EP-4740507-A1 - SYSTEM AND METHOD FOR MANAGING AND CONTROLLING 5G-ENABLED AUTONOMOUS MOBILE ROBOTS

EP4740507A1EP 4740507 A1EP4740507 A1EP 4740507A1EP-4740507-A1

Abstract

The present invention provides a system (108) and method (500) for centrally managing and controlling a fleet of Autonomous Mobile Robots (AMRs). The system (108) comprises a network module (210) configured to provide a private network coverage for each of the plurality of AMRs, a Fleet Management System (FMS) (212) deployed over an edge of network, configured to provide one or more real-time instructions to the plurality of AMRs, and a computing module (218) installed within each of the plurality of AMRs to enable for seamless communication. The system (108) achieves Ultra Reliable Low Latency Communication (URLLC) of less than 25ms, enabling real-time instructions to the plurality of AMRs. The system (108) enhances efficiency, scalability, and safety in warehouse operations by enabling advanced navigation, dynamic routing, and obstacle detection in real-time.

Inventors

BHATNAGAR, ASHISH
PURUSHAN, Gaurav Mangesh
AWASTHI, NITIN
Patil, Anup Bhaskar
BHATNAGAR, AAYUSH
BHATNAGAR, PRADEEP KUMAR

Assignees

Jio Platforms Limited

Dates

Publication Date: 20260513
Application Date: 20240618

Claims (11)

1. A system (108) for centrally managing and controlling a plurality of Autonomous Mobile Robots (AMRs) the system (108) comprising: a network module (210) configured to provide a private network coverage for each of the plurality of AMRs; a Fleet Management System (FMS) (212) deployed over an edge of a network,), configured to provide one or more real-time instructions to the plurality of AMRs; and a computing module (218) installed within each of the plurality of AMRs to enable seamless communication among the plurality of AMRs and the FMS (212) over the private network based on the one or more real-time instructions, wherein the one or more real-time instructions is provided to manage and control operations of the plurality of AMRs.
2. The system (108) as claimed in claim 1, wherein to provide the private network coverage, the network module (210) comprises a Distributed Antenna System (DAS) powered by an Outdoor Small Cell (ODSC) (116).
3. The system (108) as claimed in claim 1, wherein deployment of the FMS (212) over the edge of the network provides Ultra Reliable Low Latency Communication (URLLC).
4. The system (108) as claimed in claim 1, wherein the plurality of AMRs is equipped with an artificial intelligence for navigating each of the plurality of AMRs along dynamic routes and detecting obstacles in real-time.
5. The system (108) as claimed in claim 1, wherein the network module (210) is one of a private network module or a public network module to provide a wider signal propagation, support for high device density, and secure connectivity over a licensed spectrum.
6. A method (500) for centrally managing and controlling a plurality of Autonomous Mobile Robots (AMRs), the method (500) comprising: establishing (502), by a network module (210), a private network coverage for each of the plurality of AMRs; providing (504), by a Fleet Management System (FMS) (212), one or more real-time instructions to the plurality of AMRs; and upgrading (506), by a computing module (218), the plurality of AMRs to enable seamless communication among the plurality of AMRs and the FMS (212) over the private network based on the one or more real-time instructions, wherein the one or more real-time instructions is provided to manage and control operations of the plurality of AMRs.
7. The method (500) as claimed in claim 6, further comprising achieving Ultra Reliable Low Latency Communication (URLLC) between the FMS (212) and the plurality of AMRs based on deployment of the FMS (212) over an edge of the network.
8. The method (500) as claimed in claim 6, further comprising enabling navigation for the plurality of AMRs using an artificial intelligence for dynamic routing and obstacle detection in real-time.
9. A user equipment (UE) (104) configured to manage and control a plurality of Autonomous Mobile Robots (AMRs), the UE (104) comprising: a processor (202); and a computer readable storage medium storing programming for execution by the processor (202), the programming including instructions to: establish a private network coverage through a network module (210) for each of the plurality of AMRs; provide one or more real-time instructions to the plurality of AMRs through a Fleet Management System (FMS) (212) and via the UE (104); and upgrade the plurality of AMRs through a computing module (218) to enable seamless communication among the plurality of AMRs and the FMS (212) over the private network based on the one or more real-time instructions, wherein the one or more real-time instructions is provided to manage and control operations of the plurality of AMRs.
10. The UE (104) as claimed in claim 9, wherein the programming including instructions to enable navigation for the plurality of AMRs using an artificial intelligence for dynamic routing and obstacle detection in realtime.
11. A computer program product comprising a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method (500) for centrally managing and controlling a plurality of Autonomous Mobile Robots (AMRs), the method (500) comprising: establishing (502), by a network module (210), a private network coverage for each of the plurality of AMRs; providing (504), by a Fleet Management System (FMS) (212), one or more real-time instructions to the plurality of AMRs; and upgrading (506), by a computing module (218), the plurality of AMRs to enable seamless communication among the plurality of AMRs and the FMS (212) over the private network based on the one or more real-time instructions, wherein the one or more real-time instructions is provided to manage and control operations of the plurality of AMRs.

