Multi-Sensor Fusion for Proactive Anomaly Detection in Robot Navigation

Mobile robots frequently exhibit abnormal behaviors that can impair navigation despite the rapid progress of navigation algorithms. Modern robots need to be able to recognize these unusual behaviors in order to reach high levels of autonomy. Methods for reactive anomaly detection Detect anomalies poor task executions based on the current state of the robot and thus lack the capability to warn the robot before a malfunction actually happens. Due to the possibility of harm to the robot and the environment, such a warning delay is undesirable. For robot navigation in unstructured and uncertain situations, we suggest a proactive anomaly detection network (PAAD). Based on the anticipated movements from the predictive controller and the current observations from the perception module, PAAD forecasts the likelihood of future failure. Effective fusion of multi-sensor signals to provide reliable anomaly detection as seen in the field when there is sensor occlusion environments. Our tests on data from field robots show that our model can catch abnormal actions in real-time while retaining a low false detection rate in congested areas, outperforming earlier methods in failure identification