CN-CBF: Composite Neural Control Barrier for Navigation

Autonomous navigation in dynamic environments remains one of the greatest challenges in robotics, especially when seeking to guarantee safety without sacrificing efficiency. Control barrier functions (CBFs) have proven to be an effective tool for filtering unsafe actions, but their manual design is complex and not very scalable. Recently, proposals have emerged that combine neural networks with composite CBFs, where multiple barriers are integrated into a single one using residual architectures, trained with data generated offline from Hamilton-Jacobi reachability theory. This approach allows approximating optimal safe sets for moving obstacles, significantly improving success rates compared to baseline methods. Experimental results with ground robots and quadcopters show increases of up to 18% in success, while maintaining efficient trajectories. From a business perspective, implementing these algorithms requires a solid technological ecosystem. At Q2BSTUDIO, as a software and technology development company, we offer artificial intelligence solutions for businesses that enable the integration of advanced control models into production systems. Our team works with custom applications and custom software tailored to the specific needs of each project, whether in robotics, industrial automation, or logistics. Furthermore, deploying these systems requires robust cloud infrastructure; that is why we provide AWS and Azure cloud services that scale the training and deployment of neural networks. Cybersecurity also plays a critical role when connecting robots to corporate networks, and our cybersecurity services protect both data and processes. For monitoring and data-driven decision-making, we apply business intelligence with Power BI, transforming navigation metrics into actionable insights. We even explore the use of AI agents to coordinate robot fleets in real time. We combine these capabilities to offer comprehensive solutions that go beyond the academic prototype, bringing research to real-world environments with measurable results.