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Accelerating metal part manufacturing with robots

Heavy metallic parts are manipulated by machinery and robots with tasks involving soldering and assembly. The environment is characterized by large robots and slow-communicating large machinery and equipment, with workers overseeing the slow process. In our work with Grupo Celsa we accelerated their robotic vision pipelines by more than 62x, down to milliseconds, enabling faster robot manipulation and real-time quality control.

The challenge

The traditional process of soldering and assembling heavy metallic parts is slow and labor-intensive. These parts often have unstructured surfaces, requiring robots to rely on advanced perception systems powered by computer vision for precise manipulation. The challenge lies in speeding up these perception systems to enhance robotic efficiency and enable real-time quality control.

Identifying bottlenecks

Following a systems architecture approach, we identified the bottlenecks in the perception pipeline, which included the acquisition, 3D comparison, 3D elaboration, preprocessing, segmentation, 3D localization, postprocessing, and point selection stages.

Accelerating perception

We accelerated the perception pipeline by reimplementing the vision processing with modern techniques while leveraging ROBOTCORE® Perception, an optimized robotic perception stack that makes use of hardware acceleration to provide speedups.

Before After After2
Before After

Digital twin-driven

We used digital twins to co-develop the perception system and the robot manipulation tasks in parallel, enabling us to iterate quickly and optimize the system for real-world performance.

Robot eyes

By adopting the perspective of "robot eyes," we harnessed cutting-edge computer vision techniques to enhance robot perception and accelerate manipulation.

state-of-the-art robot simulation

We used state-of-the-art robot simulation tools to create digital twins, selecting the most suitable one for each task.

Going beyond,
perception robustness

We ensured the robustness of the perception system by testing it in a variety of scenarios and environments, including different lighting conditions and surface textures.

62x Faster
Robot Perception

As a result of our work, we achieved a more than 62x speedup in the perception pipeline, enabling faster robot manipulation and real-time quality control. The digital twin-driven development approach allowed us to speed-up the development process and iterate across multiple teams of robotic engineers and software developers.

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