Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems

TL;DR

This paper introduces a hierarchical decision-making framework combining deep reinforcement learning and fuzzy logic to improve autonomous navigation in four-wheel independent steering and driving systems, ensuring task efficiency and physical safety.

Contribution

It presents a novel integration of DRL and fuzzy logic for high-level planning and low-level control in 4WISD systems, enhancing stability and real-world applicability.

Findings

Outperforms traditional navigation methods in simulations

Enhances training efficiency and stability

Successfully navigates in dynamic industrial environments

Abstract

This paper presents a hierarchical decision-making framework for autonomous navigation in four-wheel independent steering and driving (4WISD) systems. The proposed approach integrates deep reinforcement learning (DRL) for high-level navigation with fuzzy logic for low-level control to ensure both task performance and physical feasibility. The DRL agent generates global motion commands, while the fuzzy logic controller enforces kinematic constraints to prevent mechanical strain and wheel slippage. Simulation experiments demonstrate that the proposed framework outperforms traditional navigation methods, offering enhanced training efficiency and stability and mitigating erratic behaviors compared to purely DRL-based solutions. Real-world validations further confirm the framework's ability to navigate safely and effectively in dynamic industrial settings. Overall, this work provides a scalable and reliable solution for deploying 4WISD mobile robots in complex, real-world scenarios.