Autonomous Manipulation of Hazardous Chemicals and Delicate Objects in a Self-Driving Laboratory: A Sliding Mode Approach

TL;DR

Problem: safe, precise manipulation of fragile labware in autonomous chemistry labs under dynamic disturbances. Approach: a model-based sliding mode controller (MBSMC) using a hyperbolic tangent to suppress chattering and enhance robustness for a UR5e on a mobile Ridgeback platform. Contributions: comparative evaluation against NMBSMC and PID shows substantially smoother trajectories, lower control effort, and improved joint and Cartesian accuracy, validated in both simulation and experiments. Significance: enables reliable handling of hazardous chemicals and delicate instruments in dynamic laboratory environments, advancing autonomous lab automation.

Abstract

Precise handling of chemical instruments and materials within a self-driving laboratory environment using robotic systems demands advanced and reliable control strategies. Sliding Mode Control (SMC) has emerged as a robust approach for managing uncertainties and disturbances in manipulator dynamics, providing superior control performance compared to traditional methods. This study implements a model-based SMC (MBSMC) utilizing a hyperbolic tangent function to regulate the motion of a manipulator mounted on a mobile platform operating inside a self-driving chemical laboratory. Given the manipulator's role in transporting fragile glass vessels filled with hazardous chemicals, the controller is specifically designed to minimize abrupt transitions and achieve gentle, accurate trajectory tracking. The proposed controller is benchmarked against a non-model-based SMC (NMBSMC) and a Proportional-Integral-Derivative (PID) controller using a comprehensive set of joint and Cartesian metrics. Compared to PID and NMBSMC, MBSMC achieved significantly smoother motion and up to 90% lower control effort, validating its robustness and precision for autonomous laboratory operations. Experimental trials confirmed successful execution of tasks such as vessel grasping and window operation, which failed under PID control due to its limited ability to handle nonlinear dynamics and external disturbances, resulting in substantial trajectory tracking errors. The results validate the controller's effectiveness in achieving smooth, precise, and safe manipulator motions, supporting the advancement of intelligent mobile manipulators in autonomous laboratory environments.

Figures (14)

• Figure 1: System overview and experimental setup showing mobile manipulator, electrolyte mixing system, fixed manipulator, and ECMS machine
• Figure 2: Mobile manipulator (a)Frames of manipulator (b)Workspace of manipulator
• Figure 3: Methodology and control framework
• Figure 4: SMC controller framework for UR5e manipulator
• Figure 5: Motion of manipulator (base is stationary)