![](\"https:\/\/www.inpro.de\/wp-content\/uploads\/2024\/09\/AdobeStock_646241885.jpeg\" "\\"“\u672a\u6765\u306e\u81ea\u52d5\u5316:")
<\/span>Robotics 4.0 in focus<\/h1><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Automation & robotics has been a key topic<\/a> at inpro for many years, which we continue to develop in partnership-based innovation projects and put into practice with specific digital solutions such as ginero<\/a> – multi-robot cell planning for system planners.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>\nIn this article, we we focus on this for you:<\/p>\n
\n- Which robotics trends are accelerating innovation progress in the manufacturing industry<\/li>\n
- Which applications are currently relevant for the use of computer vision in car body construction<\/li>\n
- What technical requirements must be met for the use of computer vision in modern robotics<\/li>\n
- Which complementary technologies and methods are used to plan combined computer vision systems and robots<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>
Robotics trends 2030: Flexibility in automation<\/h1>\n<\/div>According to the latest report from the International Federation of Robotics<\/span>,<\/a> computer vision is one of the most important drivers of innovation in modern robotics after artificial intelligence. The average annual growth rate of this technology is 18%. Mordor<\/span><\/a> analysts forecast a market volume of 40 billion US dollars for the year 2029. Other robotics trends such as mobile manipulators and the digital twin cannot be viewed in isolation from modern sensor technology and machine vision. Together, these technologies contribute to intelligent, autonomous industrial robots and can address challenges such as the shortage of skilled workers and cost pressure in a solution-oriented manner.Fast progress in digitalization and investments can currently be observed in robot-assisted handling, as e.g. in order picking systems in logistics.<\/p>\n<\/div><\/div><\/div>Manual vs. automated parts handling in automotive production<\/h1><\/div><\/div><\/div><\/div>\nIn carbody manufacturing, the largest share of manual activities is caused by parts handling and therefore represents the largest cost block with potential for optimization. According to our estimates, 100,000 euros can be saved in a typical production cell by automating manual processes. We assume that 70 vehicles are produced, for each of which 400 parts have to be assembled in four different configurations. On the other hand, the investment costs for equipping the robots with the necessary computer vision capabilities and providing the production planners with the virtual planning and control environment are feasible. Only 20% of the costs are attributable to the additional hardware required, 75% are pure software licenses and a further 5% are needed for service and consulting. With solutions available on the market such as Siemens Tecnomatix <\/a>and proprietary add-on solutions for setting up the computer vision system, e.g. from sensor specialist Sick<\/a>, the entire picking process in production can be digitally safeguarded during planning, i.e. the right combination of robot, effector, sensor system and component can be selected. Selecting the right combination is necessary to ensure that it can replace the skills of human labor.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/www.inpro.de\/wp-content\/uploads\/2024\/09\/AdobeStock_624872266-scaled.jpeg\" "\\"Humanoid")
<\/span><\/div><\/div><\/div><\/div><\/div><\/div>Machine learning of computer vision robot systems<\/h1>\n<\/div><\/div>\nThe integration of camera systems and sensors is necessary in order to achieve the highest possible degree of automation in car body construction. In order for robots to ideally achieve a 100% recognition rate of components and their correct placement, they must be trained accordingly. In order to achieve the optimum cycle time and guarantee 100% achievement of the automation task set, the following must be taken into account:<\/p>\n
\n- How can the design of the production cell be optimized for robot use?<\/li>\n
- Which and how many sensors are needed to ensure that all parts can be detected?<\/li>\n
- How must the interaction between the robot controller, the effectors (grippers) and the sensor system be designed for optimum process sequences?<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div>
The establishment of vision systems in the manufacturing industry<\/h1>\n<\/div>While the layout planning of production cells and the offline programming and validation of robots is still carried out as standard today without the special integration of image processing systems, we assume that this will be an integral part of the planning phase in the near future. Several technological trends are helping to ensure that this combination for safeguarding computer vision robots in engineering will quickly become established and that nothing will stand in the way of these systems becoming established on the store floor. In detail, these are<\/p>\n<\/div><\/div><\/div>
Automation & robotics has been a key topic<\/a> at inpro for many years, which we continue to develop in partnership-based innovation projects and put into practice with specific digital solutions such as ginero<\/a> – multi-robot cell planning for system planners.<\/p>\n<\/div><\/div><\/div><\/div><\/div> In this article, we we focus on this for you:<\/p>\n According to the latest report from the International Federation of Robotics<\/span>,<\/a> computer vision is one of the most important drivers of innovation in modern robotics after artificial intelligence. The average annual growth rate of this technology is 18%. Mordor<\/span><\/a> analysts forecast a market volume of 40 billion US dollars for the year 2029. Other robotics trends such as mobile manipulators and the digital twin cannot be viewed in isolation from modern sensor technology and machine vision. Together, these technologies contribute to intelligent, autonomous industrial robots and can address challenges such as the shortage of skilled workers and cost pressure in a solution-oriented manner.Fast progress in digitalization and investments can currently be observed in robot-assisted handling, as e.g. in order picking systems in logistics.<\/p>\n<\/div><\/div><\/div> In carbody manufacturing, the largest share of manual activities is caused by parts handling and therefore represents the largest cost block with potential for optimization. According to our estimates, 100,000 euros can be saved in a typical production cell by automating manual processes. We assume that 70 vehicles are produced, for each of which 400 parts have to be assembled in four different configurations. On the other hand, the investment costs for equipping the robots with the necessary computer vision capabilities and providing the production planners with the virtual planning and control environment are feasible. Only 20% of the costs are attributable to the additional hardware required, 75% are pure software licenses and a further 5% are needed for service and consulting. With solutions available on the market such as Siemens Tecnomatix <\/a>and proprietary add-on solutions for setting up the computer vision system, e.g. from sensor specialist Sick<\/a>, the entire picking process in production can be digitally safeguarded during planning, i.e. the right combination of robot, effector, sensor system and component can be selected. Selecting the right combination is necessary to ensure that it can replace the skills of human labor.<\/p>\n<\/div><\/div><\/div> The integration of camera systems and sensors is necessary in order to achieve the highest possible degree of automation in car body construction. In order for robots to ideally achieve a 100% recognition rate of components and their correct placement, they must be trained accordingly. In order to achieve the optimum cycle time and guarantee 100% achievement of the automation task set, the following must be taken into account:<\/p>\n While the layout planning of production cells and the offline programming and validation of robots is still carried out as standard today without the special integration of image processing systems, we assume that this will be an integral part of the planning phase in the near future. Several technological trends are helping to ensure that this combination for safeguarding computer vision robots in engineering will quickly become established and that nothing will stand in the way of these systems becoming established on the store floor. In detail, these are<\/p>\n<\/div><\/div><\/div>\n
Robotics trends 2030: Flexibility in automation<\/h1>\n<\/div>
Manual vs. automated parts handling in automotive production<\/h1><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/div>Machine learning of computer vision robot systems<\/h1>\n<\/div>
\n
The establishment of vision systems in the manufacturing industry<\/h1>\n<\/div>