Integrated assembly and motion planning using regrasp graphs

Wan, Weiwei; Harada, Kensuke

doi:10.1186/s40638-016-0050-2

Cited by 21 publications

(15 citation statements)

References 32 publications

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“…Wan and Harada presented an integrated assembly and motion-planning system to search the assembly sequence with the help of a horizontal surface as the supporting fixture [20]. Kramberger et al proposed two novel algorithms that learned the precedence constraints and relative part-size constraints [8].…”

Section: Assembly Sequencementioning

confidence: 99%

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Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

196

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Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper.

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Section: Assembly Sequencementioning

confidence: 99%

“…• Chair assembly, that is, a robot integrates chair parts together with a fastener [18,19]. • Pick-and-place, that is, a robot picks up an object as the base and places it down on a fixture [17,20,21].…”

mentioning

confidence: 99%

Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

196

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“…1. Industrial robots with a tool changer where a gripper was selected depending on each task as [9], [10], [11], [12] have proposed manipulation planners combined with grasp planners, it is relatively recently where assembly planners combined with grasp planners have proposed [6], [7], [8]. Hereafter, we call such assembly planner as the grasp/assembly planner.…”

Section: Introductionmentioning

confidence: 99%

“…We believe that this is the first trial on adding a function of automatically selecting a gripper to a grasp/assembly planner. In our previous research, we have proposed a dynamic regrasp graph [8], [12] for solving a grasp/manipulation and grasp/assembly planning problems. On the other hand, this research newly assumes multiple grippers for the grasp/assembly planner proposed so far [8].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous research, we have proposed a dynamic regrasp graph [8], [12] for solving a grasp/manipulation and grasp/assembly planning problems. On the other hand, this research newly assumes multiple grippers for the grasp/assembly planner proposed so far [8]. We show that, by using our proposed grasp/assembly planner, it becomes possible for automatically selecting a gripper from multiple candidates to assemble a part.…”

Section: Introductionmentioning

confidence: 99%

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Tool Exchangeable Grasp/Assembly Planner

Harada

Nakayama

Wan

et al. 2018

Intelligent Autonomous Systems 15

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This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our assembly planner assumes multiple grippers and can automatically selects a feasible one to assemble a part. For a given AND/OR graph of an assembly task, we consider generating the assembly graph from which assembly motion of a robot can be planned. The edges of the assembly graph are composed of three kinds of paths, i.e., transfer/assembly paths, transit paths and tool exchange paths. In this paper, we first explain the proposed method for planning assembly motion sequence including the function of gripper exchange. Finally, the effectiveness of the proposed method is confirmed through some numerical examples and a physical experiment.

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Multi-dimensional Error Identification during Robotic Snap Assembly

Hayami

Shi

Wan

et al. 2019

Advances in Mechanism and Machine Science

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Existing methods for predicting robotic snap joint assembly cannot predict failures before their occurrence. To address this limitation, this paper proposes a method for predicting error states before the occurence of error, thereby enabling timely recovery. Robotic snap joint assembly requires precise positioning; therefore, even a slight offset between parts can lead to assembly failure. To correctly predict error states, we apply functional principal component analysis (fPCA) to 6D force/torque profiles that are terminated before the occurence of an error. The error state is identified by applying a feature vector to a decision tree, wherein the support vector machine (SVM) is employed at each node. If the estimation accuracy is low, we perform additional probing to more correctly identify the error state. Finally, after identifying the error state, a robot performs the error recovery motion based on the identified error state. Through the experimental results of assembling plastic parts with four snap joints, we show that the error states can be correctly estimated and a robot can recover from the identified error state.

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Integrated assembly and motion planning using regrasp graphs

Cited by 21 publications

References 32 publications

Robot Learning from Demonstration in Robotic Assembly: A Survey

Robot Learning from Demonstration in Robotic Assembly: A Survey

Tool Exchangeable Grasp/Assembly Planner

Multi-dimensional Error Identification during Robotic Snap Assembly

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