A peg-in-hole robot assembly system based on Gauss mixture model

Song, Jingzhou; Chen, Qingle; Li, Zhendong

doi:10.1016/j.rcim.2020.101996

Cited by 52 publications

(11 citation statements)

References 8 publications

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“…In comparison, whole-process location mapping [38] requires a user to operate all trajectories, including movement, grasping, obstacle avoidance, and placement, which is a heavy burden on the user. In some situations, automatic operation can be achieved, such as peg-in-hole assembly [39] with complex requirements. But some scenes require a user to participate.…”

Section: Resultsmentioning

confidence: 99%

Gesture- and vision-based automatic grasping and flexible placement in teleoperation

Zhao

Chen

et al. 2022

Int J Adv Manuf Technol

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Teleoperation system has attracted a lot of attention because of its advantages in dangerous or unknown environment.It is very difficult to develop an operating system that can complete complex tasks in an completely autonomous. This paper proposes a robot arm control strategy based on gesture and visual perception. The strategy combines the advantages of humans and robots to obtain a convenient and flexible interaction model. The hand data were obtained by Leap-Motion. Then a neural network algorithm was used to classify the nine gestures used for robot control by a finite state machine. The control mode switched between indicative control and mapping control. The robot acquired a autonomous grasp ability by incorporating YOLO 6D, depth data, and a probabilistic roadmap planner algorithm.The robot completed most of the trajectory independently, and a few flexible trajectories required a user to make mapping actions. This interactive mode reduces the burden of the user to a certain extent, that makes up for the shortcomings of traditional teleoperation.

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Section: Resultsmentioning

confidence: 99%

Gesture- and vision-based automatic grasping and flexible placement in teleoperation

Zhao

Chen

et al. 2022

Int J Adv Manuf Technol

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“…Visual servoing is used for picking pose compensation and grasping in [50], and for placing pose compensation in an assembly cell in [51]. Furthermore, compliance control strategy is applied for pegin-hole insertion in [52].…”

Section: Tolerance Factors In Robotic Pick-and-placementioning

confidence: 99%

Tolerance analysis for robotic pick-and-place operations

Tipary

Erdös

2021

Int J Adv Manuf Technol

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Robotic workcell design is a complex process, especially in case of flexible (e.g., bin-picking) workcells. The numerous requirements and the need for continuous system validation on multiple levels place a huge burden on the designers. There are a number of tools for analyzing the different aspects of robotic workcells, such as CAD software, system modelers, or grasp and path planners. However, the precision aspect of the robotic operation is often overlooked and tackled only as a matter of manipulator repeatability. This paper proposes a designer tool to assess the precision feasibility of robotic pick-and-place workcells from the operation point of view. This means that not only the manufacturing tolerances of the workpiece and the placing environment are considered, but the tolerance characteristics of the manipulation and metrology process (in case of flexible applications) as well. Correspondingly, the contribution of the paper is a novel tolerance modeling approach, where the tolerance stack-up is set up as a transformation chain of low-order kinematic pairs between the workpiece, manipulator, and other workcell components, based on manipulation, seizing, releasing, manufacturing, and metrology tolerances. Using this representation, the fulfillment of functional requirements (e.g., picking or placing precision) can be validated based on the tolerance range of corresponding chain members. By having a generalized underlying model, the proposed method covers generic industrial pick-and-place applications, including both conventional and flexible ones. The application of the method is presented in a semi-structured pick-and-place scenario.

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“…Therefore, the estimated pose of the robot can be expressed as µ G = (x G , y G , θ G ). Figure 3 shows an example of the positioning process where the mobile robot G detects four reflectors A, B, C and D. The estimated position of the robot can be obtained by Equations ( 1) and (2). And the position weight of the particle can be calculated as follows:…”

Section: Position Weight Calculationmentioning

confidence: 99%

“…Mobile manipulation is an important issue in the field of robotics. Traditionally, though the breaking down of sophisticated tasks into many single tasks, fixed-base manipulators, such as palletizing robots [ 1 ] and assembly robots [ 2 ], can complete tasks with high precision via pre-programming. Emerging applications in the industry, such as material handling and mobile welding, demand high mobility and flexibility of robots.…”

Section: Introductionmentioning

confidence: 99%

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Zhou

Chou

Tuo

et al. 2020

Sensors

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Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.

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A peg-in-hole robot assembly system based on Gauss mixture model

Cited by 52 publications

References 8 publications

Gesture- and vision-based automatic grasping and flexible placement in teleoperation

Gesture- and vision-based automatic grasping and flexible placement in teleoperation

Tolerance analysis for robotic pick-and-place operations

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

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