Interpretation of force and moment signals for compliant peg-in-hole assembly

Newman, Wyatt S.; Zhao, Yu; Pao, Yoh‐Han

doi:10.1109/robot.2001.932611

Cited by 75 publications

(45 citation statements)

References 8 publications

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“…Newman et al [5] calculate the moments from sensors and interprets the current position of the peg by mapping the moments onto positions. Sharma et al [4] utilize depth profile in addition to roll and pitch data to interpret the current position of the peg.…”

Section: A Search Phasementioning

confidence: 99%

Deep reinforcement learning for high precision assembly tasks

Inoue

Magistris

Munawar

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

284

153

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Abstract-High precision assembly of mechanical parts requires accuracy exceeding the robot precision. Conventional part mating methods used in the current manufacturing requires tedious tuning of numerous parameters before deployment. We show how the robot can successfully perform a tight clearance peg-in-hole task through training a recurrent neural network with reinforcement learning. In addition to saving the manual effort, the proposed technique also shows robustness against position and angle errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the robot sensors to estimate the system state. The advantages of our proposed method is validated experimentally on a 7-axis articulated robot arm.

show abstract

Section: A Search Phasementioning

confidence: 99%

Deep reinforcement learning for high precision assembly tasks

Inoue

Magistris

Munawar

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

284

153

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show abstract

“…Some researchers proposed a force/torque map, shown in Fig. 1 (b), which presents the forces and torques on all possible contact positions to find the displacement between peg and hole centers [5,6]. …”

Section: Problem Description and State-of-artmentioning

confidence: 99%

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Abdullah

Roth

Weyrich

et al. 2015

MATEC Web of Conferences

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Abstract. This paper presents an approach for estimating contact positions of peg-in-hole assembling application using data models created from Force/Torque sensor inputs. The assembling task is carried by an industrial robot where the F/T sensor is mounted between the robot and the gripper to measure six values of forces and torques during the contact between the peg and the hole. The search approach to estimate the current position of the contact point is implemented based on human like intuitive behavior by maneuvering the peg on the hole's edge. Different models were created and evaluated using experiments results acquired from pervious assembling attempts. Assembling steps, data analysis and models results are presented in this research.

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“…For instance, a peg-in-hole assembly may be completed by "dumb" searches in which the peg to be inserted is moved around a candidate hole position (either randomly, or by a structured geometric pattern like rasters [12] or spirals [23]) until it can be pushed in. Alternatively, more explicit methods can be employed that intelligently probe the candidate hole position and, based on the effector position and force moments, accurately identify the location and orientation of the hole into which the peg will be inserted [24]. Other assemblies that are more specific in nature (for example, automobile clutch assemblies) can be parameterized and generalized [22], or broken down into separate motion primitives [23].…”

Section: Assembly Strategiesmentioning

confidence: 99%

Best Practices and Performance Metrics Using Force Control for Robotic Assembly

Marvel¹,

Falco²

2012

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Interpretation of force and moment signals for compliant peg-in-hole assembly

Cited by 75 publications

References 8 publications

Deep reinforcement learning for high precision assembly tasks

Deep reinforcement learning for high precision assembly tasks

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Best Practices and Performance Metrics Using Force Control for Robotic Assembly

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