Kinematic Analysis and Integration of Vision Algorithms for a Mobile Manipulator Employed Inside a Self-Driving Laboratory

Recent advances in robotics and autonomous systems have broadened the use of robots in laboratory settings, including automated synthesis, scalable reaction workflows, and collaborative tasks in self-driving laboratories (SDLs). This paper presents a comprehensive development of a mobile manipulator designed to assist human operators in such autonomous lab environments. Kinematic modeling of the manipulator is carried out based on the Denavit Hartenberg (DH) convention and inverse kinematics solution is determined to enable precise and adaptive manipulation capabilities. A key focus of this research is enhancing the manipulator ability to reliably grasp textured objects as a critical component of autonomous handling tasks. Advanced vision-based algorithms are implemented to perform real-time object detection and pose estimation, guiding the manipulator in dynamic grasping and following tasks. In this work, we integrate a vision method that combines feature-based detection with homography-driven pose estimation, leveraging depth information to represent an object pose as a $2$D planar projection within $3$D space. This adaptive capability enables the system to accommodate variations in object orientation and supports robust autonomous manipulation across diverse environments. By enabling autonomous experimentation and human-robot collaboration, this work contributes to the scalability and reproducibility of next-generation chemical laboratories

A Learning-based Model Reference Adaptive Controller Implemented on a Prosthetic Hand Wrist

The functionality and natural motion of prosthetic hands remain limited by the challenges in controlling compliant wrist mechanisms. Current control strategies often lack adaptability and incur high computational costs, which impedes real-time deployment in assistive robotics. To address this gap, this study presents a computationally efficient Neural Network (NN)-based Model Reference Adaptive Controller (MRAC) for a tendon-driven soft continuum wrist integrated with a prosthetic hand. The dynamic modeling of the wrist is formulated using Timoshenko beam theory, capturing both shear and bending deformations. The proposed NN-MRAC estimates the required tendon forces from deflection errors and minimizes deviation from a reference model through online adaptation. Simulation results demonstrate improved precision with a root mean square error (RMSE) of $6.14 \times 10^{-4}$ m and a settling time of $3.2$s. Experimental validations confirm real-time applicability, with an average RMSE of $5.66 \times 10^{-3}$ m, steady-state error of $8.05 \times 10^{-3}$ m, and settling time of $1.58$ s. These results highlight the potential of the controller to enhance motion accuracy and responsiveness in soft prosthetic systems, thereby advancing the integration of adaptive intelligent control in wearable assistive devices.