[Paper Review] Design of Calibration Experiments for Identification of Manipulator Elastostatic Parameters

Alexandr Klimchik, Anatol Pashkevich+3 more|arXiv (Cornell University)|Nov 25, 2012

Advanced Measurement and Metrology Techniques25 references19 citations

TL;DR

This paper proposes a non-linear experiment design framework for optimizing calibration experiments to identify elastostatic parameters of industrial robot manipulators. By introducing a user-defined test-pose concept and solving an optimization problem, it generates optimal configurations and applied forces/torques that enhance parameter identification accuracy while respecting kinematic constraints and avoiding collisions, demonstrated on a serial robot for composite machining.

ABSTRACT

The paper is devoted to the elastostatic calibration of industrial robots, which is used for precise machining of large-dimensional parts made of composite materials. In this technological process, the interaction between the robot and the workpiece causes essential elastic deflections of the manipulator components that should be compensated by the robot controller using relevant elastostatic model of this mechanism. To estimate parameters of this model, an advanced calibration technique is applied that is based on the non-linear experiment design theory, which is adopted for this particular application. In contrast to previous works, it is proposed a concept of the user-defined test-pose, which is used to evaluate the calibration experiments quality. In the frame of this concept, the related optimization problem is defined and numerical routines are developed, which allow generating optimal set of manipulator configurations and corresponding forces/torques for a given number of the calibration experiments. Some specific kinematic constraints are also taken into account, which insure feasibility of calibration experiments for the obtained configurations and allow avoiding collision between the robotic manipulator and the measurement equipment. The efficiency of the developed technique is illustrated by an application example that deals with elastostatic calibration of the serial manipulator used for robot-based machining.

Motivation & Objective

• Address the challenge of precise elastostatic calibration in industrial robots used for large-part machining of composite materials.
• Overcome limitations of prior methods by introducing a user-defined test-pose to evaluate and optimize calibration experiment quality.
• Develop an optimization framework that generates optimal manipulator configurations and applied loads for a given number of experiments.
• Ensure feasibility and safety of calibration experiments by incorporating kinematic constraints to prevent collisions with workpieces or measurement equipment.
• Improve the accuracy of elastostatic model identification to enable effective deflection compensation in robot controllers.

Proposed method

• Adapt non-linear experiment design theory to the specific context of robot elastostatic calibration.
• Define a user-defined test-pose as a performance metric to evaluate the quality of calibration experiments.
• Formulate an optimization problem that maximizes the information gain from each calibration experiment.
• Use numerical routines to compute optimal configurations and corresponding applied forces/torques for the manipulator.
• Incorporate kinematic constraints into the optimization to ensure physical realizability and collision avoidance.
• Integrate the method into a workflow for robot-based machining applications involving compliant workpieces.

Experimental results

Research questions

• RQ1 How can calibration experiment quality be systematically evaluated and optimized for elastostatic parameter identification?
• RQ2 What is the optimal set of manipulator configurations and applied loads that maximizes parameter identification accuracy?
• RQ3 How can kinematic constraints be integrated into the experiment design to ensure feasibility and safety?
• RQ4 In what way does the user-defined test-pose concept improve upon traditional calibration experiment selection methods?
• RQ5 How does the proposed method enhance the accuracy of elastostatic models used in robot control for precision machining?

Key findings

• The proposed method generates optimal calibration experiments that significantly improve the accuracy of elastostatic parameter identification.
• The user-defined test-pose concept enables a systematic and quantitative evaluation of experiment quality, leading to better experimental design.
• The optimization framework successfully identifies configurations and applied loads that maximize information gain while respecting kinematic constraints.
• Collision avoidance is effectively ensured through the inclusion of kinematic constraints in the optimization process.
• The method was validated on a real-world application involving a serial manipulator for robot-based machining of composite parts.
• The results demonstrate enhanced model accuracy, enabling effective deflection compensation in robot controllers for precision manufacturing.