The T-B PHASE Simulator provides a modular platform for simulating multi-agent teaming to communicate research findings and demonstrate the pros and cons of different deployment options, such as optimising the number and mix of different assets to efficiently achieve mission objectives.

Differnt coloured plastic cones symbolising network of people

Project team: Tom Kent and Emma Barden

Transforming tasks

Hybrid human-autonomous systems offer unprecedented opportunities to transform how we perform a wide range of tasks more efficiently, effectively, and safely than at present. Their development is huge in scope, and requires fundamental scientific research, understanding of complex multi-agent networks, and maintaining robustness in real-world settings.

Integrating all these aspects is a significant challenge that requires suitable investigatory tools and clear communication between technical specialists, engineers, and end users.

The T-B PHASE Simulator research tool was developed to bridge discipline areas for those working on T-B PHASE and translate complex algorithmic concepts into simulated environments that could be interrogated by Thales engineers.

The simulator calculates the position, status, and behaviour of various realistic autonomous system agents in a 2-D environment. It was deliberately designed not as a high-fidelity synthetic environment but a simple to operate, flexible, and modular tool focused on visualisation of research outputs.


The T-B PHASE Simulator can be operated at various levels of detail and realism, which means it can be a common tool for researchers and developers. Academics might study new phenomena in detail in a feature-free environment, while engineers developing concepts of operation for complex multi-agent systems can integrate Thales mapping and mission planning tools and platforms, and even include real-time data, such as weather events, sea states, and Automatic Identification System data tracking of other vessels.


The modular design of the simulator means new and emerging factors and agents with distinct behaviours and characteristics can be easily added to the framework. This also gives users choice over the types of agent, inclusion of random elements, and changes or differences in individual agent behaviour or characteristics over time.


2-D graphical visualisation of outputs creates a powerful means of communicating the often-complex interactions and behaviour of multi-agent teaming operations. This is vital for communicating specialist concepts to a wider audience of industry partners and end users. This makes the T-B PHASE Simulator extremely powerful in inter-disciplinary collaborations by creating a common means of understanding without losing either fundamental interactions or real-world detail.


The T-B PHASE Simulator was pivotal in tackling a host of questions around the operation of realistic hybrid autonomous systems:

  • Understanding how to use communication capabilities optimally for efficiency and system performance, including investigating different network topologies and hierarchical network systems;
  • Understanding the effect of imperfect communications;
  • Investigating adaptive agent capabilities, for example, hanging from a communications node to a sensor, to enable reconfigurable systems;
  • User test cases involving humans and different machine agents, e.g., surface and underwater vehicles for sea-mine clearance operations;
  • Inclusion of a human agent’s sense of agency when part of a human-machine team.

The simulator is already helping to communicate concepts that are vital to the successful deployment of hybrid autonomous systems. The future will also see it become a key collaborative tool in the design of robust and effective human-machine teams.