Abstract
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 is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tianchenji/PAAD</uri> .
Highlights
M OBILE robots are playing an important role in creating intelligent, productive, and easy-to-operate modern farms
Our contributions can be summarized as follows: (1) We propose a novel deep neural network architecture called proactive anomaly detection network (PAAD), which effectively fuses multi-sensor signals for robust perception in unstructured and uncertain environments
We evaluate the anomaly detection performance of PAAD on 4.1 km of real-world navigation data collected with the TerraSentia robot in corn fields from September to October 2020
Summary
M OBILE robots are playing an important role in creating intelligent, productive, and easy-to-operate modern farms. Small and low-cost robots (Figure 1a) deployed under crop canopies can increase agricultural sustainability by performing tasks that cannot be accomplished by overcanopy large equipment [1]. A lack of a detection system for anomalous behaviors before failures may cause damage to robots and plants due to collisions. The detection of such anomalous behaviors can stop the robot from entering failure modes, providing opportunities for executing recovery maneuvers and proceeding with the task. Deep-learning based anomaly detection (AD) algorithms have been widely adopted in robotic applications [6]. Many previous works approached the AD problem in a reactive manner [7]–[10]
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