In today’s industrial landscape, robots are widely utilized across various applications such as welding, assembly, handling, painting, and polishing. As the complexity of tasks increases, there is a growing demand for higher product quality and operational efficiency. Consequently, the programming methods, efficiency, and quality of robotic systems have become increasingly critical. The ultimate goal in the development of robot programming technology is to reduce the difficulty and workload involved in programming, enhance programming efficiency, and achieve adaptive programming to boost overall production performance.
The development and application of programming technologies for industrial robots include three main approaches: online programming, offline programming, and autonomous programming. While manual teaching still dominates in many areas of robot welding, offline programming is more suitable for structured environments. However, for complex 3D welds with intricate paths, manual teaching becomes inefficient and may not meet precision requirements. A promising trend is replacing manual teaching with computer-controlled robots guided by visual systems.
Online teaching programming involves an operator using a teaching box to guide the robot’s end-effector to specific positions and postures, recording joint data and motion commands. This method is user-friendly and intuitive. For example, in automotive spot welding, operators manually teach each weld point, then reproduce the path during actual welding. However, variations in vehicle body positioning often require additional laser sensors to correct the welding path.
Laser vision-assisted teaching has been developed to address challenges in remote welding scenarios, such as space exploration or nuclear plant repairs, where direct human intervention is impossible. Laser sensing can capture weld contour information in real-time, enabling the robot to adjust its position accordingly. Gao Hongming from Harbin Institute of Technology proposed a laser-assisted remote teaching technique that improves accuracy in both planar and complex spatial welds.
Force-sensing assisted teaching offers another alternative, particularly when visual feedback is limited. By directly contacting the weld seam, force sensors provide high-precision identification, allowing for local adaptive control and macro-level monitoring during remote operations.
Specialized tools, such as position and attitude measuring units, are also used to make 3D teaching more intuitive. These devices enable operators to easily record and convert spatial coordinates into robot movement paths, significantly improving ease of use and accuracy.
Offline programming offers several advantages over online methods, including reduced downtime, improved safety, broader application scope, and easier integration with CAD/CAM systems. Key steps involve 3D modeling of the work environment, trajectory planning, and simulation before downloading the program to the robot. Software like FANUC’s Roboguide allows for efficient offline programming, especially for complex surfaces that are difficult to teach manually.
Autonomous programming, leveraging technologies like structured light and binocular vision, enables robots to self-adjust their paths based on real-time sensor feedback. Multi-sensor fusion further enhances accuracy by combining visual, force, and displacement data to maintain optimal welding conditions.
Augmented reality (AR) programming represents a revolutionary approach, merging virtual and real environments to allow users to interactively generate robot programs. This method eliminates the need for physical prototypes and streamlines the programming process, making it highly effective in real-world settings.
In summary, while traditional online teaching remains relevant in some cases, advancements in technology are pushing the industry toward more efficient, adaptive, and intelligent programming solutions. As these technologies evolve, they will continue to reshape the future of industrial robotics.
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