High-Speed Autonomous Robotic Assembly Using In-Hand Manipulation and Re-Grasping

Kang, Tae-Woong; Yi, Jae-Bong; Song, Dongwoon; Yi, Seung‐Joon

doi:10.3390/app11010037

Cited by 15 publications

(6 citation statements)

References 31 publications

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“…The manipulator used in this work is a general-purpose 6degree-of-freedom serial-link cooperative manipulator composed of UR5e and an electric gripper [26,27]. The kinematic model of the manipulator used for path planning is established using the D-H parameter method based on the D-H parameters presented in Table 2.…”

Section: Model Of the Manipulatormentioning

confidence: 99%

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion

Xiao

Weng

et al. 2023

Sci. China Technol. Sci.

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This paper is concerned with the issue of path optimization for manipulators in multi-obstacle environments. Aimed at overcoming the deficiencies of the sampling-based path planning algorithm with high path curvature and low safety margin, a path optimization method, named NA-OR, is proposed for manipulators, where the NA (node attraction) and OR (obstacle repulsion) functions are developed to refine the path by iterations. In the iterations of path optimization, the node attraction function is designed to pull the path nodes toward the center of their neighbor nodes, thereby reducing the path curvature and improving the smoothness. Also, the obstacle repulsion function is developed to push the path nodes out of the potentially unsafe region by generating a repulsive torque on the path nodes, thus improving the safety margin of the motion. By introducing the effect of NA-OR, the optimized path has a significant improvement in path curvature and safety margin compared with the initial path planned by Bi-RRT, which meaningfully enhances the operation ability of manipulators for the applications that give a strong emphasis on security. Experimental results on a 6-DOF manipulator in 4 scenarios demonstrate the effectiveness and superiority of the proposed method in terms of the path cost, safety margin, and path smoothness.

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Section: Model Of the Manipulatormentioning

confidence: 99%

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion

Xiao

Weng

et al. 2023

Sci. China Technol. Sci.

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“…Staying within the same application domain but relaxing the constraint of using ROS2, the literature is more extensive, with several works where architectures/frameworks enabling robots to perform more advanced tasks through the use of middleware are proposed. In [53], an application that integrates a 3D perception module, a multi-layer motion planning module, and a real-time motion control module to autonomously perform a complex robotic assembly task is proposed without specifying the middleware used for its implementation. Similarly, in [54], a three-layer architecture (perception, planning, and control) is introduced for a human-robot cooperative assembly task.…”

Section: Ros2: Industrial Robotics Applicationsmentioning

confidence: 99%

Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey

Bonci,

Gaudeni,

Giannini

et al. 2023

Applied Sciences

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Future challenges in manufacturing will require automation systems with robots that are increasingly autonomous, flexible, and hopefully equipped with learning capabilities. The flexibility of production processes can be increased by using a combination of a flexible human worker and intelligent automation systems. The adoption of middleware software such as ROS2, the second generation of the Robot Operating System, can enable robots, automation systems, and humans to work together on tasks that require greater autonomy and flexibility. This paper has a twofold objective. Firstly, it provides an extensive review of existing literature on the features and tools currently provided by ROS2 and its main fields of application, in order to highlight the enabling aspects for the implementation of modular architectures to increase autonomy in industrial operations. Secondly, it shows how this is currently potentially feasible in ROS2 by proposing a possible high-level and modular architecture to increase autonomy in industrial operations. A proof of concept is also provided, where the ROS2-based framework is used to enable a cobot equipped with an external depth camera to perform a flexible pick-and-place task.

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“…We used a commercial Franka Emika Panda 7-DOF manipulator for this work, as the redundant DOF provides a much larger dexterous workspace compared to 6-DOF ones [25][26][27], which we found to be crucial for grasping objects in arbitrary orientation [28,29]. In addition, the manipulator's electrically adjustable compliance greatly helped prevent possible damage to the delicate robotic hand.…”

Section: Manipulator Armmentioning

confidence: 99%

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

Kim

Kang

et al. 2022

Applied Sciences

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This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results.

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High-Speed Autonomous Robotic Assembly Using In-Hand Manipulation and Re-Grasping

Cited by 15 publications

References 31 publications

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion

Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

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