Increasing grasping efficiency is very important for the robots to grasp unknown objects especially subjected to unfamiliar environments. To achieve this, a new algorithm is proposed based on the C-shape configuration. Specifically, the geometric model of the used under-actuated gripper is approximated as a C-shape. To obtain an appropriate graspable position, this C-shape configuration is applied to fit geometric model of an unknown object. The geometric model of unknown object is constructed by using a single-view partial point cloud. To examine the algorithm using simulations, a comparison of the commonly used motion planners is made. The motion planner with the highest number of solved runs, lowest computing time and the shortest path length is chosen to execute grasps found by this grasping algorithm. The simulation results demonstrate that excellent grasping efficiency is achieved by adopting our algorithm. To validate this algorithm, experiment tests are carried out using a UR5 robot arm and an under-actuated gripper. The experimental results show that steady grasping actions are obtained. Hence, this research provides a novel algorithm for fast grasping of unknown objects.@en

According to the report by the United Nations in 2015, the global population of older persons aged 60 years or over is predicted to grow to 1.4 billion by 2030. A rapidly aging population poses a challenging problem for human beings, i.e. supply shortage of working-age people. To solve this problem, increasing research efforts are poured into the field of robotics, especially in service robotics. Service robots are believed to be a solid solution to the challenging problem of an aging population. The Strategic Research Agenda (SRA) for Robotics in Europe, a development guideline for European robotics from 2014 to 2020, classifies robots’ functions into eight basic categories, i.e., assembly, surface process, interaction, exploration, transporting, inspection, grasping and manipulation. From SRA, we can find that grasping is an important basic function for robots. Combining grasping with other basic functions, robots can perform many service tasks to free humans from tedious housework, for example, cleaning rooms, cooking and washing dishes. @en

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.@en

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@en

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.@en

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.

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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.

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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.

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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.

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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.

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Abstract poster: https://personalrobotics.ri.cmu.edu/RSS2013Uncertainty/abstract5.pdf@en