arXiv subjects

Francesco Schetter

Publications and source records attributed to Francesco Schetter.

6 recordsLinked to original sources

arXiv2026

Admittance-Based Motion Planning with Vision-Guided Initialization for Robotic Manipulators in Self-Driving Laboratories

Self driving laboratories (SDLs) are highly automated research environments that leverage advanced technologies to conduct experiments and analyze data with minimal human involvement. These environments often involve delicate laboratory equipment, unpredictable environmental interactions, and occasional human intervention, making compliant and force aware control essential for ensuring safety, adaptability, and reliability. This paper introduces a motion-planning framework centered on admittance control to enable adaptive and compliant robotic manipulation. Unlike conventional schemes, the proposed approach integrates an admittance controller directly into trajectory execution, allowing the manipulator to dynamically respond to external forces during interaction. This capability enables human operators to override or redirect the robot's motion in real time. A vision algorithm based on structured planar pose estimation is employed to detect and localize textured planar objects through feature extraction, homography estimation, and depth fusion, thereby providing an initial target configuration for motion planning. The vision based initialization establishes the reference trajectory, while the embedded admittance controller ensures that trajectory execution remains safe, adaptive, and responsive to external forces or human intervention. The proposed strategy is validated using textured image detection as a proof of concept. Future work will extend the framework to SDL environments involving transparent laboratory objects where compliant motion planning can further enhance autonomy, safety, and human-robot collaboration.

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arXiv2026

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

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.

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arXiv2026

An Adaptive Neuro-Controller Developed for a Prosthetic Hand Wrist

The significance of employing a controller in prosthetic hands cannot be overstated, as it plays a crucial role in enhancing the functionality and usability of these systems. This paper introduces an adaptive neuro-controller specifically developed for a tendon-driven soft continuum wrist of a prosthetic hand. Kinematic and dynamic modeling of the wrist is carried out using the Timoshenko beam theory. A Neural Network (NN) based strategy is adopted to predict the required motor currents to manipulate the wrist tendons from the errors in the deflection of the wrist section. The Timoshenko beam theory is used to compute the required tendon tension from the input motor current. A comparison of the adaptive neuro-controller with other similar controllers is conducted to analyze the performance of the proposed approach. Simulation studies and experimental validations of the fabricated wrist are included to demonstrate the effectiveness of the controller.

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arXiv2026

A Hybrid Model-based and Data-based Approach Developed for a Prosthetic Hand Wrist

The incorporation of advanced control algorithms into prosthetic hands significantly enhances their ability to replicate the intricate motions of a human hand. This work introduces a model-based controller that combines an Artificial Neural Network (ANN) approach with a Sliding Mode Controller (SMC) designed for a tendon-driven soft continuum wrist integrated into a prosthetic hand known as "PRISMA HAND II". Our research focuses on developing a controller that provides a fast dynamic response with reduced computational effort during wrist motions. The proposed controller consists of an ANN for computing bending angles together with an SMC to regulate tendon forces. Kinematic and dynamic models of the wrist are formulated using the Piece-wise Constant Curvature (PCC) hypothesis. The performance of the proposed controller is compared with other control strategies developed for the same wrist. Simulation studies and experimental validations of the fabricated wrist using the controller are included in the paper.

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arXiv2026

A Sliding Mode Controller Based on Timoshenko Beam Theory Developed for a Tendon-Driven Robotic Wrist

Development of dexterous robotic joints is essential for advancing manipulation capabilities in robotic systems. This paper presents the design and implementation of a tendon-driven robotic wrist joint together with an efficient Sliding Mode Controller (SMC) for precise motion control. The wrist mechanism is modeled using a Timoshenko-based approach to accurately capture its kinematic and dynamic properties, which serve as the foundation for tendon force calculations within the controller. The proposed SMC is designed to deliver fast dynamic response and computational efficiency, enabling accurate trajectory tracking under varying operating conditions. The effectiveness of the controller is validated through comparative analyses with existing controllers for similar wrist mechanisms. The proposed SMC demonstrates superior performance in both simulation and experimental studies. The Root Mean Square Error (RMSE) in simulation is approximately 1.67e-2 radians, while experimental validation yields an error of 0.2 radians. Additionally, the controller achieves a settling time of less than 3 seconds and a steady-state error below 1e-1 radians, consistently observed across both simulation and experimental evaluations. Comparative analyses confirm that the developed SMC surpasses alternative control strategies in motion accuracy, rapid convergence, and steady-state precision. This work establishes a foundation for future exploration of tendon-driven wrist mechanisms and control strategies in robotic applications.

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arXiv2025

Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach

The integration of advanced control strategies into prosthetic hands is essential to improve their adaptability and performance. In this study, we present an implementation of a Model Predictive Control (MPC) strategy to regulate the motions of a soft continuum wrist section attached to a tendon-driven prosthetic hand with less computational effort. MPC plays a crucial role in enhancing the functionality and responsiveness of prosthetic hands. By leveraging predictive modeling, this approach enables precise movement adjustments while accounting for dynamic user interactions. This advanced control strategy allows for the anticipation of future movements and adjustments based on the current state of the prosthetic device and the intentions of the user. Kinematic and dynamic modelings are performed using Euler-Bernoulli beam and Lagrange methods respectively. Through simulation and experimental validations, we demonstrate the effectiveness of MPC in optimizing wrist articulation and user control. Our findings suggest that this technique significantly improves the prosthetic hand dexterity, making movements more natural and intuitive. This research contributes to the field of robotics and biomedical engineering by offering a promising direction for intelligent prosthetic systems.

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