Mobile Robotics Gallery

TerraMax - Autonomous Agricultural Robot

Source: EarthSense | Website: TerraMax Product Page

TerraMax represents the cutting edge of autonomous agricultural robotics, designed to revolutionize precision farming through advanced sensor fusion and autonomous navigation capabilities. This system demonstrates real-world applications of mobile robotics concepts covered in CS498GC.

Key Technologies Demonstrated:

• Autonomous Navigation: GPS-based path planning and obstacle avoidance
• Sensor Fusion: Multi-modal sensor integration for crop monitoring
• Computer Vision: Plant detection and health assessment
• Precision Agriculture: Targeted intervention and data collection
• Robotic Mobility: All-terrain autonomous operation

Course Relevance:

• Modules 1-2: Coordinate transformations in agricultural mapping
• Module 4: Sensor fusion for crop monitoring
• Module 6: Localization in outdoor environments
• Module 7: SLAM applications in precision agriculture

Mobile Manipulator for Active Semantic Mapping

Research: UIUC Agricultural & Biological Engineering | Location: High Tunnel Greenhouse Facility
Image Collection: /VIDEOS-MACBOOK-M3/ACTIVE-semantic-mapping-pics/

This cutting-edge mobile manipulator robot demonstrates advanced capabilities in greenhouse environments, combining autonomous navigation with precision manipulation for agricultural tasks. The system showcases the practical implementation of concepts from Assignment 4 and represents the future of automated greenhouse farming.

Key Technologies Demonstrated:

• Mobile Manipulation: UR3 arm mounted on Husky mobile base
• Active Semantic Mapping: Real-time 3D semantic understanding of crops
• Computer Vision: Tomato detection and ripeness assessment
• ROS2 Integration: Full stack implementation with Gazebo simulation
• Precision Harvesting: Selective picking based on fruit maturity

Course Relevance:

• Assignment 4: Mobile manipulator setup and control
• Module 7: SLAM for greenhouse navigation
• Module 8: Semantic mapping and scene understanding
• Extra Credit: Advanced perception with SAM & RTAB-Map

Research Materials:

The complete collection of research materials, including 3D models, point clouds, semantic segmentation results, and field test data is available in the ACTIVE-semantic-mapping-pics directory. This includes:

• 45+ visualization images and research figures
• Point cloud reconstructions from multi-view stereo
• DINO feature visualizations and Grounded SAM masks
• Field deployment photos and system architecture diagrams