AI-Powered Navigation System
Overview
The AI-Powered Navigation System project develops intelligent navigation algorithms for autonomous mobile robots. Using computer vision and deep learning, our system enables robots to navigate complex environments without human intervention.
Objectives
- Implement SLAM (Simultaneous Localization and Mapping) algorithms
- Develop neural network-based obstacle detection
- Create adaptive path planning systems
- Integrate sensor fusion for robust navigation
Current Progress
Our navigation stack successfully operates on multiple robot platforms, demonstrating real-time obstacle avoidance and dynamic path replanning. The system has been tested in both indoor and outdoor environments.
Team Structure
- Project Lead: Sophie Taylor
- ML Engineers: 3 members
- Robotics Engineers: 2 members
- Software Developers: 2 members
Technologies Used
- ROS (Robot Operating System)
- TensorFlow/PyTorch for ML models
- OpenCV for computer vision
- LiDAR and RGB-D cameras
- Python and C++ programming
Key Features
- Real-time 3D environment mapping
- Deep learning-based object recognition
- Predictive path planning algorithms
- Multi-sensor data fusion
- Autonomous exploration capabilities
Challenges
Navigating dynamic environments requires robust algorithms that can adapt to changing conditions. We’re addressing challenges in computational efficiency, sensor noise, and real-time performance optimization.
Future Goals
- Implement multi-robot coordination
- Expand to aerial navigation platforms
- Develop edge-AI deployment for embedded systems
- Compete in autonomous navigation competitions
Applications
Our navigation system has applications in warehouse automation, search and rescue operations, agricultural robotics, and autonomous delivery vehicles.