TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
CoRR(2024)
摘要
Autonomous robots exploring unknown areas face a significant challenge –
navigating effectively without prior maps and with limited external feedback.
This challenge intensifies in sparse reward environments, where traditional
exploration techniques often fail. In this paper, we introduce TopoNav, a novel
framework that empowers robots to overcome these constraints and achieve
efficient, adaptable, and goal-oriented exploration. TopoNav's fundamental
building blocks are active topological mapping, intrinsic reward mechanisms,
and hierarchical objective prioritization. Throughout its exploration, TopoNav
constructs a dynamic topological map that captures key locations and pathways.
It utilizes intrinsic rewards to guide the robot towards designated sub-goals
within this map, fostering structured exploration even in sparse reward
settings. To ensure efficient navigation, TopoNav employs the Hierarchical
Objective-Driven Active Topologies framework, enabling the robot to prioritize
immediate tasks like obstacle avoidance while maintaining focus on the overall
goal. We demonstrate TopoNav's effectiveness in simulated environments that
replicate real-world conditions. Our results reveal significant improvements in
exploration efficiency, navigational accuracy, and adaptability to unforeseen
obstacles, showcasing its potential to revolutionize autonomous exploration in
a wide range of applications, including search and rescue, environmental
monitoring, and planetary exploration.
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