Industrial robotics is a continuously developing area of in-depth robotics research, as industrial robots have demonstrated to possess advantages in the robotic automation solutions in the industrial automation applications. In this paper, a novel automatic seam bead grinding robot manipulator with the integration of machining and measuring is proposed and an experimentation platform is developed for the robotic removal of the seam weld beads at welded pipe ends. Also, a robotic grinding motion planning methodology, which consists of a robotic operation process motion planning approach and a robotic seam bead grinding method, is presented for the appropriate motion planning and applications of the operational processes. Furthermore, the effectiveness of the robotic grinding motion planning methodology and the superiorly operational manipulation performance of the proposed robot manipulator are verified through a robotic grinding experiment for the removal of external seam weld beads at a helical welded pipe end.

Hybrid robotic application is a continuously developing field, as hybrid robot manipulators have demonstrated clearly to possess benefits of both serial structures and parallel mechanisms. In this article, the physical prototype and the specially designed control system for a new 5-degree-of-freedom hybrid robot manipulator are developed. The mechanical structure, kinematics, dynamics, and control system of this robot manipulator are presented. A newly appropriate set of closed-form solutions, which can avoid multiple solutions, is proposed to address the inverse kinematics problem of this robot manipulator. Additionally, simulations of the kinematics and dynamics of this robot manipulator and the tests for the repeatability and accuracy of both the position and path are first simulated with two numerical examples and then conducted on the physical prototype.

Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is presented for a newly developed five-degree-of-freedom hybrid robot manipulator. A hybrid robot manipulator with dexterous manipulation and two experimental platforms of robot manipulators are presented. A robotic deburring tool path planning method is proposed for the robotic deburring tool position and orientation planning and the robotic layered deburring planning. Also, a robotic deburring process parameter control method is proposed based on fuzzy control. Furthermore, a dexterous manipulation verification experiment is conducted to demonstrate the dexterous manipulation and the orientation reachability of the robot manipulator. Additionally, two robotic deburring experiments are conducted to verify the effectiveness of the two proposed methods and demonstrate the highly efficient and dexterous manipulation and deburring capacity of the robot manipulator.

The dexterity characteristics and dexterous workspace features are applied as the considerable and key kinematics properties to evaluate the manipulator performance. In this paper, a novel method of the dexterity analysis with the character of graphic dexterity and a new concept of the spatial mapping envelope surface are proposed for a 5-degree of freedom (DOF) manipulator. Additionally, for an arbitrary selected point and a dexterous workspace, the dexterity characteristics and the dexterity conditions are drawn, respectively. Finally, the dexterous workspace of this 5-DOF manipulator is obtained and it can be utilized as the basis research of this manipulator in future such as the structure optimization and motion control.

In unordered stacked scenarios, object overlap and occlusion hinder accurate grasp detection, while improved detection accuracy alone cannot guarantee safe robotic grasping. Meanwhile, existing methods mainly predict discrete gripper configurations and often neglect inter-object manipulation relationships, which may result in collisions and consequent grasp failure. To address this issue, this article proposes a real-time pixel-level grasp detection method tailored for unordered stacked scenarios. First, a novel grasp representation is proposed to represent gripper configurations, enabling better observation of the mapping of the gripper’s grasp positions on the object during network prediction. Then, a grasp path labeling method is proposed to streamline pixel-level dataset labeling. Subsequently, the multi-scale feature fusion adaptive perceptual network (MFFAP-Net) is proposed, which processes each image within 13 ms. The network takes RGB images as input, extracts and processes features through a combination of hybrid attention pyramid and multi-scale fusion decoder, and outputs the grasp configurations of each pixel. Finally, the optimal grasp points are screened by an object relationship detection module. The network achieves 99.4% grasp detection accuracy on the Cornell dataset and demonstrates robustness to unseen objects and environmental variations. Grasping experiments conducted on a UR10e collaborative robot achieve an average grasp success rate of 96.81% in single-object scenarios, 94.86% in cluttered scenarios, and 73.67% in stacked scenarios.