Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches
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| Veröffentlicht in: | Robotics vol. 14, no. 9 (2025), p. 129-145 |
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MDPI AG
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| Online-Zugang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2218-6581 | ||
| 024 | 7 | |a 10.3390/robotics14090129 |2 doi | |
| 035 | |a 3254634768 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231629 |2 nlm | ||
| 100 | 1 | |a Khanesar, Mojtaba A | |
| 245 | 1 | |a Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values. | |
| 653 | |a Heuristic | ||
| 653 | |a Kinematics | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Accuracy | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Calibration | ||
| 653 | |a Neural networks | ||
| 653 | |a Robots | ||
| 653 | |a Pareto optimization | ||
| 653 | |a Manufacturers | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Manufacturing | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Uncertainty | ||
| 653 | |a Parameters | ||
| 653 | |a Evolutionary algorithms | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Industrial robots | ||
| 700 | 1 | |a Karaca Aslihan | |
| 700 | 1 | |a Minrui, Yan | |
| 700 | 1 | |a Piano Samanta | |
| 700 | 1 | |a Branson, David | |
| 773 | 0 | |t Robotics |g vol. 14, no. 9 (2025), p. 129-145 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254634768/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254634768/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254634768/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |