Case Study
Case Study
Industry.AI uses Vision AI (“Trust.AI”) for Enhancing Worker Safety in the Robotic Cells
Industry.AI uses Vision AI (“Trust.AI”) for Enhancing Worker Safety in Robotic Cells
What is Vision AI?
Vision AI integrates digital IP cameras, local or cloud-based servers for computing, machine learning software (ML), and artificial intelligence (AI) to let computers identify and categorize what is occurring in the frames of videos or pictures to provide intelligence that is helpful in retail, manufacturing, construction, security, medicine and other industries.
Background
In modern industrial environments, robotic cells which are designated areas in an industrial environment where robots perform automated tasks play a crucial role in automating complex and labor-intensive tasks. However, these high-risk zones pose significant safety challenges for workers, as robots operate on pre-defined rules without the capability to detect human presence. Traditional safety measures often fall short in preventing accidents when personnel enter these areas while robots are in motion. Recognizing the critical need for enhanced safety, Industry.AI developed Trust.AI, a cutting-edge vision AI solution designed to continuously monitor robotic cells and ensure immediate intervention to protect workers. This case study explores how Trust.AI transforms safety protocols in robotic environments, setting new standards for worker protection and operational efficiency
What is Vision AI?
Vision AI integrates digital IP cameras, local or cloud-based servers for computing, machine learning software (ML), and artificial intelligence (AI) to let computers identify and categorize what is occurring in the frames of videos or pictures to provide intelligence that is helpful in retail, manufacturing, construction, security, medicine and other industries.
Background
In modern industrial environments, robotic cells which are designated areas in an industrial environment where robots perform automated tasks play a crucial role in automating complex and labor-intensive tasks. However, these high-risk zones pose significant safety challenges for workers, as robots operate on pre-defined rules without the capability to detect human presence. Traditional safety measures often fall short in preventing accidents when personnel enter these areas while robots are in motion. Recognizing the critical need for enhanced safety, Industry.AI developed Trust.AI, a cutting-edge vision AI solution designed to continuously monitor robotic cells and ensure immediate intervention to protect workers. This case study explores how Trust.AI transforms safety protocols in robotic environments, setting new standards for worker protection and operational efficiency
Challenges faced by workers around high-risk
robotic cell areas and how AI can help:
Challenges faced by workers around high-risk
robotic cell areas and how AI can help:
Robotic cells are essential in industrial environments for automating complex tasks. However, they pose significant safety risks for workers due to the lack of intelligence in robots to detect human presence. These pre-programmed robots operate based on fixed rules, and any human intervention in their operational zones can lead to severe accidents. Traditional safety measures are often inadequate in preventing such incidents. Some challenges are explained below.
Robotic cells are essential in industrial environments for automating complex tasks. However, they pose significant safety risks for workers due to the lack of intelligence in robots to detect human presence. These pre-programmed robots operate based on fixed rules, and any human intervention in their operational zones can lead to severe accidents. Traditional safety measures are often inadequate in preventing such incidents. Some challenges are explained below.
Operational Hazards: Robotic cells are integral to industrial automation, yet they come
with significant risks due to their inherent limitations. Robots operate on fixed, pre
programmed rules without the intelligence to detect human presence. This limitation
creates hazardous situations, especially when workers inadvertently enter these high-risk
areas while robots are in motion, leading to severe accidents and injuries.
Operational Hazards: Robotic cells are integral to industrial automation, yet they come
with significant risks due to their inherent limitations. Robots operate on fixed, pre
programmed rules without the intelligence to detect human presence. This limitation
creates hazardous situations, especially when workers inadvertently enter these high-risk
areas while robots are in motion, leading to severe accidents and injuries.
Inadequate Traditional Safety Measures: Traditional safety measures, such as physical
barriers and warning signs, often fall short in ensuring worker safety. These measures rely
heavily on human vigilance and compliance, which can be unreliable, especially under
high-pressure conditions or in complex environments. The lack of real-time monitoring
and intervention leaves a significant gap in effective safety management, making it
challenging to prevent accidents proactively
Inadequate Traditional Safety Measures: Traditional safety measures, such as physical
barriers and warning signs, often fall short in ensuring worker safety. These measures rely
heavily on human vigilance and compliance, which can be unreliable, especially under
high-pressure conditions or in complex environments. The lack of real-time monitoring
and intervention leaves a significant gap in effective safety management, making it
challenging to prevent accidents proactively
Dynamic Industrial Environment: The dynamic nature of industrial operations
necessitates constant interaction between workers and robotic cells, which complicates
safety measures. Static safety protocols can be compromised due to the need for flexibility
and adaptability in industrial settings. This situation calls for an advanced, responsive
system that can continuously monitor and react in real-time to ensure worker safety
without hindering operational efficiency.
Dynamic Industrial Environment: The dynamic nature of industrial operations
necessitates constant interaction between workers and robotic cells, which complicates
safety measures. Static safety protocols can be compromised due to the need for flexibility
and adaptability in industrial settings. This situation calls for an advanced, responsive
system that can continuously monitor and react in real-time to ensure worker safety
without hindering operational efficiency.
To address these safety challenges, Industry.AI's Trust.AI leverages advanced vision AI and deep learning technologies. By continuously monitoring robotic cells through surveillance cameras, Trust.AI can detect the presence of workers in real-time. The system then instantly activates voice alerts, halts robotic movement, and displays visual warnings to prevent potential accidents.
