Unified Navigation Framework for Tethered Robots: Integration of Fuzzy Logic, Genetic Algorithms, and Deep Reinforcement Learning

This repository contains the complete implementation of a unified navigation framework for tethered robots operating in GPS-denied environments. The framework addresses the fundamental challenge of simultaneously optimizing path planning efficiency and tether constraint management through integrated computational intelligence methods.

Key Components:
- Kalman-Fuzzy Hybrid Filter: Multi-sensor fusion system reducing localization errors by 63.5% through adaptive weighting of LiDAR, IMU, and tether tension sensors
- Genetic Algorithm Optimizer: Dynamic tether parameter optimization achieving 40% reduction in entanglement incidents
- Deep Reinforcement Learning Navigation: DRL-enhanced bug algorithm with predictive obstacle avoidance, improving path efficiency by 38%

Implementation Details:
- Simulation Environment: ARGoS with custom PhysX 4.1 tether dynamics
- Hardware Support: ROS 2 integration for NVIDIA Jetson AGX Xavier platforms
- Programming Languages: Python 3.8+, C++17
- Neural Network Framework: PyTorch 1.10+

Performance Metrics:
- 92% path efficiency in cluttered environments
- 45% collision reduction versus classical methods
- 85% reduction in entanglement incidents
- Mean decision latency: 18±2.1 ms

Applications:
This framework enables reliable autonomous operation in disaster response, industrial inspection, confined-space exploration, and any scenario requiring continuous power and communication through physical tethers.

Citation:
If you use this code in your research, please cite our paper:
Sheikder, C., Zhang, W., Chen, X., Li, F., He, X., Tan, X., He, H., & Fan, S. (2026). A Unified Navigation Framework for Tethered Robots Integrating Fuzzy Logic, Genetic Algorithms, and Deep Reinforcement Learning. [Under Review].