Bayesian reinforcement learning for navigation planning in unknown environments.

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Operations Research

Background:

• Robots and drones are increasingly used in rescue operations, requiring efficient navigation in unknown environments.
• Existing autonomous techniques often need full environmental knowledge or simulators, limiting real-world application.
• Rescue missions demand rapid environmental assessment and victim localization.

Purpose of the Study:

• To develop a method for robot navigation in unknown environments for rescue missions.
• To address limitations of current techniques that require complete environmental information.
• To enable optimal decision-making for autonomous agents with uncertain information.

Main Methods:

• A probabilistic/Bayesian representation of unknown environments using a belief state.
• Joint modeling of agent navigation stochasticity and environmental uncertainty.
• Deep reinforcement learning for computing an approximate Bayesian planning policy.

Main Results:

• The proposed belief state enables offline learning of optimal Bayesian policies.
• Deep reinforcement learning effectively handles the large belief space for policy computation.
• Numerical experiments demonstrate high performance in maze-based rescue scenarios.

Conclusions:

• The developed Bayesian approach enhances autonomous navigation in unknown environments.
• This method allows for optimal planning without real-time data or interaction.
• The study provides a robust solution for improving robot-assisted rescue operations.