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Adaptive Robot Grasping with AI and Computer Vision: testing and experimentation

Service description

Integrating AI with computer vision to develop adaptive grasping systems for collaborative robots used in production lines allows these systems to identify, grasp, and manipulate objects of various shapes and sizes with precision, improving efficiency and reducing the risk of product damage.
Convolutional neural networks (CNNs) can be used to train AI models to recognize and classify objects in real-time, which is useful for applications like automated picking and component arrangement.Additionally, implementing tactile and force sensors in grasping systems provides real-time feedback to the AI, enabling more secure and adaptive object handling. The AI can adjust the grasp’s force and position based on data received from the sensors.

The aim of this service is to validate early-adoption or ad hoc development, as well as benchmark non-AI vs. AI-proposed benefits.
Expected results: Improved model performance, improved plant performance, reduced latency, better resource utilization, enhanced scalability, and seamless integration across cloud and edge environments for real-time AI processing.
Methodology: Assessment, Needs Assessment, Model Training, AI-Deployment, Cloud Integration, Performance Monitoring.
Target: Manufacturing
  • Organisation: REPLY
  • Type: Test
  • SUT/E: AI-based computer vision systems
  • Average duration: 4.0 -- 6.0 months
  • Contact: a.riccio@reply.it

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Co-Funded by the European Union Under grant agreement number 101100707

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Directorate-General for Communications Networks, Content and Technology. Neither the European Union nor the granting authority can be held responsible for them.

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