Distributed Perception for Autonomous Robot Teams

Future autonomous robot teams will require perception systems capable of operating under uncertainty, intermittent communication, and partial observations. This research investigates distributed probabilistic perception frameworks that allow robot teams to collaboratively build consistent representations of dynamic environments.

The work combines Bayesian estimation, Random Finite Set theory, and decentralised information fusion with intelligent decision-making.

Planned Research Directions

• Distributed Bayesian perception.
• Multi-robot information fusion.
• Probabilistic semantic mapping.
• Active perception.
• Resilient perception under communication constraints.

Long-Term Vision

This project represents a future direction of the research program, connecting robot perception with autonomous decision-making for large-scale multi-robot systems.