JM
Jonathan Meijer
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
5 records found
1
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.
...
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.
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
...
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
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.
...
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.
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.
...
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.
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.
...
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.