Check out Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches(reddit.com)

Hi everyone, I wanted to share our latest open-access paper published in the journal Robotics: Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches. The Problem Traditional industrial robot calibration heavily focuses on a single goal: maximizing absolute end-effector position accuracy. However, purely optimizing for position errors often results in the algorithm recommending unrealistic, drastic shifts to the robot’s physical kinematic structure (its Denavit–Hartenberg parameters). This creates a stark deviation from the manufacturer's nominal specifications and can degrade performance across different areas of the workspace. Our Approach We framed this challenge as a multi-objective optimization problem to strike a balance between two competing goals: Position Accuracy: Minimizing discrepancies using joint angle readings and a high-precision laser tracker (LT). Kinematic Realism: Minimizing the mean absolute deviation of the calibrated DH parameters from the manufacturer's original design specs. To find the optimal trade-off, we deployed and benchmarked several leading evolutionary and swarm optimization algorithms: NSGA (Nondominated Sorting Genetic Algorithms) MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) MOPSO (Multi-Objective Particle Swarm Optimization) Key Takeaways Utilizing a multi-objective framework prevents overfitting to specific target points and keeps the structural kinematic parameters physically viable. Swarm and evolutionary approaches excel at generating an adaptable Pareto front, giving automation engineers finer control over calibration tradeoffs. The full methodology, mathematical formulations, and comparative results are available to read for free on the MDPI Robotics Publication Page. I would love to hear the community's thoughts on using meta-heuristics for kinematic calibration, or answer any questions you might have about our experimental setup and algorithm performances! submitted by /u/MAK42018 [link] [Kommentare]