Robotic Vision Systems

As automation technology in warehouses has evolved, Robotic Vision Systems (RVS) have emerged, giving robotic applications “eyes” to “see” by perceiving, interpreting, acting, and learning within their surroundings, mimicking human visual capabilities. This capability has broadened the application of warehouse robotics, as robotic vision systems enable more diverse automation opportunities.

How do Robotic Vision Systems Work?

A robotic vision system (RVS) integrates cameras and sensors with advanced processing software, enabling robots to interpret and interact with their environment effectively. An RVS is made up of several complex, sophisticated processes. The combination of these processes allows robotic vision systems to acquire visual data, process and interpret this data, and then take actions based on the interpretation. Let’s explore each part of this process.

1. Data Acquisition: Cameras and Sensor Data

High-speed, high-tech cameras and sensors serve as the first layer of a robotic vision system. These cameras and sensors capture images and digital representations of the physical world and turn this information into actionable, virtual data sets. Objects, patterns, shapes, colors, anomalies and other relevant data are acquired.

2. Data Processing: Real Time Path Planning and Decision Making

With the virtual data captured above , the data is processed swiftly and accurately. Robotic vision systems use powerful computer processing coupled with advanced software and algorithms to determine the best path for the robot to take in 4 dimensions (x, y, z, and time). This processing capability is also finding multiple plausible moves to make and deciding which next move has the greatest degree of confidence for success. This is all done through software.

3. Action: Execute the Work

Armed with multiple route plans, and a comparative analysis of which move out of a set of possible moves has the highest probability of success, the robot can now execute a specific task such as picking up an object and placing it in a bin.

4. Learn: Improve Performance through Machine Learning

Machine learning algorithms enable robotic vision system applications to learn from data and real world results to improve their object recognition and decision-making over time. This allows for more accurate and efficient picking, sorting, placement, and overall performance. The system adapts and improves on its own without reprogramming or significant input from humans.

Benefits of Robotic Vision Systems

Robotic Vision Systems (RVS) represent a significant advancement in warehouse automation, providing several key benefits:

Increased Accuracy and Efficiency: RVS equips robots with the ability to see and interpret their surroundings, allowing for more precise and efficient picking, sorting, and handling of items. This accuracy reduces errors and boosts overall throughput.
Enhanced Flexibility: The adaptability of robotic vision systems to different environments and tasks makes them highly versatile. Whether sorting diverse product types or handling complex tasks like packaging, RVS adjusts to meet various operational demands without needing extensive reprogramming.
Reduced Operational Costs: By automating tasks that traditionally require human vision and decision-making, RVS decreases the need for manual labor, thereby reducing labor costs and associated expenses.
Improved Safety: Integrating RVS reduces the necessity for human workers to engage in potentially hazardous tasks, enhancing workplace safety. Automated systems can perform high-risk activities with consistent precision, minimizing accidents and improving overall work conditions.

These benefits make Robotic Vision Systems a valuable addition to modern warehouses, driving improvements in operational efficiency, cost management, and safety protocols.

Application of Robotic Vision Systems

The wide variety of applications for robotic vision systems demonstrates why this technology is so valuable for warehouse automations.

Piece Picking

Robots equipped with proper end effectors coupled with a robotic vision system can enable individual piece picking applications for ecommerce fulfillment or any other application requiring the manipulation and control of individual items such as a shirt in a plastic bag, or a tube of toothpaste. The resulting “hand-eye coordination” enabled by both the end effector and robotic vision system unlocks significant new capabilities within the warehouse.

Sorter Induction

Individual items or larger boxes can be automatically acquired and subsequently placed by a robot onto induction belts or directly onto an adjacent sorter. This application would not otherwise be possible without a robotic vision system to acquire relevant sensor data, determine the best acquisition route, and execute the work.

Put Wall Sortation

In many ecommerce fulfillment applications, multiple individual items within an order come from different areas in a warehouse and arrive at different times. A put wall can be used to consolidate these items and are traditionally done manually. Robotic vision systems can automate this task entirely, making an otherwise “extra touch” in the fulfillment process an automated one, thereby reducing labor.

Object Recognition and Sorting

Robotic vision systems excel at recognizing objects, distinguishing between shapes, sizes, and colors. They're capable of sorting items with astounding speed and accuracy, revolutionizing industries like manufacturing and logistics.

Packaging

In the world of e-commerce, packaging individual items into a shipping container is a recurring opportunity to reduce labor. Robotic vision systems can enable more efficient packaging operations by picking and placing items directly into a poly bagger, coldseal line, or corrugated box prior to seal.

Inventory Management

Efficient inventory management is a crucial aspect of many industries. RVS ensure precise tracking, minimizing losses, and ensuring that shelves are stocked with the right items, at the right time.

Types of Robotic Vision Systems (RVS)

There are two primary categories of Robotic Vision Systems (RVS) — 2D Systems and 3D Systems.

2D RVS: These systems focus on capturing flat, static images, much like traditional photographs. They are ideal for tasks that do not require depth perception, such as document scanning or basic object or pattern recognition.
3D RVS: In contrast, 3D RVS add an extra dimension to their vision—the depth perception. This allows them to perceive the world in three dimensions, similar to how humans do. It's like giving machines the ability to gauge distances and spatial relationships, opening up a world of possibilities in areas like autonomous navigation and advanced object recognition.

How are Robotic Visions Systems Used?

Robotic Vision Systems are versatile tools with applications across various industries:

Manufacturing: Picture robots efficiently managing assembly lines, inspecting products with unmatched precision, and ensuring every item meets stringent quality standards.

Logistics: Envision warehouses bustling with automated activity as robots tirelessly move goods, sort packages, and optimize storage space, ensuring seamless supply chain operations.

Agriculture: Imagine autonomous drones hovering over vast fields, assessing crop health, and ensuring efficient planting and harvesting, leading to more sustainable and productive agriculture.

Healthcare: Consider robotic surgical assistants aiding surgeons with unparalleled precision, or autonomous robots delivering medications to patients, improving the accuracy and efficiency of healthcare services.

Retail: Visualize smart shelves that keep track of inventory, replenishing products as they run low, and checkout processes that become frictionless, thanks to automation.

Robotic Vision Systems have transcended their initial niche and are now integral to countless sectors, driving efficiency, accuracy, quality and productivity.