Bin Picking in 1 Minute with AI and 3D Vision

Introduction to Vision-Based Bin Picking

As automated industrial processes become more complex, the need for precise and efficient object handling is more critical than ever. Traditional 2D vision systems often struggle with irregular shapes or semi-transparent materials, limiting their effectiveness. Integrating AI with 3D vision technologies enables robots to comprehensively understand object positioning and orientation. This combination enhances robotic bin picking, improving accuracy and speed. This blog explores how advanced 3D vision techniques and AI integration empower robots to perform complex picking and material handling tasks with ease.

2D vs 3D Machine Vision

2D machine vision provides a flat, two-dimensional view of an object, focusing on its position along the X and Y axes. 3D machine vision, on the other hand, adds depth with the Z-axis, providing a fuller, three-dimensional understanding of objects. With the rise of Industry 4.0, integrating 3D vision with robotic systems has become increasingly popular, driving advancements in manufacturing processes. While 2D imaging is typically captured using standard industrial cameras, 3D imaging requires specialized optical technologies. Common techniques for obtaining 3D data include Active Stereo Vision, Time-of-Flight, and Structured Light.
red icon representing stereo vision

Stereo Vision

Stereo vision mimics human depth perception by capturing both an object’s position and its three-dimensional structure. It offers key benefits, including low energy consumption and cost efficiency, making it an attractive option for industrial applications. However, stereo vision can experience delays, and its effectiveness can be reduced in low-light environments. In addition to its use in robotics, stereo vision is being integrated into AR + AI systems for advanced visual inspection and object recognition.

Time-of-Flight

Time-of-Flight (TOF) technology measures the distance to objects by calculating the time it takes for infrared light to travel to the object and return. This method offers fast processing speeds and is resistant to interference, making it well-suited for dynamic environments. However, while TOF is efficient, its accuracy can be lower than other 3D vision technologies.

Structured Light

Structured light projects distinct light patterns, typically alternating black-and-white stripes, onto objects. The deformation of these patterns as they interact with the object’s surface allows the system to map its shape and capture precise depth information along the Z-axis. This technology is commonly used in applications like facial recognition and quality control, particularly in manufacturing through Automatic Optical Inspection (AOI) systems.

Enhancing Robotic Object Recognition with AI

While 3D vision provides valuable spatial data, relying solely on it to recognize complex or irregularly shaped objects can still present challenges. For example, 3D vision may struggle to accurately detect semi-transparent items or precisely handle small, intricate metal parts with robots.

Integrating AI with 3D vision systems enhances the ability of robots to recognize and manipulate objects with greater precision. This combination allows robots to effectively pick and place irregularly shaped items, ensuring correct orientation and positioning.

In cases involving complex shapes—such as stacking U-shaped metal pieces—3D vision alone may fail to accurately align and position the objects. AI fills this gap by enabling the system to understand the objects’ true shape and orientation, ensuring precise handling.

Train an AI Model in 60 Seconds

Integrating deep learning AI with 3D vision technology involves more than just object recognition—it requires seamlessly coordinating processes like gripping, path planning, and motion control to enable efficient robotic operations.

Solomon’s deep learning AI, combined with advanced motion planning modules, enables robots to learn to identify and interact with objects in as little as 60 seconds. This allows the bin picking system to quickly determine the optimal path for grasping and placement while avoiding obstacles, ensuring accuracy and efficiency.

This powerful integration offers a practical, cost-effective solution for complex tasks like mixed depalletizing, kitting, packing, and random bin picking, making it an ideal fit for dynamic industrial environments.
an ABB robot on an automated assembly line arm conducts a kitting task using Solomon AI and 3D vision technology