The RPL has developed a groundbreaking method that leverages robotics to automate the preparation, characterization, and disposal of complex liquid samples for synchrotron coherent X-ray scattering experiments. Using a pendant drop technique shielded from air turbulence, the automated experiments achieved results consistent with traditional containers, making this approach suitable for high-precision studies. The robotic system, featuring an electronic pipette mounted on a robotic arm, enables precise sample handling and high-throughput exchange. By integrating a single Python script with beamline and robot control libraries, this enables seamless automation, paving the way for AI-driven, fully autonomous material design at large-scale scientific facilities. This approach enhances experimental efficiency and consistency, revolutionizing the study of complex fluids. Read the related paper.
In a recent paper entitled Flexible open-source automation for robotic engineering (Chroy et al. 2021), a new software package named Pyhamilton was presented which allows the user to program the actions of Hamilton STAR and STARlet liquid handling robots using standard Python. The capabilities of Pyhamilton were demonstrated by preforming several biological experiments including the use of a feedback loop to maintain culture turbidostats and a high-throughput perturbation analysis of metabolites.
This project aims to enable computer vision capabilities on a mobile robot in such a way that decisions can be made local to the robot in real time based on video input. This includes design and initial development of a vision API. This could include adding vision to UR5e arm so that QR-type tags could (1) identify fiducial positions to provide a coordinate system transformation to enable accurate interactions with mobile arm and things on static tables, and (2) identify things of interest called out by name in protocol: “beaker 7”, “start button”, “trash bin”