A fully integrated autonomous laboratory operating system — combining precision robotics, multi-modal sensing, and AI-driven experimental control in a single unified architecture.
ATLAS (Autonomous Technology for Laboratory Automation and Science) is BRAINLAB's flagship robotic platform. Now in its second generation, it represents six years of iterative development toward a single goal: a laboratory that designs, executes, and learns from its own experiments without human intervention.
The system integrates mechanical, electronic, chemical, and computational subsystems into a seamless, modular architecture — deployable in research lab, pilot plant, or distributed multi-site configurations.
Three specialized robotic subsystems work in coordinated concert, each optimized for its role in the autonomous chemistry workflow.
Six-axis collaborative robotic arm with 0.02 mm repeatability. Equipped with interchangeable end-effectors for pipetting, solid handling, vial manipulation, and instrument docking. Payload: 3 kg.
96-channel parallel liquid handler with volume range 0.5–1000 µL per channel. Integrated with gravimetric verification, viscosity compensation, and dead-volume minimization algorithms.
32-channel bipotentiostat with integrated cell array. Supports CV, EIS, CA, CP, and GEIS protocols. Built-in reference electrode management, nitrogen purging, and temperature control.
Rotate, zoom, and examine the geometry, holes, and edges of a mechanical component, rendered directly from its CAD design.
Every ATLAS campaign follows a continuous closed-loop cycle — from AI-driven experiment design through robotic execution, real-time analysis, and model-guided iteration.
Every physical parameter is monitored continuously, enabling precise reaction control and rich multi-modal datasets for machine learning.
All 24 channels stream simultaneously at up to 10 kHz, feeding a high-performance time-series database with automated quality control and anomaly flagging.
ATLAS OS is the software backbone connecting hardware, sensors, data, and AI into a unified intelligent control system. Built on open standards and designed for extensibility.
ROS2-based orchestration layer with real-time scheduling, collision avoidance, and hot-swap module support.
BoTorch-based multi-objective optimizer with custom acquisition functions and constraint handling.
Physics-informed simulation environment for experiment pre-validation and predictive maintenance.
REST and WebSocket APIs for external model integration, remote monitoring, and inter-lab data federation.
We welcome collaborative access proposals from academic research groups, industrial R&D teams, and technology partners. Contact us to discuss your project.