Intel corporation (20240351200). COBOT MODEL GENERATION BASED ON A GENERIC ROBOT MODEL simplified abstract

COBOT MODEL GENERATION BASED ON A GENERIC ROBOT MODEL

Organization Name

intel corporation

Inventor(s)

Edgar Macias Garcia of Jalisco (MX)

Leobardo Campos Macias of Guadalajara (MX)

Hector Cordourier Maruri of Guadalajara (MX)

Rafael De La Guardia Gonzalez of Leioa (MX)

David Gonzalez Aguirre of Portland OR (US)

Alejandro Ibarra Von Borstel of Buda TX (US)

Paulo Lopez Meyer of Zapopan (MX)

Javier Turek of Beaverton OR (US)

Julio Zamora Esquivel of West Sacramento CA (US)

COBOT MODEL GENERATION BASED ON A GENERIC ROBOT MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240351200 titled 'COBOT MODEL GENERATION BASED ON A GENERIC ROBOT MODEL

Simplified Explanation

The patent application describes an apparatus that can control the movement of a collaborative robot (cobot) by generating a joint trajectory in a generic robot model and mapping it to the cobot model using a trained neural network.

• The interface receives the target end-effector pose of the cobot.
• The processing circuitry generates a joint trajectory in a generic robot model based on the target end-effector pose.
• A trained neural network model is used to map the joint trajectory from the generic robot model to the cobot model.
• A movement instruction is then generated to control the cobot's movement based on the mapped joint trajectory.

Key Features and Innovation

• Interface for receiving target end-effector pose of a cobot.
• Generation of joint trajectory in a generic robot model.
• Utilization of a trained neural network model to map the trajectory to a cobot model.
• Generation of movement instruction for controlling cobot movement.

Potential Applications

This technology can be used in industrial settings for tasks that require precise and coordinated movements of cobots, such as manufacturing, assembly, and logistics.

Problems Solved

This technology addresses the challenge of efficiently controlling the movement of cobots with different degrees of freedom compared to generic robot models.

Benefits

• Improved precision and coordination in cobot movements.
• Enhanced efficiency in industrial tasks.
• Simplified programming and control of cobots.

Commercial Applications

Title: Advanced Control System for Collaborative Robots This technology can be applied in industries such as automotive manufacturing, electronics assembly, and warehouse automation to optimize production processes and increase productivity.

Questions about the Technology

How does the trained neural network model improve the control of cobot movements?

The trained neural network model enhances the accuracy and efficiency of mapping joint trajectories from a generic robot model to a cobot model, ensuring precise and coordinated movements.

What are the potential cost savings associated with implementing this technology in industrial settings?

Implementing this technology can lead to cost savings by improving production efficiency, reducing errors, and streamlining tasks that require the use of cobots.

Original Abstract Submitted

an apparatus, including: an interface configured to receive a target end-effector pose of a cobot; processing circuitry configured to: generate in a generic robot model a joint trajectory based on the target end-effector pose; employ a trained neural network model to map the joint trajectory generated in the generic robot model into a cobot model; and generate a movement instruction to control a movement of the cobot based on the joint trajectory mapped to the cobot model, wherein the generic robot model has a number of degrees of freedom that is equal to or greater than that of the cobot model.