Peg-in-hole assembly in live-line maintenance based on generative mapping and searching network

Wu, Wei; Zhou, Hui; Guo, Yu; Wu, Yanpeng; Guo, Jian

doi:10.1016/j.robot.2021.103797

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…Shandong University used the LS-YOLOv4 method to detect targets in the distribution network scene and calculate the grasping attitude [16]. Wei Wu et al [17] proposed a new semi-supervised learning network to carry out 2D hole searching by fusing visual detection signal and fuzzy force as the condition of state transition, thereby enabling the robot to independently complete the installation of lightning arrester.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent power distribution live‐line operation robot systems based on stereo camera

et al. 2023

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Maintenance tasks in distribution networks are often accompanied by hazards associated with high altitudes and high voltages. By utilising robots instead of human operators to perform these tasks, potential risks can be avoided, while productivity can be increased. This research proposes an intelligent power distribution live‐line operation robot (PDLOR) system based on a stereo camera to replace human to complete work. The PDLOR system consists of several key components, including dual manipulators, wireless tools, a visual perception system, an insulated bucket truck, and a ground control terminal. Once the task is confirmed, the real‐time vision system identification and positioning enable the adjustment of the insulated bucket to position the robot correctly for its intended work. The stereo camera plays a crucial role in accurately recognising and estimating the object's orientation. Additionally, a simplified reconstruction is performed within a virtual simulation environment, which aids in collision detection during path planning. After obtaining the optimal path, it is then communicated to the real manipulator for execution. To validate the feasibility of the PDLOR system, field experiments were conducted in actual distribution network scenarios. The results demonstrate that the PDLOR effectively completes single‐phase power‐line connection tasks within a remarkable 10‐min timeframe.

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Section: Introductionmentioning

confidence: 99%

“…Wei Wu et al. [17] proposed a new semi‐supervised learning network to carry out 2D hole searching by fusing visual detection signal and fuzzy force as the condition of state transition, thereby enabling the robot to independently complete the installation of lightning arrester.…”

Section: Introductionmentioning

confidence: 99%

Intelligent power distribution live‐line operation robot systems based on stereo camera

et al. 2023

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“…Kim et al [12] trained a position-force mapping model for a 3-mm square area using a clustering algorithm and achieved a model error of less than 0.35 mm. A peg-hole data model uses machine learning methods to train contact state data offline and predict the current state online [13][14][15]. This model is updated in real time for online learning through data input from the current contact state [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

et al. 2023

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Taking the gear assembly of the reducer assembly line as a research background, the holefinding strategy for robot assembly with keyed circular pegs is proposed to solve the problem of low holefinding efficiency and success rate. in the circular hole-finding task, deviation domains were divided based on a static mechanism, position vector trajectory optimization in the step distance and direction was designed, and a deviation domain-force mapping relationship was established using a genetic algorithm-support vector machine (GA-SVM) classification algorithm; the accuracy of this algorithm is approximately 90.00%. Thirty groups completed the circular hole-finding task in an average time of 5.4 s. For the square hole-finding task, dual monocular cameras were integrated to identify corner points of the flat key and keyway. Image semantic segmentation based on deep learning was used for the corner-point recognition of the flat key to suppress the effect of changes in light intensity; the recognition has an average error of 0.39 mm. Coarse and fine adjustment circumferential deflection strategies were adopted sequentially. The 30 groups of square holefinding tasks exhibited a 96.70% success rate in an average time of 10.2 s. The proposed hole-finding strategy improves the efficiency and success rate of the gear assembly.INDEX TERMS keyed circular peg; learning-based robot hole finding; support vector machine; image semantic segmentation based on deep learning; genetic algorithm

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“…Live-line work tasks [21,22] can be divided into two categories according to the type of contact used when a robot works. One type is flexible contact between a tool and an object, such as the contact between a wire connection tool and a wire during power line connection work, which can be accomplished via visual recognition, positioning, and robot planning [23].…”

Section: Introductionmentioning

confidence: 99%

“…One type is flexible contact between a tool and an object, such as the contact between a wire connection tool and a wire during power line connection work, which can be accomplished via visual recognition, positioning, and robot planning [23]. The other type is rigid contact, such as the process of assembling a peg hole between the copper connection terminal of a wire and a lightning arrester during arrester installation, a process that can only be completed under vision /force fusion [22].…”

Section: Introductionmentioning

confidence: 99%

6-DOF Bilateral Teleoperation Hybrid Control System for Power Distribution Live-Line Operation Robot

Chen

Wang²,

Tang³

et al. 2022

Machines

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In the master-slave heterogeneous teleoperation, the workspace of the slave manipulator is usually much larger than that of the master manipulator. This paper proposes a 6-DOF bilateral hybrid teleoperation control strategy to map the workspace of the manipulators without changing the operation accuracy. The hybrid control includes the admittance and force control based on the feedback of the force sensor at the end of the manipulator. The two control strategies switched autonomously through the positioning of the Sigma.7 handle in the workspace. Compared with the classic bilateral teleoperation control, it overcomes the limitation of pre-matching the workspace of the master and slave. When the tool contacts a rigid environment, the robot can make adaptive compensation through the admittance controller even if the operator has not responded. We conduct extensive experiments to evaluate the changes in displacement and velocity before and after the switching process and under different admittance controller parameters. Finally, teleoperation is applied to live-line operation in distribution networks. The experiment proved that the control strategy is more consistent with human operation habits and can improve assembly success rate and efficiency.

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Peg-in-hole assembly in live-line maintenance based on generative mapping and searching network

Cited by 9 publications

References 23 publications

Intelligent power distribution live‐line operation robot systems based on stereo camera

Intelligent power distribution live‐line operation robot systems based on stereo camera

Hole-Finding Learning Strategy for a Robot Assembly With Keyed Circular Peg

6-DOF Bilateral Teleoperation Hybrid Control System for Power Distribution Live-Line Operation Robot

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