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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Weed that grow among crops is a problem in many ways since humanity started agriculture. Weeds compete with crops for water, soil nutrients, sunlight. They can host pets, harbor diseases. They cost labor to remove them, either manually or chemically. This also increases overall costs and chemical removal may mean environmental impacts. Overall, weeds cause lower crop yield, decrease in quality, and higher costs. Considering all these, it is a very critical task, to remove them as efficiently as possible, which is where robots can be very useful and bring the costs down.
A robot that is developed in Spain, which is called “The GreenBot” aims to undertake such task. The robot is still in development stage but according to the press release provided by the team, it completed its successful field trials. The robot is developed by GMV (www.gmv.com), and a consortium made of University of Seville’s AGR-278 “Smart Biosystems Laboratory” research group, GMV, TEPRO, PIONEER HiBred Spain SL, and Cooperativas Agroalimentarias de Andalucía, where each participant undertook tasks belonging to different disciplines. The collaboration was initially scheduled to continue for 21 months, which concluded end of June, 2025.
The robot is basically a robotic vehicle and a robotic arm, equipped with AI, autonomous navigation and machine vision technologies, which are all essential to accurately identify and treat weeds such as the ones that grow near almond, citrus and olive trees.
During field tests, the robot effectively completed its tasks under different light, soil and plant combinations. Detection of smaller weeds under shade however, still remains a challenge, which the team plans to tackle by training the model with further data. The robot operates in real time, with an inference frequency of 1 second per image. This eliminated the need of using external servers, and enabled seamless integration between perception, navigation and application. The robot runs with the popular open source operating system ROS2 (Robot Operating System).
The robot basically works by approaching the tree, encircling the trunk by its robotic arm, and while further movement of the robot body (basically the vehicle) still continues, the half circular arm sprays precisely targeted chemicals on identified weeds. This not only automates weed treatment but also significantly reduces the use of chemicals, and hence, the environmental impact. The weed detection core, which was developed by the University of Seville, can identify position, species and dimensions of weeds within a tolerance of 2 cm.
The project was funded by grants for European Innovation Partnership (EIP) Operational Groups, within the framework of Rural Development Program of Andalusia, which operates under Spanish Ministry of Agriculture.
The project specific details in this post were obtained from a press release shared by Ariadne Comunicación (www.ariadne.es), who handles press communications for GMV (www.gmv.com), the maker of the robot.
Post By: A. Tuter
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