International business machines corporation (20240248436). OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES simplified abstract
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 20240248436 titled 'OPTIMIZING COLLABORATIVE WORK AMONG ROBOTIC MACHINES
The patent application describes techniques for optimizing collaborative work performed by robotic machines in an industrial environment. This involves analyzing industrial activity data and robotic machine data using a machine learning model to generate a production plan for the robotic machines on the industrial floor.
- Obtaining industrial activity data with steps of industrial activities performed by robotic machines
- Obtaining robotic machine data with configuration information of the robotic machines
- Inputting the data into a machine learning model to analyze and generate a production plan
- Configuring the 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 Robotic Machine Collaboration in Industrial Environments" This technology can be used in various industries such as manufacturing, logistics, and warehousing to improve operational efficiency and productivity.
Prior Art: Readers can explore prior patents related to machine learning in industrial automation and robotic systems to understand the evolution of this technology.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for industrial automation and collaborative robotics to enhance the performance of robotic machines.
Questions about Optimizing Robotic Machine Collaboration in Industrial Environments: 1. How does this technology impact the overall efficiency of industrial operations? - This technology significantly improves efficiency by optimizing the collaboration of robotic machines and streamlining industrial activities. 2. What are the key factors to consider when implementing this technology in an industrial environment? - Factors to consider include the specific industrial activities, the configuration of robotic machines, and the integration of the machine learning model for production planning.
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.
