In modern systems, "pick-and-place" is performed by automated grippers. They must be able to operate both powerfully and delicately and do so precisely and reliably millions of times over. Increasingly, the necessary power comes from electric motors.
From a machine builder's point of view, robots can solve many processing and packaging line problems, but the chosen material handling systems supplying the line can reduce the robot's performance.
Solving the challenge of the last mile using traditional methods, like human teams and traditional automation systems, can require a massive upfront investment of capital and time - and often isn't enough to gain an edge in an already cutthroat market.
Watch this MIT startup's robot unload a trailer blazingly fast. The secret? Keep people in the picture.
In today's e-commerce environment, consumers are purchasing single items with a click and that item-handling task has been pushed back upstream into distribution and fulfillment centers.
Stretch's mobile base allows it to go to where repetitive box lifting is required - unloading trucks, building pallets of boxes and order building. Stretch makes warehouse operations more efficient and safer for workers.
With more than 70% of labor in warehousing being dedicated to picking and packing, numerous companies are gradually investing in logistics automation. But what happens when the robots must handle an unlimited number of (unknown) stock keeping units?
Pick-by-Vision adds to smart technology because smart glasses learn manufacturers' parts; they memorize the visual appearance including robots and bin locations. Pick-by-Vision predicts and prevents mis-picks and decides the best part to pick next.
A foundry, making different car suspension parts, was using a gripper with a dust cover to grind and deburr parts out of a press. They were having issues with the gripper and getting debris in the guideways and not being able to actuate the gripper.
Universal Robots Launches ActiNav, the World's First Autonomous Bin Picking Kit for Machine Tending Applications
ActiNav synchronously handles vision processing, collision-free motion planning and autonomous real-time robot control, eliminating the complexity and risk usually associated with bin picking applications.
At the Institute for Intelligent Process Automation and Robotics of the Karlsruhe Institute of Technology (KIT), the Robot Learning Group (ROLE) focuses on various aspects of machine learning. The scientists are investigating how robots can learn to solve tasks by trying them out independently.
Robots aren't always given a favorable representation in pop culture. In Stanley Kubrick's 2001: A Space Odyssey (1968), homicidal supercomputer HAL 9000 demonstrates how a robot could conspire against its human colleagues.
Picking is a specific, repetitive task that takes place in every fulfillment warehouse. It's also one of the biggest expenses, often making up more than half of operational costs.
This Honeywell Intelligrated white paper examines the specific labor, operational and technological challenges on the loading docks of distribution centers (DCs), and introduces a new solution to help deliver the performance modern supply chains require.
Winner of Fetch Robotics FetchIt! Challenge Executes Complex Manufacturing Tasks Using Autonomous Mobile Robotic Arm
Georgia Institute of Technology Awarded Prize Package Worth Over $150K In Inaugural Contest Hosted at IEEE Conference on Robotics and Automation (ICRA)
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OCTOPUZ makes complex robot programming simple through cutting-edge Offline Robot Programming Software (OLRP) that enables detailed robot operations, complete with machinery and manufacturing components, to be first simulated on a computer, then output for the real world. Within this virtual cell, OCTOPUZ uses built-in machine logic to identify the optimal toolpath trajectory and program the required code for a multitude of industrial tasks. The code is then output for the specific robot brand, for use in the real world.