Model based assembly state estimation algorithm for the components of Tablet PC

Dong-Hyeong, Lee; Na, Minwoo; Kim, Young-Loul; Song, Jae-Bok

doi:10.1109/urai.2017.7992814

Cited by 1 publication

(3 citation statements)

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“…During the assembly process, the assembly may still fail even if the robot end-effector reaches the specified position. Reference [2] adopted a model-based assembly and established an assembly model according to the position data of the robot end-effector and the threshold value of the morphological contact force under a specific contact shape. Then the contact state and the assembly result can be obtained.…”

Section: Related Workmentioning

confidence: 99%

“…However, this method can only determine whether the assembly is successful or not. Min-Woo Na and Tac-Jung Kim proposed the assembly failure state modeling method based on [2] in [3]. The data of the torque sensor is used as the input, and the assembly failure state is used as the output.…”

Section: Related Workmentioning

confidence: 99%

“…The non-manual methods for assembly status detection can be divided into two categories, contact detection and noncontact detection. The contact detection methods are mainly based on torque sensors [2,3]. This requires the arrangement and dismantling of multiple measuring devices, and the process is complex.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assembly Status Detection Based on Step-by-step Strategy for Robotic Assembly

Chen

Bie

Yao

et al. 2022

J. Phys.: Conf. Ser.

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Assembly status detection is of great significance in the automated assembly of industrial robots. However, in addition to the traditional manual inspection, the existing methods for assembly status detection are mainly through methods such as torque sensors, etc., which are difficult to implement in practical assembly and lack the detection of the entire assembly cycle. To solve this problem, this paper proposes a method for assembly statute detection based on a step-by-step strategy. The first step is to use fast bounding box collision detection to determine whether the robot enters the correct assembly neighborhood. The second step is to use accurate pose information to determine whether the product is assembled correctly. The experiments show that the proposed method can accurately detect the assembly status for the entire assembly cycle with high assembly efficiency.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%