AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion

2025-10-03

AI Summary

To address the low pose estimation accuracy and poor real-time performance of flexible manipulators during vertical motion, this paper proposes an intelligent multi-IMU fusion estimation framework. The flexible link is discretized into a series of rigid segments; joint angles are reconstructed from low-cost IMU-acceleration and angular velocity measurements. Crucially, particle swarm optimization (PSO) is innovatively integrated to adaptively tune complementary filter parameters, while a radial basis function neural network (RBFNN) models and compensates residual dynamic errors. Experimental results demonstrate significant improvements in both accuracy and real-time capability: root-mean-square errors in y- and z-direction displacements and orientation angle θ are reduced to 0.21 mm, 0.41 mm, and 0.00024 rad, respectively. This work establishes a lightweight, deployable inertial sensing paradigm for high-precision autonomous operation of flexible manipulators.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents a novel framework for estimating the position and orientation of flexible manipulators undergoing vertical motion using multiple inertial measurement units (IMUs), optimized and calibrated with ground truth data. The flexible links are modeled as a series of rigid segments, with joint angles estimated from accelerometer and gyroscope measurements acquired by cost-effective IMUs. A complementary filter is employed to fuse the measurements, with its parameters optimized through particle swarm optimization (PSO) to mitigate noise and delay. To further improve estimation accuracy, residual errors in position and orientation are compensated using radial basis function neural networks (RBFNN). Experimental results validate the effectiveness of the proposed intelligent multi-IMU kinematic estimation method, achieving root mean square errors (RMSE) of 0.00021~m, 0.00041~m, and 0.00024~rad for $y$, $z$, and $θ$, respectively.

Problem

• Estimating position and orientation of flexible manipulators using multiple IMUs
• Modeling flexible links as rigid segments with joint angle estimation
• Compensating residual errors via neural networks to improve accuracy

Innovation

• Multi-IMU sensor fusion for flexible manipulator modeling
• Particle swarm optimization for complementary filter calibration
• Radial basis function networks compensating residual errors