The use of mobile robots in unstructured environments like the agricultural field is becoming increasingly common. The ability for such field robots to proactively identify and avoid failures is thus crucial for ensuring efficiency and avoiding damage. However, the cluttered field environment introduces various sources of noise (such as sensor occlusions) that make proactive anomaly detection difficult. Existing approaches can show poor performance in sensor occlusion scenarios as they typically do not explicitly model occlusions and only leverage current sensory inputs. In this work, we present an attention-based recurrent neural network architecture for proactive anomaly detection that fuses current sensory inputs and planned control actions with a latent representation of prior robot state. We enhance our model with an explicitly-learned model of sensor occlusion that is used to modulate the use of our latent representation of prior robot state. Our method shows improved anomaly detection performance and enables mobile field robots to display increased resilience to predicting false positives regarding navigation failure during periods of sensor occlusion, particularly in cases where all sensors are briefly occluded. Our code is available at: https://github.com/andreschreiber/roar

翻译：在农业田地等非结构化环境中，移动机器人的使用日益普及。因此，此类田间机器人主动识别并避免故障的能力，对于确保效率、规避损害至关重要。然而，杂乱的环境会引入多种噪声源（如传感器遮挡），使得主动异常检测变得困难。现有方法在传感器遮挡场景下表现不佳，因为它们通常未明确建模遮挡，且仅利用当前传感器输入。本文提出一种基于注意力的循环神经网络架构，用于主动异常检测，该架构将当前传感器输入、规划的控制动作与机器人先前状态的潜在表示相融合。我们通过显式学习的传感器遮挡模型来增强模型，该模型用于调节对机器人先前状态潜在表示的使用。我们的方法提升了异常检测性能，使移动田间机器人在传感器遮挡期间对导航故障的误报预测表现出更高的鲁棒性，尤其是在所有传感器短暂完全遮挡的情况下。我们的代码开源地址为：https://github.com/andreschreiber/roar