Transforming Robotics Research: RCCL's Migration to AWS

The Robotics, Automatic Control, and Cyber-Physical Systems Laboratory (RCCL) aimed for a high-performance cloud infrastructure to support their advanced research in robotics, cyber-physical systems, and IoT data analysis. To process and analyze vast amounts of real-time sensor data using machine learning techniques and MATLAB computations, RCCL turned to PCG to help them achieve this with AWS, seeking a scalable and secure platform capable of managing complex computational needs while enabling collaborative research. This AWS migration project provided RCCL with an optimized, secure, and cost-efficient cloud infrastructure that supports their advanced data and machine learning workflows.

The Robotics, Automatic Control, and Cyber-Physical Systems Laboratory (RCCL), based at the National and Kapodistrian University of Athens, focuses on advancing robotics, cyber-physical systems, and IoT research. By merging robotics and computational sciences, RCCL leads projects that address both theoretical challenges and practical applications in automated control and sensor-based systems. Through this AWS migration, RCCL has taken a significant step toward ensuring that its data and computational requirements align with the growing complexity of its research initiatives, enabling greater agility and collaboration across its research activities

As RCCL’s research and data demands grew, so did the need for a scalable and high-performance infrastructure capable of supporting real-time data processing, advanced machine learning, and computationally intensive MATLAB tasks. The lab required a cloud solution that could integrate IoT data from multiple devices, provide secure access to vast data volumes, and enable GPU-powered MATLAB computations. Another critical requirement was establishing an accessible environment for data sharing and collaboration among researchers while maintaining efficient data governance and cost-effective resource allocation.

Key challenges included:

• Handling high-frequency IoT sensor data across devices and robotic systems.
• Supporting high-performance computations with GPU-enabled environments for MATLAB tasks.
• Enabling scalable and secure data storage and processing for large volumes of research data.
• Facilitating machine learning model training and deployment to support AI-driven insights in cyber-physical systems.
• Maintaining cost efficiency while optimizing resource utilization and data management.

To address RCCL’s requirements, we developed an AWS migration plan to implement a fully integrated cloud infrastructure. The solution enabled RCCL to collect and process IoT data in real-time, execute MATLAB computations on GPU-accelerated instances, and efficiently manage large datasets. The project included the following components:

• Infrastructure Setup: AWS Control Tower was implemented to manage account governance, while VPC configurations, IAM policies, security groups, and NACLs established a secure and managed environment.
• MATLAB Computing Environment: High-performance EC2 instances with GPU acceleration were set up for MATLAB workloads, featuring MATLAB Parallel Server and MATLAB Production Server to effectively manage computational tasks.
• IoT Infrastructure: AWS IoT Core was configured to handle data ingestion and device management, ensuring real-time data availability from IoT devices across RCCL’s research projects.
• Data Management: S3 buckets and AWS Lake Formation enabled secure, structured data storage and retrieval.
• Machine Learning Infrastructure: SageMaker and Bedrock were used to train and deploy machine learning models, with full integration for MATLAB-driven data processing. Amazon OpenSearch Service used as the vector store for Bedrock.
• Analytics and Visualization: Real-time data processing was handled via EC2 instances running MATLAB, while QuickSight provided researchers with interactive data visualization capabilities.
• Monitoring and Operations: AWS CloudWatch, along with SNS and SES, ensured system monitoring and alerts, while X-Ray enabled performance analysis across services, and a backup and disaster recovery configuration provided data resilience.

Through the AWS migration, PCG helped RCCL establish a streamlined platform for research that enabled advanced data processing and real-time analysis across IoT and robotics applications. Key outcomes include:

• Real-Time Data Processing: The solution enabled real-time data ingestion from IoT devices, allowing RCCL to access and act on sensor data promptly, a critical component in robotics research.
• Enhanced Computational Capacity for MATLAB: GPU-enabled EC2 instances allowed RCCL to conduct MATLAB computations at significantly higher speeds, improving data processing times for complex models and simulations.
• Optimized Machine Learning Environment: SageMaker and Bedrock enabled RCCL to develop, train, and deploy machine learning models efficiently, enhancing RCCL’s capacity for predictive analytics and AI-driven insights in cyber-physical research.
• Data Security and Accessibility: By leveraging IAM, VPCs, and NACLs, RCCL’s research data is securely managed, with collaborative access available to authorized researchers across departments.
• Cost-Efficient Resource Utilization: Auto-scaling EC2 instances and optimized storage solutions, provided RCCL with flexible, on-demand resources that align with project requirements, reducing unnecessary costs.

Conclusions

RCCL’s migration to AWS marks a transformative phase in its research capabilities, equipping the lab with advanced tools for handling complex computational and machine learning workloads. This new cloud infrastructure enables RCCL to efficiently process, analyze, and visualize high-frequency IoT data, supports their high-performance MATLAB requirements, and promotes collaborative data access and management. With AWS's scalable and secure environment, RCCL can confidently focus on advancing their research in robotics and cyber-physical systems, knowing that the platform will grow and evolve alongside their expanding needs.

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