  Despite the rapid advancement of navigation algorithms, mobile robots often
produce anomalous behaviors that can lead to navigation failures. The ability
to detect such anomalous behaviors is a key component in modern robots to
achieve high-levels of autonomy. Reactive anomaly detection methods identify
anomalous task executions based on the current robot state and thus lack the
ability to alert the robot before an actual failure occurs. Such an alert delay
is undesirable due to the potential damage to both the robot and the
surrounding objects. We propose a proactive anomaly detection network (PAAD)
for robot navigation in unstructured and uncertain environments. PAAD predicts
the probability of future failure based on the planned motions from the
predictive controller and the current observation from the perception module.
Multi-sensor signals are fused effectively to provide robust anomaly detection
in the presence of sensor occlusion as seen in field environments. Our
experiments on field robot data demonstrates superior failure identification
performance than previous methods, and that our model can capture anomalous
behaviors in real-time while maintaining a low false detection rate in
cluttered fields. Code, dataset, and video are available at
https://github.com/tianchenji/PAAD
