Robot Self-Modification Using Digital Twins and 3D-Printed Resources
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Solution Overview
Problem
Robotic devices in industrial environments often lack sufficient resources to complete tasks effectively, such as insufficient grippers or counterweights, leading to inefficiencies and potential damage during activities like assembly or object handling.
Innovation Solution
A system that utilizes real-time data and historical information to create an AI knowledge corpus, execute digital twin simulations, identify resource deficiencies, and predict necessary modifications using a comparable robotic device, with 3D printing to attach additional resources like grippers or counterweights to the first robotic device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic devices are assigned to perform activities in industrial environments, then productivity is improved, but the devices often lack sufficient resources (grippers, counterweights) leading to task completion failures and potential damage
Solution Approach 1:
The robotic device performs self-modification by automatically receiving, printing, and attaching resources based on its own simulated performance deficiencies. The system enables the robot to self-diagnose resource needs through digital twin simulation and self-provision resources without external intervention, resolving the contradiction between maintaining high productivity and ensuring resource sufficiency.
Solution Approach 2:
The system uses digital twin simulation to predict resource needs before actual task execution. By simulating the robotic device's performance in advance and identifying potential resource deficiencies, the system proactively provisions necessary resources (grippers, counterweights) before they are needed, ensuring reliability without compromising productivity.
2Reliability
If resources are added to robotic devices to ensure task completion, then reliability is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts the robotic device's resource configuration based on simulated performance needs. Rather than statically pre-configuring all possible resources, the system modifies the device's resource set adaptively through digital twin simulation and 3D printing, allowing the device to maintain optimal complexity while ensuring task completion success.
Solution Approach 2:
The system changes the physical parameters of the robotic device by adding or modifying resources (e.g., attaching counterweights, replacing grippers) based on simulated performance data. This parameter-based approach allows reliable task completion while managing complexity through targeted, data-driven modifications rather than comprehensive system redesign.
3Manufacturing precision
If digital twin simulation and AI knowledge corpus are used to predict resource needs, then manufacturing precision of resource allocation is improved, but loss of time for data processing increases
Solution Approach 1:
The system creates a digital twin (virtual copy) of the robotic device to simulate performance and predict resource needs. This copying approach allows high-precision resource allocation analysis to be performed on the virtual model rather than the physical device, minimizing actual downtime while maintaining accurate prediction of resource requirements.
Solution Approach 2:
The system performs AI knowledge corpus creation and digital twin simulation in advance of actual resource provisioning. By pre-processing historical data and simulating various scenarios beforehand, the system reduces real-time decision-making time while maintaining high precision in resource allocation predictions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Dynamically enhances the capabilities of robotic devices by providing necessary resources, ensuring they can complete tasks without incidents, thereby improving industrial machine efficiency and adaptability in multi-machine environments.
Implementation Method 1
one or more resources printed by a 3D printer
Data Source
AI summary
An embodiment for self-development of resources is provided. The embodiment may include receiving data relating to an activity and a first robotic device assigned to perform the activity. The embodiment may also include creating a knowledge corpus of a second set of one or more robotic devices capable of performing the activity. The embodiment may further include executing a digital twin simulation of a digital twin model of the first robotic device performing the activity. The embodiment may also include in response to determining the first robotic device is unable to complete the activity without incident, identifying within the second set of one or more robotic devices a most comparable robotic device to the first robotic device. The embodiment may further include predicting a modification of the first robotic device. The embodiment may also include attaching one or more resources printed by a 3D printer to the first robotic device.