The learning-based optimization algorithm for robotic dual peg-in-hole assembly

Hou, Zhimin; Philipp, Markus; Zhang, Kuangen; Guan, Yong Liang; Chen, Ken; Xu, Jing

doi:10.1108/aa-03-2018-039

Cited by 21 publications

(14 citation statements)

References 18 publications

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“…The policies include but are not limit to scanning search, spiral research, impedance control, hybrid force/position control, etc. [2] [3] [4], which need force sensors [5], tactile sensors [6] [7], or current sensors for feedback. Compliant mechanisms are hardware alternatives of the policies [8] [9].…”

Section: Reducing Uncertainty Using Placement and Regraspmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Hu¹,

Wan²,

Koyama³

et al. 2021

Preprint

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This paper presented an regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF's geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method's ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method's robustness and efficacy.

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Section: Reducing Uncertainty Using Placement and Regraspmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Hu¹,

Wan²,

Koyama³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The learning approaches provide an alternate to avoid hard coding as we do not have to establish a complex dynamic model for the interactive task (Hou et al, 2018). Gullapalli et al (1994) proposed a stochastic real-valued (SRV) reinforcement learning algorithm to learn the high-precision PiH insertion skill.…”

Section: Introductionmentioning

confidence: 99%

Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism

Liu

Zhou²,

Liu

et al. 2020

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Purpose This paper aims to enable the robot to obtain human-like compliant manipulation skills for the peg-in-hole (PiH) assembly task by learning from demonstration. Design/methodology/approach A modified dynamic movement primitives (DMPs) model with a novel hybrid force/position feedback in Cartesian space for the robotic PiH problem is proposed by learning from demonstration. To ensure a compliant interaction during the PiH insertion process, a Cartesian impedance control approach is used to track the trajectory generated by the modified DMPs. Findings The modified DMPs allow the robot to imitate the trajectory of demonstration efficiently and to generate a smoother trajectory. By taking advantage of force feedback, the robot shows compliant behavior and could adjust its pose actively to avoid a jam. This feedback mechanism significantly improves the dynamic performance of the interactive process. Both the simulation and the PiH experimental results show the feasibility and effectiveness of the proposed model. Originality/value The trajectory and the compliant manipulation skill of the human operator can be learned simultaneously by the new model. This method adopted a modified DMPs model in Cartesian space to generate a trajectory with a lower speed at the beginning of the motion, which can reduce the magnitude of the contact force.

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“…Cheng and Chen (2014) proposed to use Gaussian process regression to model the relation between parameters and performance, which may be used in the online configuration. Hou et al (2018) proposed to use the learningbased optimization algorithm to reduce the assembly time and smooth the contact forces during the assembly process.…”

Section: Introductionmentioning

confidence: 99%

Robotic assembly for tight tolerant cascaded peg-in-hole process with incomplete constraints

Cheng

Liu

Zhang

et al. 2020

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Purpose Installing a tight tolerant stepped shaft is not a trivial task for an industrial robot. If all peg-hole constraints are complete, the cascaded peg-in-hole task can be simplified into several independent stages and accomplished one by one. However, if some of the constraints are incomplete, the cross stage interference will bring additional difficulties. This paper aims to discuss the cascaded peg-in-hole problem with incomplete constraints. Design/methodology/approach In this paper, the problem is formulated according to geometric parameters of the stepped shaft and completeness of the corresponding hole. The possible jamming type is modeled and analyzed. A contact modeling and control strategy is proposed to compensate the peg postures under incomplete constraints. Findings The above methods are implemented on an experiment platform and the results verify the effectiveness of the proposed robotic assembly strategy. Originality/value Based on force/torque sensor, a hybrid control strategy for incomplete constraints cascaded peg-in-hole assembly problem is proposed.

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The learning-based optimization algorithm for robotic dual peg-in-hole assembly

Cited by 21 publications

References 18 publications

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism

Robotic assembly for tight tolerant cascaded peg-in-hole process with incomplete constraints

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