A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Large industrial robots are inexpensive compared to their workspace. Due to flexibility, the positioning accuracy is compromised. This paper presents a simple yet effective static calibration method for robots using a laser tracker that can be applied to processes with no change in external load and without high processing forces. The calibration method is based on a novel elastokinematic model that captures the nonlinear gearbox and link compliance and also accounts for the effects of pose dependent joint friction and nonlinearities due to the gravity compensation mechanism. Furthermore, a feedforward concept is presented that separates kinematics from compliance to reduce computation time. The calibration method was implemented on industrial hardware and experimentally validated on a KUKA KR210–2. In comparison to state of the art compliance modeling, accuracy is improved by 23.1 %. Finally, the feedforward model reduces the mean positioning error in the entire workspace by 54.9 % compared to the purely kinematically calibrated model, emphasizing the need for compliance compensation. Note to Practitioners—Industrial processes such as bonding, welding, quality inspection, and aircraft assembly require high path and positioning accuracy. This is often accomplished by programming paths offline. Due to inaccuracies, these programs often require manual corrections, which is undesirable in an era when production aims for greater customization. Another possibility is to utilize additional, costly sensors. Especially for large industrial robots, the inaccuracy increases as the lever arms and component masses increase due to the additional load on the drivetrains. Therefore, robot manufacturers specify repeatability rather than absolute positioning accuracy, because these effects depend on the robot’s pose. To overcome this issue, a practical compensation technique that minimizes the pose dependent error is presented. This is achieved by using a laser tracker to identify an improved model of the robot. The calibration procedure can then be automated without the need for any information other than that provided in the robot’s manual. Finally, the compensation algorithm is numerically inexpensive, enabling it to be implemented in robot control when there is a possibility of manipulating the path planning angles. A short step by step explanation is given on how to implement our methods for other robots. With that, it is possible to expand the spectrum of robotic applications without external sensors, where no large process forces act on the axes.