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.
Sheikder et al. (Sun,) studied this question.