NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces
NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces
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DOI：
10.1109/iros47612.2022.9982207
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
S. Siva;Maggie B. Wigness;J. Rogers;Long Quang;Hao Zhang-
中科院分区：
文献类型：
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作者：
S. Siva;Maggie B. Wigness;J. Rogers;Long Quang;Hao Zhang-
当机器人在具有非结构化地形的真实越野环境中运行时，调整其导航策略的能力对于有效和安全的导航至关重要。然而，越野地形给机器人导航带来了一些挑战，包括动态障碍物和地形不确定性，导致低效的穿越或导航失败。为了解决这些挑战，我们引入了一种新的方法，通过协商，使地面机器人调整其导航行为，通过协商过程的适应。我们的方法首先学习各种导航策略的预测模型，以作为地形感知的联合本地控制器和规划器。然后，通过一个新的协商过程，我们的方法从各种政策与环境的相互作用中学习，以在线方式商定最佳的政策组合，使机器人导航适应非结构化的越野地形。此外，我们实现了一个新的优化算法，提供了最佳的解决方案，在执行过程中的实时机器人谈判。实验结果验证了我们的方法适应协商优于以前的方法，机器人导航，特别是在看不见的和不确定的动态地形。
When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.