English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Despite the rapid advancement of navigation algorithms, mobile robots oftenproduce anomalous behaviors that can lead to navigation failures. The abilityto detect such anomalous behaviors is a key component in modern robots toachieve high-levels of autonomy. Reactive anomaly detection methods identifyanomalous task executions based on the current robot state and thus lack theability to alert the robot before an actual failure occurs. Such an alert delayis undesirable due to the potential damage to both the robot and thesurrounding objects. We propose a proactive anomaly detection network (PAAD)for robot navigation in unstructured and uncertain environments. PAAD predictsthe probability of future failure based on the planned motions from thepredictive controller and the current observation from the perception module.Multi-sensor signals are fused effectively to provide robust anomaly detectionin the presence of sensor occlusion as seen in field environments. Ourexperiments on field robot data demonstrates superior failure identificationperformance than previous methods, and that our model can capture anomalousbehaviors in real-time while maintaining a low false detection rate incluttered fields. Code, dataset, and video are available athttps://github.com/tianchenji/PAAD

Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion