Robotics and AI technology have proven useful for the material recovery and separation of construction and demolition (C&D) waste and debris. Here’s a look at some of the technology C&D operations can leverage for their unique needs.
AMP Robotics Corp. launched a new AI-guided dual-robot system for recyclers, the AMP Cortex dual-robot system (DRS), focused on material recovery in municipal solid waste, electronic waste (e-waste) and C&D. The AMP Cortex DRS expands on its existing product line of high-speed recycling robotics guided by the AMP Neuron AI platform and uses two high-performance robots that rapidly sort, pick and place materials at a speed of 160 pieces per minute. AMP Neuron uses computer vision and machine learning to recognize different colors, textures, shapes, sizes and patterns to identify material characteristics. Then, it directs the robots to pick and place the targeted material.
The design of the two robots opens new material applications, namely the ability to process difficult material streams of post-consumer fiber. From sheets of paper to cardboard, sorting fiber is a major challenge for recycling lines, often becoming a contaminant for other recycled commodities. By solving this challenge, AMP said its technology improves the purity of materials to be recycled, while also increasing the recycling rates of post-consumer recycled fiber overall.
Additionally, during the “Robots & Recycling: A Dynamic Duo” session, Will Hancock of Plexus Recycling Technologies said the company developed a demolition processing operation and recently built a state-of-the-art plant that processes roughly 65 tons an hour. Plexus, which concentrates on the C&D market, has a robotics waste sorting system. But there are preliminary steps before its Zen robot does its job, including shredding materials to get them to a manageable size and a ballistic separator that eliminates 2D materials before the robot receives what’s remaining.
Rock, brick, wood and metals bounce backward and on to a separate line. They are bunker fed to the robots that sort the material. The AI system realizes what it picked wrong and reclassifies. Robots can be controlled from devices and operators can change applications.