Robotics

Announcing Transitive 2.0!

Open-source robotics framework adds ClickHouse, Grafana, and Alertmanager for growing fleets beyond 50 robots.

Deep Dive

Transitive Robotics has announced Transitive 2.0, a major evolution of its open-source framework for full-stack robotics. The update marks a strategic pivot from focusing on transactional features for small fleets—like WebRTC video streaming and remote teleoperation—to providing the tools needed for monitoring and operating at scale. The core of this shift is the integration of three key technologies: ClickHouse for storing historic and time-series data, Grafana for data visualization, and Alertmanager for creating custom alerting systems. These features are already powering capabilities like the free Health Monitoring tool, offering significant new value for robotics companies with expanding fleets.

Previously, Transitive excelled at enabling direct, real-time interaction between a single operator and a single robot, a model that becomes unsustainable as fleets grow. Version 2.0 addresses this by enabling longitudinal and historic views of entire fleets, facilitating a transition from active, hands-on monitoring to passive, automated oversight. The goal is to support robotics companies in their 'second chapter of growth' beyond 50 robots, while maintaining the framework's founding principles of embeddability, ease of use, and fine-grained, namespaced access control. This release provides the foundational data infrastructure necessary for large-scale fleet management without sacrificing the reliability of its existing MQTTSync protocol and authentication features.

Key Points
  • Adds ClickHouse integration for scalable storage of historic and time-series robot fleet data.
  • Enables fleet-wide visualization via Grafana and custom alerting through Alertmanager for passive monitoring.
  • Targets the operational challenges of companies scaling their robot fleets beyond 50 units.

Why It Matters

Provides the critical data infrastructure for robotics companies to scale operations from dozens to hundreds of robots efficiently.