability analysis is then used to select out all executable grasp candidates followed by force balance optimization to choose the most reliable grasp as the final grasp execution. In order to verify the effectiveness of our algorithm, Simulations on a Universal Robot arm UR5 and an under-actuated Lacquey Fetch gripper are used to examine the performance of this algorithm, and successful results are obtained.

A survey of unknown object grasping and our fast grasping algorithm-C shape grasping

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. In recent years, extensive research has been conducted in the domain of unknown object grasping and many successful grasping algorithms for unknown objects are created. However, So far there is not a very general fast grasping algorithm suits various kinds of unknown objects. Therefore, choice among different grasping algorithms becomes necessary for users. In order to make it more convenient for users to quickly understand and choose a suitable grasping algorithm, a survey about the latest research results of unknown object grasping is made in this paper. We compared different grasping algorithms with each other and obtained a table to clearly show the result of comparison. The comparison could give researchers meaningful information in order to quickly pick a grasping approach with their requirements. Meanwhile, we briefly showed our latest fast grasping algorithm which employs only a partial point cloud of the target object as input, and the grasping algorithm can quickly work out a suitable grasp for most objects within 2 seconds on a common personal computer. Simulations are used to examine the performance of our algorithm and successful results are obtained.

Fast C-shape grasping for unknown objects

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. In this paper, we propose an original fast grasping algorithm for unknown objects. The geometry of the under-actuated gripper is approximated as a C-shape, which is used to fit the point cloud of the target object to find a suitable grasp. In order to make the robot arm quickly execute the grasp found by the grasping algorithm, we made a comparison of the popular online motion planners. The motion planner with the highest solved runs, lowest computing time and the shortest path length is chosen to execute the grasp action. Simulations and experiments on a UR5 robot arm and an under-actuated gripper are used to examine the performance of the grasping algorithm, and successful results are obtained.

Fast grasping of unknown objects using principal component analysis

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.

Performance study of single-query motion planning for grasp execution using various manipulators

This paper identifies high performing motion planners among three manipulators when carrying out grasp executions. Simultaneously, this paper presents useful benchmarking data. Sampling-based motion planners of OMPL available for use in MoveIt! are compared by performing several grasping-related motion planning problems. The performance of the planners is measured by means of solved runs, computing time and path length. Based on the results, recommendations are made for planner choice that shows high performance for the used manipulators.

An empirical study of single-query motion planning for grasp execution

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.