CV
Computer Vision Plant Identification & Navigation System
Advanced autonomous robot using computer vision to identify plants and navigate to them for automated care.
Overview
This project focuses on the perception and autonomy stack of an autonomous plant care robot. The system uses computer vision algorithms to identify plants in real-time, then plans and executes navigation paths to reach target plants for watering and monitoring. The architecture emphasizes robust perception in dynamic environments and reliable autonomous navigation.
Role
Systems Architect & Lead Developer - Architected the perception pipeline, developed computer vision algorithms, implemented navigation and path planning, and integrated sensor systems.
Tech Stack
- •Computer Vision (OpenCV, TensorFlow)
- •SLAM (Simultaneous Localization and Mapping)
- •Path Planning Algorithms (A*, RRT)
- •Python
- •C++
- •ROS2
- •Camera Systems (RGB, Depth)
- •IMU & Odometry Sensors
Key Challenges
- •Real-time plant detection and classification in cluttered environments
- •Robust visual navigation with limited computational resources
- •Handling occlusions and varying plant appearances
- •Integrating perception with motion planning for smooth navigation
- •Maintaining localization accuracy during autonomous operation
Results
- •Achieved real-time plant identification with sub-second processing times
- •Successfully demonstrated autonomous navigation to identified plant targets
- •Robust performance across different lighting conditions and plant varieties
- •Validated system reliability through extended autonomous operation sessions
Next Steps
- •Deep learning model optimization for edge deployment
- •Multi-camera fusion for improved perception robustness
- •Advanced path planning with dynamic obstacle avoidance
- •Integration with IoT sensors for comprehensive plant health monitoring
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