About the project
The SHARCEX project focuses on improving underwater operations by integrating advanced autonomous underwater vehicles (AUVs) with human divers. The goal is to enhance safety and efficiency in extreme underwater environments such as defense, rescue operations, and law enforcement.
Key technologies being developed and integrated include:
- Embedded AI for real-time decision-making
- Machine learning for environmental adaptation
- Computer vision for precise navigation and object recognition
- Safe reinforcement learning to ensure strict safety protocols
The project progresses through multiple phases:
- Development of AI models
- System integration
- User interface design
- Operational testing
KTH leads the project in collaboration with FMV and Saab, aiming to demonstrate robust AUV-Diver collaborations validated through extensive simulations and experiments.
Background
Underwater environments are among the most challenging operational domains due to their unpredictability, extreme pressure conditions, limited visibility, and communication constraints. Human divers working in these environments face significant safety risks, particularly in defense, search-and-rescue, law enforcement, and infrastructure inspection scenarios. While Autonomous Underwater Vehicles (AUVs) have been deployed in these fields, their potential remains largely untapped due to limitations in real-time adaptability and human interaction.
Current AUV systems often operate independently, following pre-programmed missions with limited real-time decision-making capabilities. This restricts their usefulness in dynamic and high-risk situations, where divers must quickly adapt to changing conditions, assess threats, and make complex decisions. There is a clear need for AUVs that can function as intelligent, real-time assistants, enhancing human capabilities rather than merely executing pre-set tasks.
The SHARCEX project addresses this gap by developing a next-generation human-robot collaboration frameworkfor underwater operations. By integrating AI-driven AUVs with human divers, the project aims to create a synergistic system where both human and machine leverage each other’s strengths.
Crossdisciplinary collaboration
The project integrates expertise from multiple fields:
- Artificial Intelligence & Machine Learning (real-time decision-making and reinforcement learning)
- Underwater Robotics & Control Systems (AUV operation and sensor integration)
- Human-Robot Interaction (enhancing collaboration between divers and robots)
- Computer Vision (object recognition and navigation)
- Safety & Risk Management (ensuring compliance with safety protocols in extreme environments)
Collaboration partners:
- KTH (lead institution, technical development, and validation)
- FMV (defense applications and operational requirements)
- Saab (industrial expertise in autonomous systems and marine technology)
Principal Investigators (PIs)
- Ivan Stenius (KTH, Project Lead)
- Jana Tumova (KTH co-PI)
- Dimos Dimarogonas (KTH co-PI)
