The increasing demand for precision, reproducibility, and scalability in scientific research has driven the development of advanced robotic systems for laboratory automation. This thesis presents the design, implementation, and validation of a mobile robotic platform, SIMO, capable of operating within semi-structured laboratory environments. The primary objective of this work is to address the challenges associated with achieving long-term reliability and precision in robotic manipulation and to explore the potential of integrating robotics with intelligent sampling techniques to optimize high-dimensional experimental problems.

The first part of the thesis investigates the integration of Computer Vision (CV) and Force Feedback systems to enhance the robustness and precision of robot-instrument interactions. A novel approach that combines these technologies is developed, enabling the SIMO platform to achieve millimetric precision in handling standard laboratory microplates and interacting with various instruments. Experimental validation demonstrates a 95% success rate in complex handling tasks, even under challenging conditions, marking a significant improvement over existing methods.

Building on this foundation, the second part focuses on applying the SIMO platform to automate the measurement of the Critical Micelle Concentration (CMC), a Materials Science process tipically performed manually. The development of the first automated sequential CMC determination protocol resulted in an 80% reduction in standard deviation compared to manual methods, highlighting substantial gains in precision and reproducibility. This application demonstrates the broader potential of mobile robots to perform high-throughput experimentation with greater precision and consistency than conventional approaches.

The third part of the thesis explores the integration of intelligent sampling techniques with robotic automation to optimize enzymatic reactions. A novel method for quantifying enzyme digestion efficiency is developed, which is combined with Machine Learning (ML) algorithms to predict optimal experimental conditions. This approach significantly reduces the number of experiments required to model complex biological processes, accelerating the discovery process and enhancing research efficiency.

Overall, this thesis advances the field of Laboratory Automation by demonstrating the capability of mobile robots to perform precise, reliable, and flexible operations in dynamic research environments. The results provide a pathway for future developments in robotic automation, including expanding the range of compatible laboratory instruments, enhancing experimental discoveries trough ML, and fostering seamless human-robot collaboration. The integration of sustainable practices and adaptive, intelligent sampling techniques presents a compelling vision for the future of autonomous laboratories, capable of transforming scientific research across multiple disciplines.