Robot Learning From Human Demonstration for Industrial Inspection
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Solution Overview
Problem
Industrial tasks such as asset inspection and maintenance often require human intervention, which can be time-consuming, labor-intensive, and unsafe, especially in hard-to-reach locations, and existing technologies lack efficient automation for learning and performing these tasks autonomously.
Innovation Solution
A system that uses robots and unmanned vehicles to learn industrial tasks through human demonstration, employing machine learning and a remote server with data processing and communication capabilities, allowing them to perform tasks with minimal human oversight by generating adaptive flight plans and utilizing sensor data for inspection and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators perform industrial tasks manually, then task completion is achieved, but time consumption and labor intensity increase
Solution Approach 1:
The robotic system performs industrial tasks autonomously without continuous human intervention. The robot learns from human demonstrations and executes tasks independently, including navigation to assets, inspection, and manipulation operations, thereby eliminating the need for operators to perform these repetitive tasks manually.
Solution Approach 2:
The patent replaces human operators with an autonomous robotic system equipped with sensors, actuators, and machine learning algorithms. The robot substitutes human physical presence and manual operations with automated mechanical systems, enabling continuous operation without fatigue or time constraints associated with human workers.
2Reliability
If human operators perform tasks in hard-to-reach locations, then inspection and maintenance are completed, but safety risks increase
Solution Approach 1:
The robotic system autonomously navigates to and performs tasks in dangerous or hard-to-reach locations such as confined spaces, heights, or areas with hazardous materials. The robot eliminates the need for human operators to physically enter these hazardous environments, thereby removing safety risks while maintaining task completion reliability through autonomous operation.
Solution Approach 2:
The patent substitutes human operators with an autonomous robot equipped with specialized sensors and manipulation tools. The robotic system can safely operate in environments that are unsafe for humans, including areas with toxic substances, extreme temperatures, or physical hazards, thereby eliminating exposure to harmful factors while maintaining operational reliability.
3Extent of automation
If robots perform tasks autonomously, then human intervention is reduced, but system complexity increases
Solution Approach 1:
The robotic system is designed with multi-functionality to perform various industrial tasks including navigation, inspection, manipulation, and data collection. By integrating multiple functions into a single autonomous platform, the system achieves high extent of automation while managing complexity through unified architecture rather than separate specialized systems.
Solution Approach 2:
The patent employs machine learning algorithms and sensor data processing as intermediaries between the robot's sensors and actuators. These intermediary systems interpret sensor inputs, make decisions, and control actuators, thereby managing the complexity of autonomous operation through layered processing rather than direct control, which simplifies the overall system architecture.
Data Source
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AI summary
A system for performing industrial tasks includes a robot and a computing device. The robot includes one or more sensors that collect data corresponding to the robot and an environment surrounding the robot. The computing device includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the collected data from the robot, generate a virtual recreation of the robot and the environment surrounding the robot, receive inputs from a human operator controlling the robot to demonstrate an industrial task. The system is configured to learn how to perform the industrial task based on the human operator's demonstration of the task, and perform, via the robot, the industrial task autonomously or semi-autonomously.