Energy-Aware Sensor Fusion Architecture for Autonomous Channel Robot Navigation in Constrained Environments.

Area of Science:

• Robotics
• Autonomous Systems
• Sensor Fusion

Background:

• Autonomous robots in confined channels face challenges like limited space, dynamic obstacles, and energy constraints.
• Current sensor fusion methods are power-intensive due to constant sensor activation, limiting operational duration.
• Efficient navigation in environments like pipelines and sewers requires optimized energy usage.

Purpose of the Study:

• To develop an energy-aware adaptive sensor fusion framework for autonomous channel robots.
• To minimize energy consumption without compromising navigation accuracy and reliability.
• To enable extended autonomous operations in challenging confined environments.

Main Methods:

• An adaptive sensor fusion framework utilizing RGB cameras, laser range finders, and IMU sensors.
• Adaptive Extended Kalman Filter (EKF) for selective multi-sensor data integration.
• An energy management module for dynamic sensor activation and computational load adjustment.

Main Results:

• A 35% reduction in energy consumption was achieved.
• Minimal impact on overall navigation performance was observed.
• The system demonstrated effectiveness in simulations and prototype testing within constrained channels.

Conclusions:

• The proposed energy-aware adaptive sensor fusion framework significantly reduces power consumption for autonomous channel robots.
• This approach enhances the feasibility of long-duration autonomous operations in pipelines, sewers, and industrial ducts.
• The adaptive strategy balances energy efficiency with reliable navigation performance.