Two of the most critical success factors in the manufacturing industry are time to deployment and cycle time. Despite this, the design and deployment of robotic workcells has long remained a surprisingly manual and time-consuming process. Realtime Robotics is aiming to change that with Resolver, a cloud-based optimization engine that introduces industrial-scale automation into the earliest stages of robotic system planning.
At its core, Resolver addresses some of the most persistent engineering challenges in workcell design: motion planning, robot task allocation, target sequence optimization, and layout validation. Traditionally, these steps require iterative tweaking, deep domain expertise, and a significant investment of time and resources to get right and be able to deliver on time. Resolver replaces that trial-and-error approach with intelligent automation. As it runs, the engine explores thousands of potential options to deliver an increasingly optimized result; one that balances performance, accuracy, and feasibility – and does so within minutes.
This kind of computational efficiency opens new doors for how teams approach the design process. Rather than being limited by what’s manually achievable, engineers can let Resolver handle the mechanical complexity and instead focus on higher-level goals such as throughput, safety, or flexibility. Resolver adapts to a range of use cases, from greenfield line builds to individual cell retrofits, making it broadly applicable across industries and production scales. And it can do all this in mere minutes – faster than what’s humanly possible.
Recent integrations with leading 3D simulation platforms including Siemens Process Simulate, Visual Components, and Mitsubishi Electric’s MELSOFT Gemini, enable users to access Resolver’s capabilities directly within their preferred simulation environments. This embedded approach reflects a broader shift toward interoperability and hybrid workflows in advanced manufacturing, where simulation, design, and optimization are increasingly converging.
Early adopters, particularly in automotive manufacturing, have already reported cycle time improvements ranging from 15% to 40%, along with faster deployments and fewer errors. These outcomes suggest that Resolver is not just a point solution, but part of a larger movement toward AI-assisted engineering. A future where decision-making is augmented, not replaced, by automation.
Post provided by: Realtime Robotics – www.rtr.ai
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