Description

SYSTEM AND METHOD FOR MANAGING AND CONTROLLING 5G-ENABLED AUTONOMOUS MOBILE ROBOTS RESERVATION OF RIGHTS [0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner. TECHNICAL FIELD OF DISCLOSURE [0002] The present disclosure generally relates to a field of robotics, automation, and telecommunication. More precisely, the present disclosure pertains to a system and a method for centrally managing and controlling Autonomous Mobile Robots (AMRs). BACKGROUND OF DISCLOSURE [0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art. [0004] The use of forklifts in warehouses may present certain dangers and risks if not managed properly. Forklift operators may be at risk of accidents and injuries if they are not adequately trained, lack experience, or fail to follow safety protocols. The operator may be at risk of collisions, falls, being struck by falling objects, or tipping over the forklift. Warehouse workers and other employees working in the vicinity of forklift operations may be at risk of accidents if they are not aware of the forklift's movements or fail to follow designated pedestrian walkways. Inadequate separation of pedestrian and forklift traffic may lead to collisions and serious injuries. There is, therefore, a need to come up with automated solutions to manage the processes in the warehouse. [0005] Edge computing technology is a distributed computing paradigm that brings data processing and storage closer to the source of data generation, enabling faster response times, reduced latency, and improved efficiency. Unlike traditional cloud computing, where data is sent to a central data center for processing, edge computing processes data at or near the edge of the network, where the data is being generated. In edge computing, small-scale data centers, or edge nodes, are deployed at the edge of the network infrastructure, such as in proximity to loT devices, mobile base stations, or local networks. These edge nodes may perform computational tasks, store data, and provide real-time analytics, eliminating the need to send all data to a centralized cloud environment for processing. [0006] Fleet management systems are software platforms or solutions used by organizations to efficiently manage and monitor their vehicle fleets. These systems provide a range of features and functionalities that help optimize fleet operations, improve safety, reduce costs, and enhance overall productivity. Instead of relying on foreign suppliers or manufacturers, indigenous 5G radios are designed and produced domestically, often with the aim of promoting local technology development, fostering economic growth, and ensuring national security. Private 5G refers to a local and dedicated 5G network that is deployed and operated by a specific organization or enterprise for its own private use. Unlike public 5G networks that are provided by telecommunication companies and available to the public, private 5G networks are designed to serve the connectivity needs of a specific entity in a closed environment. [0007] Existing systems for warehouse robots vary depending on the specific requirements and applications. Automated Guided Vehicles (AGVs) are mobile robots that follow predefined paths or markers on the floor to transport goods within a warehouse. Drawbacks of the AGVs include limited flexibility, as the AGVs typically operate on fixed routes and may require infrastructure modifications to accommodate their navigation. AGVs may also have slower speeds compared to other robotic systems. Autonomous Mobile Robots (AMRs) are more flexible than AGVs as they can navigate autonomously using built-in sensors, cameras, and mapping capabilities. However, none of the industrial warehouse AMRs have been developed to operate wirelessly over a private 5G network. The main drawbacks of the existing systems are that implementing robots in the warehouse may involve high initial costs for equipment, integration, and infrastructure modifications. The return on investment needs to be c