Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures.

Autonomous underwater systems are very important as they could be a remarkable advancement in areas such as defence, pollution monitoring, deep sea mining, meteorology, flora and fauna exploration, etc. A fastidious static and dynam-ical model of the system is necessary for the implementation of an effective control system. The hydrodynamic parameters of the vehicle should also be considered for developing an accurate model of the vehicle. This paper presents the design, analysis and fabrication of an Autonomous Underwater Vehicle (AUV) with a simple yet effective design having 5 degrees of freedom. This paper also describes the static and dynamic modelling of the AUV. Hydrodynamic coefficients of the system are obtained through analysis and its aberrations are interpreted by varying different variables. The proposed AUV is equipped with six thrusters in order to achieve felicitous manoeuverability. The use of Aluminium extrusions for the frame of the vehicle makes the thruster configuration easily reconfigurable.

Self-driving laboratories are redefining autonomous experimentation by integrating robotic manipulation, computer vision, and intelligent planning to accelerate scientific discovery. This work presents a vision-guided motion planning framework for robotic manipulators operating in dynamic laboratory environments, with a focus on evaluating motion smoothness and control stability. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic modeling of the manipulator is carried out using the screw theory formulations, which provides a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a vision pipeline that integrates feature-based detection with homography-driven depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles including velocity continuity, acceleration, jerk, and snap. Simulation studies demonstrate the robustness and adaptability of the proposed framework in autonomous experimentation workflows, highlighting its potential to enhance precision, scalability, and efficiency in next-generation self-driving laboratories.