面向巡检路径规划的语义驱动深度强化学习
Semantically-driven Deep Reinforcement Learning for Inspection Path Planning
AI总结：
本研究提出一种语义驱动的深度强化学习巡检规划策略，仅依靠深度图、分割图等局部信息即可同时实现语义目标巡检与无碰撞导航，具备强泛化性并通过了飞行机器人的真实环境验证。
摘要：
本文提出了一种通过深度强化学习得到的新型语义感知巡检规划策略。针对未知环境下自主信息式路径规划任务中通常仅需巡检稀疏的感兴趣目标这一实际情况，该方法提出了一种端到端策略，可同时执行语义目标视觉巡检与无碰撞导航。该方法仅需利用瞬时深度图、对应的分割图像、以自身为中心的局部占用栅格以及机器人邻域内的历史位置信息，展现出强泛化性，可成功跨越sim2real（仿真到现实）差距。除仿真实验与大量对比研究外，该方法还在部署于全新环境的飞行机器人上完成了实验验证，测试环境包含此前未见过的语义类别与整体几何构型。
英文摘要：
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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