How AI Helps in Material Handling

Logistics Tech Outlook | Monday, October 04, 2021

Artificial technology helps increase the efficiency of material handling by decreasing the expense and the time taken for shipment.

FREMONT, CA: It is estimated that artificial intelligence (AI) will have a seismic impact on logistics, and the technology will increase the value of the industry in the next few years. The increase in value can be accomplished by driving expense, time, and resource efficiencies with the help of smart automation. AI is best defined as a collection of related technologies that share the characteristics of collaborating insights with autonomous actions. Here are some examples of AI technologies and how they are implemented in the materials handling sector.

Autonomous Vehicles & Robots

Autonomous vehicles and picking robots are the most popular examples of AI utilized in material handling. The developing automation of equipment like pallet jacks, wheeled totes, and forklifts is originated from the usage of data analytics, sensor technologies, and camera so that it can take care of decision-making like collision avoidance and data analytics. In the same way, the robotic pricking arms can precision unit sorting tasks that can pack, pick, and sort goods based on conditions such as branding designs, weight, and shape.

The level of autonomy that these systems have is evolving all the time as the technologies develop. For example, while the fixed-paths Autonomous Guided Vehicles (AGVs) have become popular in the worldwide material handling sector in the past few years, the progress of self-driving vehicles (SDVs) is increasing the efficiency of material handling. It is possible because they can move through a warehouse freely and adapt to real-time path changes. The increase in efficiency has also increased the adoption of automation technologies because most organizations are using robotics and automation, while others are planning to embrace it.

Computer Vision

Computer vision is also a part of AI that includes the interpretation of visual data captured by the camera. For example, computer vision utilizes the intelligent robot pickers to identify and sort various items based on visual cues. Autonomous vehicles use computer vision for monitoring the surrounding so that it can avoid collisions.

Other applications involve visual inspection of goods to automate the features of quality control. The technology can also be used for removing the damaged and unsatisfactory items from shipment by combining autonomous vehicles and robotic picking equipment. It has the potential to increase the efficiency of inventory management.

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