  This paper introduces SHANGUS, an advanced framework combining Deep
Reinforcement Learning (DRL) with heuristic optimization to improve
frontier-based exploration efficiency in unknown environments, particularly for
intelligent vehicles in autonomous air services, search and rescue operations,
and space exploration robotics. SHANGUS harnesses DRL's adaptability and
heuristic prioritization, markedly enhancing exploration efficiency, reducing
completion time, and minimizing travel distance. The strategy involves a
frontier selection node to identify unexplored areas and a DRL navigation node
using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for
robust path planning and dynamic obstacle avoidance. Extensive experiments in
ROS2 and Gazebo simulation environments show SHANGUS surpasses representative
traditional methods like the Nearest Frontier (NF), Novel Frontier-Based
Exploration Algorithm (CFE), and Goal-Driven Autonomous Exploration (GDAE)
algorithms, especially in complex scenarios, excelling in completion time,
travel distance, and exploration rate. This scalable solution is suitable for
real-time autonomous navigation in fields such as industrial automation,
autonomous driving, household robotics, and space exploration. Future research
will integrate additional sensory inputs and refine heuristic functions to
further boost SHANGUS's efficiency and robustness.
