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Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 7954

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Dr. Yukang Cui

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College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: multi-robot collaborative positioning and mapping; intelligent connected vehicle platooning and game; multi-agent federated learning and optimization; nonlinear time-delay system analysis

Special Issue Information

Dear Colleagues,

The rapid development of intelligent robotic systems has catalyzed advancements in multi-robot cooperation, enabling more complex tasks in dynamic and uncertain environments. This Special Issue focuses on the integration of perception, calibration, and adaptive control within multi-robot systems, aiming to bridge the gap between accurate environment understanding and robust system coordination. Topics of interest include multi-modal sensing and fusion for situational awareness, intrinsic and extrinsic sensor calibration under real-time constraints, and learning-based or model-based adaptive control strategies for cooperative path planning and formation maintenance. We welcome original research and review articles that explore innovative algorithms, system architectures, and experimental validations to push the boundaries of perception-driven autonomy in distributed robotic systems.

Dr. Yukang Cui
Guest Editor

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Keywords

multi-robot systems
cooperative perception
sensor calibration
adaptive control
path planning
sensor fusion
formation control
real-time robotics
learning-based control
intelligent navigation

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Published Papers (5 papers)

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Research

23 pages, 14568 KB

Open AccessArticle

A Real-Time Magnetic Adhesion Force Estimation Method for Wall-Climbing Robots Equipped with Halbach Permanent Magnet Arrays

by Jiabin Cao, Lin Zhang, Yiyang Zhao and Ming Chen

Sensors 2026, 26(9), 2678; https://doi.org/10.3390/s26092678 - 25 Apr 2026

Viewed by 1592

Abstract

This paper presents a real-time magnetic adhesion force estimation framework for wall-climbing robots equipped with Halbach permanent magnet arrays (PMAs) and air-gap–adjustable mechanisms. Accurately computing the magnetic adhesion force between a PMA and a large ferromagnetic surface is challenging due to the nonlinear magnetization behavior of soft magnetic materials and the strongly coupled, highly nonuniform magnetic fields generated by Halbach arrays. Conventional analytical models fail to capture these effects, while finite element methods (FEM) incur prohibitive computational cost for real-time applications. To address this, we propose an analytical magnetic-force estimation model based on the magnetostatic MoI (Method of Images), which replaces the unknown magnetization inside the steel plate with an equivalent image magnet distribution that satisfies boundary conditions at the air–steel interface. The method avoids solving complex magnetization in soft magnetic media and enables a unified force computation for arbitrarily oriented magnet elements. Additionally, complex Halbach PMA geometries are approximated through cuboid-element segmentation into cuboid magnet array, allowing efficient force evaluation. Comparative studies demonstrate that the proposed method achieves accuracy comparable to FEM while reducing computation time by several orders of magnitude. Experimental validation using a linear Halbach array and a large steel plate proved that the framework can reliably estimate magnetic adhesion force across varying air-gap distances, meeting the real-time requirements of air-gap–adjustable wall-climbing robots. Full article

(This article belongs to the Special Issue Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control)

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20 pages, 8164 KB

Open AccessArticle

Targetless LiDAR–Camera Extrinsic Calibration via Class-Agnostic Boundary Mask Alignment and SPSA-Based Optimization

by Han-You Jeong, Woo-Hyuk Son, Dong-Wook Shin, Kyuna Cho, Minwoo Chee and Tae (Tom) Oh

Sensors 2026, 26(5), 1501; https://doi.org/10.3390/s26051501 - 27 Feb 2026

Viewed by 1045

Abstract

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane depth and intensity projections of LiDAR data and camera images. It then obtains robust initial pose candidates through bounded rotation-only global initialization and refines them using a computationally efficient stochastic gradient approximation to estimate the optimal extrinsic parameters. Experiments on the KITTI benchmark demonstrate a superior accuracy–runtime trade-off compared with a segmentation-based global optimization baseline, while real-world driving tests confirm stable cross-modal alignment under vibration and inter-modal timing jitter. Full article

(This article belongs to the Special Issue Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control)

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25 pages, 1946 KB

Open AccessArticle

Prescribed-Time Leader–Follower Synchronization of Higher-Order Nonlinear Multi-Agent Systems via Fuzzy Neural Adaptive Sliding Control

by Safeer Ullah, Muhammad Zeeshan Babar, Sultan Alghamdi, Ahmed S. Alsafran, Habib Kraiem and Abdullah A. Algethami

