Peg-in-hole assembly based on hybrid vision/force guidance and dual-arm coordination

Zheng, Yanglong; Zhang, Xianmin; Chen, Yanlin; Huang, Yanjiang

doi:10.1109/robio.2017.8324453

Cited by 19 publications

(14 citation statements)

References 16 publications

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“…And it determines the magnitude of force through kinesthetic teaching. Besides, the dual-arm coordination robot adopts a hybrid assembly strategy based on vision/force guidance for pegin-hole assembly [27]. This method can be used in round, triangle, and square assembly parts with 0.5 mm maximum clearance.…”

Section: Introductionmentioning

confidence: 99%

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

et al. 2021

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The method of tactile perception can accurately reflect the contact state by collecting force and torque information, but it is not sensitive to the changes in position and posture between assembly objects. The method of visual perception is very sensitive to changes in pose and posture between assembled objects, but they cannot accurately reflect the contact state, especially since the objects are occluded from each other. The robot will perceive the environment more accurately if visual and tactile perception can be combined. Therefore, this paper proposes the alignment method of combined perception for the peg-in-hole assembly with self-supervised deep reinforcement learning. The agent first observes the environment through visual sensors and then predicts the action of the alignment adjustment based on the visual feature of the contact state. Subsequently, the agent judges the contact state based on the force and torque information collected by the force/torque sensor. And the action of the alignment adjustment is selected according to the contact state and used as a visual prediction label. Whereafter, the network of visual perception performs backpropagation to correct the network weights according to the visual prediction label. Finally, the agent will have learned the alignment skill of combined perception with the increase of iterative training. The robot system is built based on CoppeliaSim for simulation training and testing. The simulation results show that the method of combined perception has higher assembly efficiency than single perception.

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Section: Introductionmentioning

confidence: 99%

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

et al. 2021

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“…In Alpizar et al (2017), human demonstration and optimization-based motion planner were used to generate the robot motion trajectory of a dual-arm robot. In recent studies, vision/force guidance (Huang et al , 2017; Zheng et al , 2017) and two phases assembly schemes (Zhang et al , 2017a, 2017b) were used to realize the peg-in-hole assembly task by using a compliant dual-arm robot. The idea of master–slave coordination (MSC) was considered in the previous study (Zheng et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Peg-in-hole assembly based on master-slave coordination for a compliant dual-arm robot

Huang

Zheng

Wang

et al. 2020

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Purpose The paper aims to propose an assembly scheme based on master–slave coordination for a compliant dual-arm robot to complete a peg-in-hole assembly task. Design/methodology/approach The proposed assembly scheme is inspired by the coordinated behaviors of human beings in the assembly process. The left arm and right arm of the robot are controlled to move alternately. The fixed arm and the moving arm are distinguished as the slave arm and the master arm, respectively. The position control model is used at the uncontacted stage, and the torque control model is used at the contacted stage. Findings The proposed assembly scheme is evaluated through peg-in-hole assembly experiments with different shapes of assembly piece. The round, triangle and square assembly piece with 0.5 mm maximum clearance between the peg and the hole can be assembled successfully based on the proposed method. Furthermore, three assembly strategies are investigated and compared in the peg-in-hole assembly experiments with different shapes of assembly piece. Originality/value The contribution of this study is that the authors propose an assembly scheme for a compliant dual-arm robot to overcome the low positioning accuracy and complete the peg-in-hole assembly tasks with different shapes parts.

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“…15,16 The inserting assembly is one of classic assembly modes. 10,[17][18][19][20] According to fit type between components, the inserting assembly can be categorized into clearance assembly, transition assembly, and interference assembly. 7 In precision assembly, the interference assembly is widely used.…”

Section: Introductionmentioning

confidence: 99%

“…4,7 The aligning is usually completed based on visual features, 10 while the force-based control strategy is employed in inserting stage due to the visual features are blocked in inserting process. [18][19][20][21][22] Liu et al 19 proposed a high precision assembly control strategy. Firstly, the orientation alignment and position alignment are carried out separately.…”

Section: Introductionmentioning

confidence: 99%

“…Because most of the above precision assembly systems are equipped with multiple independent manipulators and the degree of freedom of each manipulator is limited, the orientation and position alignments are carried out sequentially. 4,7,[18][19][20][21] Obviously, the efficiency of the decoupled pose alignment is low. In the works above, the grasping control is not investigated, and the components to be assembled are put on the manipulators by human.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic precision robot assembly system with microscopic vision and force sensor

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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An automatic precision robot assembly system is established. The robot assembly system mainly consists of an industrial robot, three cameras, a micro force sensor, and a specific gripper. The industrial robot is a six-axis serial manipulator, which is used to conduct grasping and assembly subtasks. Two microscopic cameras are fixed on two high accuracy translational platforms to provide visual information in aligning stage for assembly. While one conventional camera is installed on the robotic end effector to guide the gripper to grasp component. The micro force sensor is installed on the robotic end effector to perceive the contacted forces in inserting stage. According to the characteristics of components, an adsorptive gripper is designed to pick up components. In addition, a three-stage "aligning-approaching-grasping" control strategy for grasping subtask and a two-stage "aligning-inserting" control strategy for assembly subtask are proposed. Position offset compensation is computed and introduced into aligning stage for assembly to make the grasped component in the microscopic cameras' small field of view. Finally, based on the established robot assembly system and the proposed control strategies, the assembly tasks including grasping and assembly are carried out automatically. With 30 grasping experiments, the success rate is 100%. Besides, the position and orientation alignment errors of pose alignment for assembly are less than 20 mm and 0.1 .

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Peg-in-hole assembly based on hybrid vision/force guidance and dual-arm coordination

Cited by 19 publications

References 16 publications

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

Peg-in-hole assembly based on master-slave coordination for a compliant dual-arm robot

Automatic precision robot assembly system with microscopic vision and force sensor

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