18158535. OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES simplified abstract (International Business Machines Corporation)
Contents
OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES
Organization Name
International Business Machines Corporation
Inventor(s)
Jennifer M. Hatfield of San Francisco CA (US)
Jeremy R. Fox of Georgetown TX (US)
Tushar Agrawal of West Fargo ND (US)
Sarbajit K. Rakshit of Kolkata (IN)
OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18158535 titled 'OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES
The abstract describes techniques for optimizing collaborative work performed by robotic machines in an industrial environment. This involves obtaining industrial activity data and robotic machine data, inputting this data into a machine learning model to generate a production plan, and configuring the robotic machines on the industrial floor according to this plan.
- Obtaining industrial activity data and robotic machine data
- Inputting data into a machine learning model to generate a production plan
- Configuring robotic machines on the industrial floor based on the production plan
Potential Applications: - Manufacturing processes - Warehouse operations - Logistics and supply chain management
Problems Solved: - Efficient utilization of robotic machines - Streamlining industrial activities - Improving productivity and performance
Benefits: - Increased efficiency - Cost savings - Enhanced production output
Commercial Applications: Title: "Optimizing Collaborative Work in Industrial Environments" This technology can be utilized in various industries such as manufacturing, logistics, and warehousing to improve operational efficiency and productivity.
Prior Art: Researchers can explore existing patents related to machine learning in industrial settings, robotic machine optimization, and production planning.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for industrial applications, robotic machine configurations, and production planning methodologies.
Questions about the technology: 1. How does this technology impact the overall efficiency of industrial operations? 2. What are the key factors to consider when implementing this technology in different industrial settings?
Original Abstract Submitted
Described are techniques for optimizing collaborative work performed by robotic machines in an industrial environment. The techniques include obtaining industrial activity data comprising steps of industrial activities performed by robotic machines on an industrial floor. The techniques further include obtaining robotic machine data comprising configuration information of the robotic machines associated with performing the steps of the industrial activities. The techniques further include inputting the industrial activity data and the robotic machine data to a machine learning model to analyze the steps of the industrial activities in view of the configuration information of the robotic machines to generate a production plan that aggregates performance of selected steps by the robotic machines, and configuring the robotic machines on the industrial floor according to the production plan generated by the machine learning model.
