The Edge AIR project, carried out by Energy Robotics, addresses the growing need for safer, more efficient, and automated inspection solutions in industrial environments such as chemical plants and energy infrastructures.
These industries depend on frequent inspections for safety and operational continuity, but traditional methods are labor-intensive, costly, and expose personnel to hazardous conditions. Demographic shifts have further reduced the availability of qualified workers, while increasingly dynamic, remote, and complex sites require more flexible solutions.
Edge AIR aimed to address these challenges by developing a robot-agnostic, AI-driven software platform that enables fleets of mobile robots to autonomously perform routine inspections. Unlike vendor-specific solutions, the platform was designed to integrate with robots from different manufacturers and connect with industrial systems through open interfaces. This ensures interoperability, scalability, and adaptability across a wide range of industrial use cases.
A core innovation lies in its use of novel capabilities for on-the-edge Artificial Intelligence, including semantic scene understanding, advanced multi-modal data analysis, and real-time sense & react capabilities. These innovations give robots an artificial form of "common sense," allowing them to perceive, interpret, and adapt to complex and dynamic environments in real time.
The project’s main objectives were to:
- Develop a robot-agnostic software platform for managing heterogeneous robotic fleets.
- Implement edge-based AI modules for autonomous perception and decision-making.
- Enable semantic navigation and adaptive behavior control for operation in complex industrial environments.
- Build a scalable AI ecosystem for continuous learning and extension.
- Demonstrate the platform’s applicability across multiple industries and use cases.
Thereby, Edge AIR contributes to EU priorities in digitalization, industrial safety, competitiveness, and sustainability. It reduces human exposure to risk, improves inspection quality, and supports the transition toward automated, data-driven infrastructure management. The approach also accounts for human-robot interaction and workforce adaptation, ensuring automation complements—not replaces—human roles.
