Active Traversability Learning via Risk-aware Information Gathering for Planetary Exploration Rovers
Active Traversability Learning via Risk-aware Information Gathering for Planetary Exploration Rovers
复制标题
通过行星探索漫游车的风险意识信息收集进行主动可穿越性学习
DOI：
10.1109/lra.2022.3207554
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发表时间：
2022
影响因子：
5.2
通讯作者：
Genya Ishigami
中科院分区：
文献类型：
--
作者：
Masafumi Endo;Genya Ishigami
可穿越性预测使漫游车能够在可变形的行星表面上安全高效地自主运行。揭示从地形几何到探测车滑动行为的空间分布是评估未来可穿越性的关键，但由于探测车穿越的保守性，对危险状态的原位测量不足，阻碍了这一点。为了实现更准确的预测，本文提出了一种框架，在随机漫游车滑移约束下，通过探索信息地形主动学习潜在可穿越性。利用高斯过程（GP）建模空间分布，我们设计了一个迭代的两阶段框架，逐步改进模型估计，结合风险感知信息路径规划和GP更新，通过现场测量。路径规划阶段采用我们设计的基于采样的算法来生成具有容错风险推断的信息轨迹，而GP则谨慎地使用导线数据更新，以避免漫游车的固定。在框架中利用机会约束公式来推断信息区域的随机可达性。通过GP估计降低不确定性，算法沿可行轨迹逐步达到信息丰富但危险的状态。在粗糙地形环境下的仿真研究表明，该框架在避免漫游车卡死的情况下收集了信息丰富的穿越数据，以估计潜在的可穿越性模型。
Traversability prediction enables safe and efficient autonomous rover operation on deformable planetary surfaces. Revealing spatial distribution from terrain geometry to rover slip behavior is key to assessing prospective traversability, but is hindered by insufficient in situ measurements on hazardous states due to conservative rover traverses. To achieve a more accurate prediction, this letter proposes a framework that actively learns latent traversability by exploring informative terrain under the constraints of stochastic rover slip. With a Gaussian process (GP) modeling the spatial distribution, we devise an iterative two-stage framework that gradually refines the model estimation, combining risk-aware informative path planning and GP updates by taking in situ measurements. The path planning stage employs our designed sampling-based algorithm to generate informative trajectories with fault-tolerant risk inference, while the GP is cautiously updated with traverse data to avoid rover immobilization. Chance constraint formulation is exploited in the framework to infer the stochastic reachability of informative regions. Through GP estimates reducing uncertainty, the algorithm incrementally reaches informative yet hazardous states along feasible trajectories. Simulation studies in rough terrain environments demonstrate that the proposed framework gathers informative traverse data while averting rover stuck situations to estimate the latent traversability model.