18046767. AUTONOMOUS MOBILE ROBOT BEHAVIORAL ANALYSIS FOR DEVIATION WARNINGS simplified abstract (Dell Products L.P.)

AUTONOMOUS MOBILE ROBOT BEHAVIORAL ANALYSIS FOR DEVIATION WARNINGS

Organization Name

Dell Products L.P.

Inventor(s)

[[:Category:Herberth Birck Fr�hlich of Florianópolis (BR)|Herberth Birck Fr�hlich of Florianópolis (BR)]][[Category:Herberth Birck Fr�hlich of Florianópolis (BR)]]

Ítalo Gomes Santana of Rio de Janeiro (BR)

Julia Drummond Noce of Rio de Janeiro (BR)

Vinicius Michel Gottin of Rio de Janeiro (BR)

AUTONOMOUS MOBILE ROBOT BEHAVIORAL ANALYSIS FOR DEVIATION WARNINGS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18046767 titled 'AUTONOMOUS MOBILE ROBOT BEHAVIORAL ANALYSIS FOR DEVIATION WARNINGS

Simplified Explanation

The abstract describes a method for analyzing the real-time operational data of Autonomous Mobile Robots (AMRs) to identify behavioral scenarios.

• Receiving real-time operational data related to the operation of AMRs belonging to an AMR group.
• Accessing clusters of expected behavior for the AMRs, generated using historical operational data.
• Generating resultant vectors from the cluster centroid to the most recent operational point of each AMR.
• Using a predetermined phase threshold value to group close resultant vectors into Resultant of Resultant Vectors (RoRs).
• Using RoRs to identify behavioral scenarios of the AMRs.

Potential Applications

This technology could be applied in various industries where AMRs are used for tasks such as warehouse automation, logistics, and manufacturing.

Problems Solved

This technology helps in identifying and predicting the behavioral scenarios of AMRs, which can improve operational efficiency, prevent collisions, and enhance overall safety in dynamic environments.

Benefits

The benefits of this technology include improved decision-making, enhanced operational control, increased productivity, and reduced downtime for AMRs.

Potential Commercial Applications

The potential commercial applications of this technology include autonomous warehouse management systems, smart factories, and robotic fleet management solutions.

Possible Prior Art

One possible prior art could be the use of clustering algorithms in robotics for behavior analysis and prediction. Another could be the use of real-time data analysis for improving the performance of autonomous systems.

Unanswered Questions

How does this technology handle unexpected or abnormal behaviors of AMRs?

The method described in the abstract focuses on identifying expected behavioral scenarios of AMRs. It would be interesting to know how the system reacts to unexpected or abnormal behaviors and if there are mechanisms in place to address such situations.

What is the scalability of this technology for large fleets of AMRs?

While the abstract mentions analyzing operational data related to AMRs belonging to an AMR group, it would be beneficial to understand the scalability of this method for managing and analyzing data from a large number of AMRs in real-time.

Original Abstract Submitted

One example method includes receiving real-time operational data related to the operation of Autonomous Mobile Robots (AMRs) belonging to an AMR group. Clusters of expected behavior, for the AMRs, are accessed. The clusters were generated using historical operational data. Each cluster defines a possible behavioral scenario for each AMR and includes a cluster boundary that defines a limit of the expected behavior and a cluster centroid that defines an average of expected behavior of each AMR. Resultant vectors that extend from the cluster centroid to the most recent operational point of each AMR are generated. A predetermined phase threshold value is used to determine when two or more of the resultant vectors are close to each other. The close resultant vectors are grouped to generate Resultant of Resultant Vectors (RoRs). The RoRs are used to identify behavioral scenarios of the AMRs.