Collaborative robots, AI and machine vision boost agricultural and manufacturing capabilities

The combination of artificial intelligence (AI) and machine vision is generating more practical applications for collaborative robots (cobots), particularly in modern agricultural and manufacturing scenarios.

By Yvonne Zhang March 22, 2024
Courtesy: Chris Vavra, CFE Media and Technology

Collaborative robot insights

  • The article discusses the rising prominence of AI and machine vision in collaborative robots (cobots), enhancing their applications in agricultural and manufacturing sectors, with examples including Denso Robotics’ Cobotta Pro and Kane Robotics’ precision in operations.
  • It highlights specific advancements in agriculture, such as a tomato-picking robot using generative AI and machine vision for improved efficiency and precision in tasks like strawberry picking and cabbage harvesting, indicating a significant shift towards automated agricultural practices.

The application of AI and machine vision in collaborative robots is gradually becoming more prominent

The application scenarios of AI and machine vision in collaborative robots are gradually expanding, with increasing penetration rates. Machine vision can assist collaborative robots in more accurately identifying and tracking targets. Combined with artificial intelligence decision-making capabilities, collaborative robots can quickly learn and optimize methods for task execution, achieving higher efficiency in task completion.

Here are several practical examples:

Denso Robotics showcased its new collaborative robot, Cobotta Pro, along with a vision system for scooter assembly at the 2023 iREX Exhibition. This demonstration highlighted the advantages of integrating artificial intelligence and vision systems into collaborative robots: they can read QR codes, perform intelligent position correction, and recognize human commands through a voice-controlled IPC, allowing flexible switching of assembly steps. Collaborative robots can accurately grip the frame and work in coordination with workers to assemble tires and handlebars.

Kane Robotics from the United States has also combined artificial intelligence with machine vision, enabling its collaborative robots to automatically track and polish weld seams with high precision and speed, showcasing the potential of AI in fine operations.

Doosan Robotics and AiV, a South Korean industrial deep learning computer vision technology company, jointly introduced the new Otto Matic palletizing system, which applies a combination of AI, machine vision and collaborative robots to the palletizing process. This system can handle unstructured and randomly sized boxes to improve the efficiency of logistics automation.

AI, machine vision and cobots are being implemented in modern agricultural applications.

With the advancement of technology, collaborative robots equipped with vision systems and artificial intelligence are gradually changing traditional agricultural practices and are being applied to various agricultural picking applications.

In 2023, research teams from the Netherlands and Switzerland successfully created a tomato-picking robot pairing generative artificial intelligence ChatGPT with a machine vision system. This robot captures images through cameras and utilizes ChatGPT for image recognition. Meanwhile, ChatGPT can communicate in real-time with researchers, asking questions about tomato ripeness, picking techniques, and more, making decisions based on the information received. This highly intelligent collaborative approach enables the cobot to accurately identify and pick ripe tomatoes, introducing new picking capabilities to modern agriculture.

In addition to research teams, collaborative robot companies have also begun to explore the integration of AI, machine vision, and collaborative robot technologies in practical applications. For example, Flexiv Robotics has applied these technologies to cabbage harvesting.

Interact Analysis is a CFE Media and Technology content partner.

Original content can be found at Interact Analysis.

Author Bio: Yvonne joined Interact Analysis as a Research Associate to assist the research team with organizing, interpreting findings, and enhancing product outputs.