FH-DRL: Exponential-Hyperbolic Frontier Heuristics with DRL for accelerated Exploration in Unknown Environments
This addresses the problem of efficient exploration for autonomous vehicles and robots in large or cluttered environments, representing an incremental improvement over existing frontier-based and DRL methods.
The paper tackles autonomous robot exploration in unknown environments by proposing FH-DRL, a framework that integrates a customizable heuristic for frontier detection with a DRL agent, resulting in reduced travel distance and completion time compared to standard methods in simulated and real-world tests.
Autonomous robot exploration in large-scale or cluttered environments remains a central challenge in intelligent vehicle applications, where partial or absent prior maps constrain reliable navigation. This paper introduces FH-DRL, a novel framework that integrates a customizable heuristic function for frontier detection with a Twin Delayed DDPG (TD3) agent for continuous, high-speed local navigation. The proposed heuristic relies on an exponential-hyperbolic distance score, which balances immediate proximity against long-range exploration gains, and an occupancy-based stochastic measure, accounting for environmental openness and obstacle densities in real time. By ranking frontiers using these adaptive metrics, FH-DRL targets highly informative yet tractable waypoints, thereby minimizing redundant paths and total exploration time. We thoroughly evaluate FH-DRL across multiple simulated and real-world scenarios, demonstrating clear improvements in travel distance and completion time over frontier-only or purely DRL-based exploration. In structured corridor layouts and maze-like topologies, our architecture consistently outperforms standard methods such as Nearest Frontier, Cognet Frontier Exploration, and Goal Driven Autonomous Exploration. Real-world tests with a Turtlebot3 platform further confirm robust adaptation to previously unseen or cluttered indoor spaces. The results highlight FH-DRL as an efficient and generalizable approach for frontier-based exploration in large or partially known environments, offering a promising direction for various autonomous driving, industrial, and service robotics tasks.