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About this paper
Reinis Cimurs, Il Hong Suh, Jin Han Lee
Researchers from Hanyang University and Cogaplex company developed Goal-Driven Autonomous Exploration (GDAE), a hierarchical system that combines a global waypoint selection strategy with a deep reinforcement learning (DRL) local navigation policy to enable autonomous robot exploration towards distant, unknown goals. The system achieved a perfect success rate in complex real-world indoor environments and demonstrated shorter average travel times compared to traditional planning methods.
Key insights
- Hierarchical integration of a global goal-driven strategy with reactive DRL-based local control effectively mitigates the local optimum problem in DRL for large-scale navigation.
- Dynamic waypoint selection from real-time sensor data, combined with concurrent occupancy grid mapping, facilitates robust goal-driven exploration without prior environmental knowledge.
- A DRL policy, trained on local sensor and waypoint information, offers agile and efficient local motion control, resulting in faster task completion and enhanced robustness to unmapped obstacles compared to traditional planning methods.
Summary by alphaXiv.
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