让机器人在未知环境里智能找目标、避障碍，边看边走。
Semantically-driven Deep Reinforcement Learning for Inspection Path Planning
- 基于深度强化学习，结合视觉语义与导航规划
- 仅用实时深度图和分割图，实现无碰撞路径规划
- 在真实飞行机器人上验证，跨仿真到现实表现良好
本文提出一种基于深度强化学习的语义感知巡检路径规划方法。针对自主环境中仅有少量感兴趣目标需被检测的问题，该方法构建了一个端到端策略，同时完成语义目标视觉检测与无碰撞导航。仅依赖瞬时深度图、分割图像、自车局部占用栅格及历史位置信息，展现出强泛化能力，并成功跨越了仿真到现实的差距。通过大量仿真对比与实验验证，该方法在部署于飞行机器人的真实新环境中，面对未见过的语义内容和几何结构，均实现了有效巡检。
原文摘要 · Abstract (English)
This paper introduces a novel semantics-aware inspection planning policy derived through deep reinforcement learning. Reflecting the fact that within autonomous informative path planning missions in unknown environments, it is often only a sparse set of objects of interest that need to be inspected, the method contributes an end-to-end policy that simultaneously performs semantic object visual inspection combined with collision-free navigation. Assuming access only to the instantaneous depth map, the associated segmentation image, the ego-centric local occupancy, and the history of past positions in the robot's neighborhood, the method demonstrates robust generalizability and successful crossing of the sim2real gap. Beyond simulations and extensive comparison studies, the approach is verified in experimental evaluations onboard a flying robot deployed in novel environments with previously unseen semantics and overall geometric configurations.
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