Boston, Mass., April 9, 2020—Universal Robots (UR) has launched ActiNav, a new UR+ application kit for companies of all sizes that simplifies the integration of autonomous bin picking of parts and accurate placement in machines using UR cobots. 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, according to UR.
The complexity of automated bin picking is well-known throughout the industry, requiring major efforts in both integration and programming. Today, most bin picking products are solely focused on the vision aspect of bin picking and often require hundreds of lines of additional programming to bridge the gap from “pick” to “place” – especially if the “place” is not just dropping into a box or tote but accurately inserting the part into a fixture for further processing. ActiNav Autonomous Bin Picking changes that, allowing manufacturers with limited or no bin picking deployment expertise to achieve high machine uptime and accurate part placement with few operator interventions, the company says.
ActiNav combines real-time autonomous motion control, collaborative robotics, vision and sensor systems in one kit. The system requires no vision or robotic programming expertise, but is instead based on a “teach-by-demonstration” principle using a six-step, wizard-guided setup process integrated into the UR cobot teach pendant. ActiNav can be deployed by manufacturers’ in-house automation teams or through assistance from a UR distributor or integrator.
“Machine tending has always been one of the mainstay applications for our collaborative robot arms,” said UR’s Vice President of Product and Applications Management Jim Lawton. “We discovered a significant market need for a simple solution that enables UR cobots to autonomously locate and pick parts out of deep bins and place them precisely into a machine. This is not pick and drop; this is accurate pick and part-oriented placement.”
ActiNav is available through UR’s distribution channel and via the new UR+ Application Kits platform, an expansion of the UR+ ecosystem of components certified to work seamlessly with UR cobots. ActiNav works with UR’s UR5e and UR10e e-Series cobots, a UR+ component or user-defined end effector, and application-specific frame or fixture as needed. The kit includes the Autonomous Motion Module (AMM) and ActiNav URCap user interface software, along with a choice of 3D sensors.
While there is a variety of approaches to automating machine tending stations, many of which include implementing trays, bowl feeders or conveyors to get the parts to the machine, Lawton explains how ActiNav bypasses this step. “Parts are often already in bins, so the most flexible and scalable option is to deliver that bin of parts to the machine and then pick them directly from the bin and place them into the machine,” Lawton said. “This minimizes floor space and reduces the need for part-specific tooling.”
ActiNav autonomously inserts parts into CNC or processing machines such as drilling, deburring, welding, trimming or tapping. The high-resolution 3D sensor and CAD matching enables high-accuracy picks powered by ActiNav’s Autonomous Motion Module (AMM) that determines how to pick the part, then controls the robot to pick the part and place it in a fixture each time, according to the company. The autonomous motion control enables ActiNav to operate inside deep bins that hold more parts; something that stand-alone bin picking vision systems struggle to accomplish.
IDC’s Research Director covering robotics, Remy Glaisner, follows the market for automated machine tending solutions. “Today more than ever, technology users are looking to preserve the integrity and continuity of business operations,” he said. “In that context, simplifying the integration or redeployment of highly flexible robotic systems becomes a critical capability for manufacturers and other industrial users. In many ways, ActiNav will set a new level of operational expectations regarding the future of intelligent systems.”