Addressing the challenges of strong coupling, nonlinearity, and time-varying characteristics in underwater manipulator systems, along with the inevitable environmental disturbances such as gravity, buoyancy, and hydrodynamic forces that reduce system state convergence speed and affect control performance, an underwater manipulator dynamic model integrating the Lagrange equation, Morison equation, and nonlinear friction forces is built. A linear active disturbance rejection control (LADRC) strategy incorporating a hierarchical cascade linear extended state observer (HCLESO) is proposed. According to LADRC theory, the underwater manipulator system is approximated as an independent subsystem, which reduces the coupling effect between each joint of the manipulator arm. To address the incomplete estimation issue of the Linear Extended State Observer (LESO), a HCLESO is designed to estimate the total system disturbance, suppress high-frequency measurement noise, and reduce the disturbance observation residual, thereby improving the control precision and robustness of the underwater manipulator. The simulation results indicate that the proposed control strategy significantly improves system performance in terms of smaller control inputs, reduced overshoot, and lower steady-state errors. Compared to conventional PD control, the proposed strategy exhibits superior control accuracy and faster response. Compared to the standard LADRC, the introduction of HCLESO provides notable enhancements. With HCLESO ( p = 2), the IAE for joints q ₁ and q ₂ is reduced by 50.1% and 64.0%, respectively, while the ITAE decreases by 36.2% and 50.0%. Further improvements are observed with HCLESO ( p = 3), where the IAE for joints q ₁ and q ₂ is reduced by 50.1% and 65.1%, respectively, and the ITAE by 46.2% and 57.8%.

This paper presents a structural–parametric synthesis of the four-link and Stephenson I, Stephenson II, and Stephenson III six-link path-generating mechanisms. The four-link path-generating mechanism is formed by connecting the output point and the base using an active closing kinematic chain (CKC) with two DOFs and a negative CKC of the type RR. The six-link path-generating mechanisms are formed by connecting the output point and the base by active, passive and negative CKCs. Active CKC has active kinematic pair, passive CKC has zero DOF, and negative CKC has a negative DOF. Active and negative CKCs impose geometrical constraints on the movement of the output point, and the geometric parameters of their links are determined by least-square approximation. Geometric parameters of the passive CKC are varied to satisfy the geometrical constraints of the active and negative CKCs. The CKCs of the active, passive and negative types, connecting the output point and the base, are the structural modules from which the different types of the path-generating mechanisms are synthesized. Numerical examples of the parametric synthesis of the four-link and six-link path-generating mechanisms are presented.

In recent years, the global industrial production and manufacturing paradigm from mass production has continued to shift to customized production. Enterprise order processing is also increasingly showing strong timeliness, variety, small batch, and batch characteristics. The market demand for highly flexible robotic automated production lines is also continuing to grow. Mobile manipulator is a new type of robot that integrates two functions of mobile robots and robotic arms, which can plan routes, navigate accurately, avoid obstacles, and identify, sort, grasp, and transport items. It is widely used in logistics and warehousing, factories, indoor exhibition halls, etc., which can save the cost of manpower, improve efficiency, and create differentiated competitiveness. Despite its promising application, it also faces some problems, especially in the cooperative operation of the mobile platform and robotic arm. Understanding how to realize vision-based target intelligent recognition and grasping is still a current research challenge. In this paper, we build a mobile manipulator platform, deploy and verify a YOLOv8n-SCS-CE lightweight detection network proposed in the previous stage (which can detect common logistics parcels), and test the robot’s autonomous mobile grasping of parcels and autonomously navigating to the target place. The test demonstrates that it can utilize the improved YOLOv8 to achieve intelligent grasping and autonomous navigation, thereby solving the challenges of intelligent grasping and autonomous transportation for indoor mobile manipulators. This study provides key technologies and methodologies for the intelligent grasping and manipulation capabilities of mobile manipulators.