A Novel Adaptive framework interconnects four pillars for Tethered Robots: Integrating Fuzzy Logic, Genetic Algorithms, and Neural Networks for Robust Dynamic Environment Navigation

Traditional navigation systems for tethered robots often rely on simulated environments, failing to address the unpredictability of real-world dynamic settings—particularly when managing moving obstacles , tether constraints, and sensor noise. To bridge this gap, we propose a novel adaptive hybrid navigation framework that integrates soft computing techniques (fuzzy logic, genetic algorithms, and neural networks) with classical bug algorithms. Our methodology combines sensor fusion to mitigate environmental noise, real-time optimal control for adaptive path planning, and machine learning-driven tether management to dynamically balance efficiency and safety. By enhancing bug algorithms with fuzzy decision-making and genetic optimization, we overcome their inherent limitations in handling non-static obstacles and complex tether configurations. Extensive simulations and real-world trials with a tethered inspection robot demonstrated a 45% reduction in collisions and a 38% improvement in path efficiency compared to conventional methods. Results further show a 28% faster recovery from entanglement scenarios, underscoring the framework’s robustness. This work establishes a foundation for deploying tethered robots in high-stakes applications—from disaster response to industrial inspections—where adaptability and reliability are critical.