Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks

Abstract Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence. However, the low structural stiffness a robot significantly affects its positional accuracy quality operation equipment. Studying characteristics optimization methods is an effective method improving performance robot. Accordingly, aiming at poor modeling caused by approximating each joint as constant, variable identification proposed based on space gridding. Subsequently, task-oriented axial evaluation index to quantitatively assess direction. In addition, analyzing redundant kinematic system, configuration further developed maximize index. For numerous points or trajectory-processing tasks, smoothing strategy rapidly acquire optimized configurations. Finally, experiments KR500 were conducted verify feasibility validity methods.