Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
This addresses the need for early anomaly detection in autonomous robots to prevent damage, though it is incremental as it builds on existing methods with a proactive approach.
The paper tackles the problem of anomalous behaviors in mobile robot navigation by proposing a proactive anomaly detection network (PAAD) that predicts future failures based on planned motions and current observations, achieving superior failure identification performance and low false detection rates in cluttered fields.
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