In today's industrial landscape, robots have become essential tools in various fields such as welding, assembly, handling, painting, and polishing. As the complexity of tasks increases, so does the demand for higher product quality and efficiency. This has made the programming style, efficiency, and quality of robot programming increasingly important. The ultimate goal of developing robot programming technology is to reduce the difficulty and workload of programming, enhance programming efficiency, and achieve adaptive programming, ultimately boosting production efficiency.
The development and application of programming technologies for industrial robots include three main types: online programming, offline programming, and autonomous programming. While manual teaching still dominates in many robotic welding applications, it becomes inefficient and inaccurate for complex 3D welds. A growing trend is to replace manual teaching with computer-controlled robots guided by visual systems, offering better precision and adaptability.
Online teaching programming involves an operator using a teaching box to guide the robot’s end-effector to specific positions and postures, recording data and generating motion commands. This method is user-friendly and intuitive, as seen in automotive spot welding, where each weld point is manually taught. However, due to variations in vehicle positioning, laser sensors are often used to correct the welding path during actual operations.
Laser-assisted teaching enhances remote control capabilities, especially in environments like space exploration or nuclear facilities, where direct human intervention is impossible. By capturing weld contours, laser vision allows real-time adjustment of the welding torch position. Gao Hongming from Harbin Institute of Technology developed a system that uses laser feature extraction to improve the accuracy of remote teaching for both plane and complex spatial welds.
Force-sensing assisted teaching provides another alternative, particularly when visual feedback is limited. Using force sensors, the robot can identify the weld seam directly, offering high precision and lower cost. This method enables local adaptive control, ensuring accurate remote teaching through shared data and visual presence.
Specialized tools also assist in teaching, such as devices that measure spatial coordinates and postures. These tools allow operators to teach paths intuitively without directly operating the robot, making it accessible for non-professionals. Laser and other sensor-based tools significantly improve flexibility, precision, and efficiency in various applications.
Offline programming offers several advantages over online methods, including reduced downtime, safer working conditions, and easier integration with CAD/CAM systems. Key steps involve 3D modeling, trajectory planning, and simulation. Software like FANUC’s Roboguide helps create precise programs for complex tasks, such as laser cladding on intricate mold surfaces. Commercial offline programming software includes RobotArt, RobotMaster, and RobotStudio, among others.
Autonomous programming leverages advanced sensing technologies like structured light and binocular vision to enable self-teaching. For example, South Korea’s Pyunghyun Kim used structured light sensors to track weld paths and generate smooth trajectories. Multi-sensor fusion systems combine vision, force, and displacement feedback to maintain consistent welding quality.
Augmented reality (AR) programming represents a revolutionary approach, merging virtual and real-world environments. AR allows users to simulate and program robots in a virtual setting before applying them in real situations. This technique is particularly useful when physical prototypes are unavailable, offering a seamless interaction between virtual and real environments.
In conclusion, while traditional online teaching remains relevant in some cases, advancements in technology are pushing the industry toward more efficient and adaptive programming methods. As these technologies evolve, they will continue to reshape the future of industrial robotics.
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