Sensors 2025, 25(24), 7483; https://doi.org/10.3390/s25247483 - 9 Dec 2025

Viewed by 1185

Abstract

This paper introduces a novel control framework for prescribed-time synchronization of higher-order nonlinear multi-agent systems (MAS) subject to parametric uncertainties and external disturbances. The proposed method integrates a fuzzy neural network (FNN) with a robust non-singular terminal sliding mode controller (NTSMC) to ensure leader–follower consensus within a user-defined time horizon, regardless of the initial conditions. The FNN is employed to approximate unknown nonlinearities online, while an adaptive update law ensures accurate compensation for uncertainty. A terminal sliding manifold is designed to enforce finite-time convergence, and Lyapunov-based analysis rigorously proves prescribed-time stability and boundedness of all closed-loop signals. Simulation studies on a leader–follower MAS with four nonlinear agents under directed communication topology demonstrate the superiority of the proposed approach over conventional sliding mode control, achieving faster convergence, enhanced robustness, and improved adaptability against system uncertainties and external perturbations. Full article

(This article belongs to the Special Issue Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control)

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35 pages, 15822 KB

Open AccessArticle

XGBoost-Based Digital Twin Model for Predicting Trajectory Errors in a Hexapod Coordinated Machining System Using Positioning Accuracy and Vibration Data

by Kanglin Xing, Miao Feng, Ilian A. Bonev, Henri Champliaud, Mohamed Cheriet and Zhaoheng Liu

Sensors 2025, 25(23), 7142; https://doi.org/10.3390/s25237142 - 22 Nov 2025

Cited by 3 | Viewed by 1825

Abstract

Dynamic errors in robotic machining can degrade part quality, particularly in flexible platforms that are susceptible to both geometric and inertial disturbances. This work introduces a data-driven digital twin for pointwise prediction of circular trajectory errors in a hexapod-based machining cell, using a compact sensing configuration that combines ballbar measurements with tri-axial vibration signals. Deviations measured by ballbar, acceleration data, and CMM-measured profiles are synchronized in the angular domain via a unified pipeline for denoising, resampling, and phase alignment. Sliding-window vibration statistics and the ballbar path error are used as inputs to XGBoost, multilayer perceptron, and random forest regressors. Model performance is evaluated under a deployment-relevant leave-one-run-out protocol and a conventional random 70:30 point split. XGBoost achieves micrometer-level accuracy on unseen runs, with RMSE around 5 µm, R² exceeding 0.80, and near-complete coverage within a ±20 µm tolerance band. Compared to baseline models, it also provides improved suppression of extreme residuals. Feature importance and ablation studies show that the ballbar path error captures the dominant geometric component, while compact hybrid feature sets—combining this anchor with selected vibration descriptors—retain most of the predictive accuracy and enable practical offline batch-level compensation. Full article

(This article belongs to the Special Issue Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control)

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20 pages, 11855 KB

Open AccessArticle

High-Precision Extrinsic Calibration for Multi-LiDAR Systems with Narrow FoV via Synergistic Planar and Circular Features

by Xinbao Sun, Zhi Zhang, Shuo Xu and Jinyue Liu

Sensors 2025, 25(20), 6432; https://doi.org/10.3390/s25206432 - 17 Oct 2025

Cited by 2 | Viewed by 1938

Abstract

Precise extrinsic calibration is a fundamental prerequisite for data fusion in multi-LiDAR systems. However, conventional methods are often encumbered by dependencies on initial estimates, auxiliary sensors, or manual feature selection, which renders them complex, time-consuming, and limited in adaptability across diverse environments. To address these limitations, this paper proposes a novel, high-precision extrinsic calibration method for multi-LiDAR systems with a narrow Field of View (FoV), achieved through the synergistic use of circular and planar features. Our approach commences with the automatic segmentation of the calibration target’s point cloud using an improved VoxelNet. Subsequently, a denoising step, combining RANSAC and a Gaussian Mean Intensity Filter (GMIF), is applied to ensure high-quality feature extraction. From the refined point cloud, planar and circular features are robustly extracted via Principal Component Analysis (PCA) and least-squares fitting, respectively. Finally, the extrinsic parameters are optimized by minimizing a nonlinear objective function formulated with joint constraints from both geometric features. Simulation results validate the high precision of our method, with rotational and translational errors contained within 0.08° and 0.8 cm. Furthermore, real-world experiments confirm its effectiveness and superiority, outperforming conventional point-cloud registration techniques. Full article

(This article belongs to the Special Issue Intelligent Robotic Systems: Integration of Perception, Calibration, and Adaptive Control)

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