Preparatory Manipulation Planning Using Automatically Determined Single and Dual Arm

Wan, Weiwei; Harada, Kensuke; Kanehiro, Fumio

doi:10.1109/tii.2019.2892772

Cited by 39 publications

(20 citation statements)

References 29 publications

(51 reference statements)

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“…Previously, many projects were developed using grasping poses planned by the algorithms in this article. These projects use the planned grasps to iteratively select start and goal poses in the robot workspace [43]- [45], build manipulation graphs [46]- [50], or optimize object assembly sequences [51]- [53]. Although the proposed algorithms were heavily used in these projects, they were not carefully introduced or analyzed.…”

Section: Relations To Our Previous Studiesmentioning

confidence: 99%

“…The proposed grasp planning is a preprocessing component that prepares preannotated grasp poses. The preannotated grasps are used by a task and motion planning component to build a manipulation graph [46] for selecting grasps and generating robot motion. This section presents some real-world examples developed based on the system, with a special focus on grasp planning.…”

Section: Some Real-world Examples Using the Grasp Plannermentioning

confidence: 99%

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Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

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This article develops model-based grasp planning algorithms. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering computer aided design (CAD) models of target objects. The developed algorithms can stably find many high-quality grasps, with satisfying precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which preprocesses a mesh model by peeling it into superimposed facets. The algorithms use the facets to locate contacts and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters are prepared to adapt the algorithms to meet various requirements. The experimental section studies the influence of the tunable parameters and analyzes the cost, precision, and robustness of the proposed algorithms and their planned grasps, with both simulations and real-world systems. Besides, the proposed algorithms are applicable to mesh models reconstructed from point clouds obtained by depth sensors. Some experiments and analysis are also carried out to study and demonstrate the ability. Index Terms-Grasping, grippers and other end-effectors, manipulation planning. I. INTRODUCTIONT HIS article develops algorithms to plan grasping configurations automatically. It focuses on industrial end-effectors, and plans grasp poses for these end-effectors considering computer aided design (CAD) models of target objects.Developing grasp planning algorithms is vital to manufacturing using "teachingless" robotic manipulators. Modern robotic manipulation systems use manually annotated or pretaught grasp configurations to perform specific tasks, which is not only costly Manuscript

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Section: Relations To Our Previous Studiesmentioning

confidence: 99%

Section: Some Real-world Examples Using the Grasp Plannermentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

Self Cite

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“…Raessa et al [18] proposed a method to teach a dual-arm robot to use common electric tools. Grasp and regrasp planning were implemented to adjust the work pose of the tool following [19]. Sanchez et al [20] developed a planner with orientation constraints to manipulate a tethered tool.…”

Section: Grasp and Regrasp Planningmentioning

confidence: 99%

“…The minimum θ end can be computed using Equation (17). p should meet equations (18) and (19). h should meet equation (20).…”

Section: Size Optimizationmentioning

confidence: 99%

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Designing a Mechanical Tool for Robots With Two-Finger Parallel Grippers

Wan

Harada

2019

IEEE Robot. Autom. Lett.

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This work designs a mechanical tool for robots with 2-finger parallel grippers, which extends the function of the robotic gripper without additional requirements on tool exchangers or other actuators. The fundamental kinematic structure of the mechanical tool is two symmetric parallelograms which transmit the motion of the robotic gripper to the mechanical tool. Four torsion springs are attached to the four inner joints of the two parallelograms to open the tool as the robotic gripper releases. The forces and transmission are analyzed in detail to make sure the tool reacts well with respect to the gripping forces and the spring stiffness. Also, based on the kinematic structure, variety tooltips were designed for the mechanical tool to perform various tasks. The kinematic structure can be a platform to apply various skillful gripper designs. The designed tool could be treated as a normal object and be picked up and used by automatically planned grasps. A robot may locate the tool through the AR markers attached to the tool body, grasp the tool by selecting an automatically planned grasp, and move the tool from any arbitrary pose to a specific pose to grip objects. The robot may also determine the optimal grasps and usage according to the requirements of given tasks.

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Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

Huo,

Hu,

Wang

et al. 2024

Advanced Intelligent Systems

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The robotic manipulation of deformable linear objects is a frontier problem with many potential applications in diverse industries. However, most existing research in this area focuses on shape control for a provided explicit goal and does not consider physical constraints, which limits its applicability in many real‐world scenarios. In this study, a self‐supervised planning and control approach are proposed to address the challenge of rearranging deformable linear objects for implicit goals. Specifically, the context of making both ends of the object reachable (inside the robotic access range) and graspable (outside potential collision regions) by dual‐arm robots is considered. Firstly, the object is described with sequential keypoints and the correspondence‐based action is parameterized. Secondly, a generator capable of producing multiple explicit targets is developed, which adhere to implicit conditions. Thirdly, value models are learnt to assign the most promising explicit target as guidance and determine the goal‐conditioned action. All models within the policy are trained in a self‐supervised manner based on data collected from simulations. Importantly, the learned policy can be directly applied to real‐world settings since we do not rely on accurate dynamic models. The performance of the new method is validated with simulations and real‐world experiments.

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Preparatory Manipulation Planning Using Automatically Determined Single and Dual Arm

Cited by 39 publications

References 29 publications

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Designing a Mechanical Tool for Robots With Two-Finger Parallel Grippers

Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

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