Air-Ground Collaborative Autonomous Exploration and Mapping Method for Complex Multi-Grain Pile Environments.

Area of Science:

• Robotics
• 3D Mapping
• Agricultural Technology

Background:

• 3D mapping of grain storage is crucial for management but challenging due to dust, grain piles, and narrow aisles.
• Single robots lack coverage, and multi-robot systems suffer from redundant scanning without explicit task allocation.

Purpose of the Study:

• To develop an air-ground collaborative exploration system for efficient 3D mapping in complex multi-grain pile scenarios.
• To reduce exploration redundancy and improve mapping quality in dusty, GPS-restricted environments.

Main Methods:

• Air-ground collaborative exploration with viewpoint-based task allocation using ray tracing.
• Quality-aware fusion strategy to mitigate dust interference on aerial sensor data.
• LiDAR-based mapping and a re-scanning mechanism for coverage-driven exploration.

Main Results:

• Achieved 97.2% grain pile coverage in simulations.
• Reduced total exploration time by 20.1% compared to single-robot systems.
• Demonstrated effective dust management and reliable mapping.

Conclusions:

• Viewpoint-aware task allocation and dust-sensitive perception fusion provide a practical solution for autonomous inspection in challenging industrial environments.
• The proposed system enhances efficiency and coverage for granary mapping.