To address these safety challenges, Industry.AI's Trust.AI leverages advanced vision AI and deep learning technologies. By continuously monitoring robotic cells through surveillance cameras, Trust.AI can detect the presence of workers in real-time. The system then instantly activates voice alerts, halts robotic movement, and displays visual warnings to prevent potential accidents.
Industry.AI Approach
Industry.AI Approach
To improve safety and overcome these challenges, Industry.AI deployed its Trust.AI
product to drive change.
Connected the camera infrastructure across the robotic cell, evaluated the existing
infrastructure capabilities, and put in place a suitable architecture. The Industry.AI team
used the existing camera infrastructure, and additionally installed cameras in order to
achieve the use cases required. Post installation of the required infrastructure, a camera
mapping exercise was done to ensure the cameras were in the right angle, position and
the network infrastructure supported the required analytics to be done.
The points below explain the steps taken by Industry.AI while deploying the Trust.AI
product:
To improve safety and overcome these challenges, Industry.AI deployed its Trust.AI
product to drive change.
Connected the camera infrastructure across the robotic cell, evaluated the existing
infrastructure capabilities, and put in place a suitable architecture. The Industry.AI team
used the existing camera infrastructure, and additionally installed cameras in order to
achieve the use cases required. Post installation of the required infrastructure, a camera
mapping exercise was done to ensure the cameras were in the right angle, position and
the network infrastructure supported the required analytics to be done.
The points below explain the steps taken by Industry.AI while deploying the Trust.AI
product:
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- Data Collection and Ingestion
• Video feeds from cameras monitor the winding process.
• Data from machine PLCs is periodically collected to support real-time analytics. - Data Preprocessing:
• Video data undergoes normalization (brightness, contrast adjustments), noise reduction, and feature extraction to prepare for analysis. - Computer Vision Techniques:
• Image Processing techniques: such as edge detection and pattern recognition, help detect anomalies.
• Machine learning algorithms enhance the system’s accuracy in detecting potential safety hazards in the robotic cell.
- Data Collection and Ingestion
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- Data Collection and Ingestion
• Video feeds from cameras monitor the winding process.
• Data from machine PLCs is periodically collected to support real-time analytics. - Data Preprocessing:
• Video data undergoes normalization (brightness, contrast adjustments), noise reduction, and feature extraction to prepare for analysis. - Computer Vision Techniques:
• Image Processing techniques: such as edge detection and pattern recognition, help detect anomalies.
• Machine learning algorithms enhance the system’s accuracy in detecting potential safety hazards in the robotic cell.
- Data Collection and Ingestion
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The Key Use Cases included:
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- Real-Time Monitoring:
• Surveillance cameras equipped with vision AI continuously scan robotic cells for any human presence - Immediate Alerts:
• When a worker is detected inside a robotic cell, the system triggers instant voice alerts and visual warnings to ensure quick action. - Robotic Movement Control:
• Trust.AI halts the robotic operations immediately upon detecting human presence, preventing any possible injuries. - Seamless Integration :
• The solution integrates with robotic cell gates, robot controls, public announcement systems, and visual displays to create a comprehensive safety net.
- Real-Time Monitoring:
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The Key Use Cases included:
-
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-
-
- Real-Time Monitoring:
• Surveillance cameras equipped with vision AI continuously scan robotic cells for any human presence - Immediate Alerts:
• When a worker is detected inside a robotic cell, the system triggers instant voice alerts and visual warnings to ensure quick action. - Robotic Movement Control:
• Trust.AI halts the robotic operations immediately upon detecting human presence, preventing any possible injuries. - Seamless Integration :
• The solution integrates with robotic cell gates, robot controls, public announcement systems, and visual displays to create a comprehensive safety net.
- Real-Time Monitoring:
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Benefits
Benefits
Enhanced Worker Safety:
By preventing accidents in real-time, Trust.AI
significantly enhances worker safety in industrial environments.
Operational Efficiency: Reduced downtime due to accidents leads to improved
operational efficiency and productivity.
Compliance:
Trust.AI helps companies comply with stringent safety regulations
and standards.
Cost Savings: Preventing accidents and ensuring safety reduces the costs
associated with workplace injuries and legal liabilities.
Enhanced Worker Safety: By preventing accidents in real-time, Trust.AI
significantly enhances worker safety in industrial environments.
Operational Efficiency: Reduced downtime due to accidents leads to improved
operational efficiency and productivity.
Compliance: Trust.AI helps companies comply with stringent safety regulations
and standards.
Cost Savings: Preventing accidents and ensuring safety reduces the costs
associated with workplace injuries and legal liabilities.
Results / Conclusion:
Industry.AI’s Trust.AI is revolutionizing safety standards in robotic environments. By leveraging vision AI to monitor and control robotic cell operations, Trust.AI ensures a safer workplace for industrial workers. This advanced solution not only enhances worker safety
but also boosts operational efficiency and compliance with safety regulations. Trust.AI exemplifies how AI can transform safety protocols in high-risk environments, safeguarding
lives and setting new benchmarks for industrial safety.
Results / Conclusion:
Industry.AI’s Trust.AI is revolutionizing safety standards in robotic environments. By leveraging vision AI to monitor and control robotic cell operations, Trust.AI ensures a safer
workplace for industrial workers. This advanced solution not only enhances worker safety
but also boosts operational efficiency and compliance with safety regulations. Trust.AI
exemplifies how AI can transform safety protocols in high-risk environments, safeguarding
lives and setting new benchmarks for industrial safety.