Generic Manufacturing Corp., a renowned manufacturer of Sheet Metal and Tubular Fabricated Assemblies, faced a unique challenge in its manufacturing operations. With over 200 welding robots working in close proximity to human operators, safety concerns loomed large. Conventional safety curtains proved inadequate and led to interruptions due to frequent robot resets. In response, Generic Manufacturing Corp. deployed an innovative Robotic Perception solution. Leveraging Advanced Inspection Camera (AIC) technology on Nvidia Xavier and a custom Robotic Perception Framework, the system used 3D and 2D camera arrays to track human movements in real-time. This enabled robots to understand human 3D locations, detect human presence in unsafe zones, and pause operations for safety. The result was not only enhanced safety but also reduced downtime, fostering seamless collaboration between humans and robots. This case study exemplifies the transformative power of AI-driven Robotic Perception in manufacturing, making human-robot interaction safer and more efficient
Client: A leading manufacturer of Sheet Metal and Tubular Fabricated Assemblies, serving a wide range of industries including Global Automotive OEMs, Heavy Fabrication, Construction Equipment, Off-Road Vehicles, and White Goods.
Problem: The company faced a unique challenge with its 200+ welding robots, as they had to collaborate closely with human operators, posing significant safety risks. Conventional safety curtains proved ineffective and caused frequent interruptions due to robot resets.
Solution: To address these challenges, the company implemented a cutting-edge Robotic Perception solution:
Advanced Inspection Camera (AIC) on Nvidia Xavier: AIC technology was deployed to enhance robotic perception capabilities.
Robotic Perception Framework: The development of a custom Robotic Perception Framework allowed for real-time tracking of human movements.
3D and 2D Camera Arrays: A novel approach utilizing 3D and 2D camera arrays was implemented to track human movements accurately.
Human Feedback to Robots: The system provided real-time feedback to robots, enabling them to understand the 3D locations of human operators and detect when humans entered unsafe zones.
Robot Interface on Modbus: Integration with robots facilitated immediate response and action.
Results:
The results of this implementation were remarkable:
Enhanced Safety: By detecting human presence and pausing robot operations when necessary, the system significantly enhanced safety in human-robot collaboration.
Minimized Downtime: Unlike traditional safety curtains, this solution reduced wastage of productive time due to robot resets, thereby increasing operational efficiency.
Seamless Collaboration: The system allowed for seamless collaboration between humans and robots, fostering a safer and more productive work environment.
In conclusion, the company’s innovative approach to robotic safety through AI-driven Robotic Perception demonstrates the potential of technology to improve safety and productivity in manufacturing. This case study showcases how advanced systems can transform human-robot collaboration, making it safer and more efficient in diverse industrial settings.