This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.
翻译:本文介绍了利用航空机器人积极收集三维表面信息的在线信息信息路径规划方法。大部分现有的地面检查工作侧重于规划一条能够全面覆盖地面的离线路径,这一路径本身假定地表信息被统一分布,从而忽略了信息领域潜在的空间相关性。在本文中,我们利用具有大地测量内核功能的多重高斯进程(MGPs)绘制地表信息字段,并以递减地平线的方式规划在线信息路径。我们的方法根据最近观测的结果积极规划信息收集路径,以尊重车辆的动态限制和总飞行时间预算。我们为简单和复杂的3D地貌模型(一个圆筒和一个飞机模型)提供模拟温度模型的规划结果。我们证明,我们的信息规划方法在重建错误和信息-理论测量中都超过了3D覆盖规划和随机探索等传统方法。我们还表明,通过将信息领域的空间相关性纳入利用地平面数据进行规划,信息收集的效率得到显著提高。