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18 pages, 4862 KB

Open AccessArticle

Development of a Robot-Assisted TMS Localization System Using Dual Capacitive Sensors for Coil Tilt Detection

by Czaryn Diane Salazar Ompico, Julius Noel Banayo, Yamato Mashio, Masato Odagaki, Yutaka Kikuchi, Armyn Chang Sy and Hirofumi Kurosaki

Sensors 2026, 26(2), 693; https://doi.org/10.3390/s26020693 - 20 Jan 2026

Viewed by 220

Abstract

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for neurological research and therapy, but its effectiveness depends on accurate and stable coil placement. Manual localization based on anatomical landmarks is time-consuming and operator-dependent, while state-of-the-art robotic and neuronavigation systems achieve high accuracy using [...] Read more.

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for neurological research and therapy, but its effectiveness depends on accurate and stable coil placement. Manual localization based on anatomical landmarks is time-consuming and operator-dependent, while state-of-the-art robotic and neuronavigation systems achieve high accuracy using optical tracking with head-mounted markers and infrared cameras, at the cost of increased system complexity and setup burden. This study presents a cost-effective, markerless robotic-assisted TMS system that combines a 3D depth camera and textile capacitive sensors to assist coil localization and contact control. Facial landmarks detected by the depth camera are used to estimate the motor cortex (C3) location without external tracking markers, while a dual textile-sensor suspension provides compliant “soft-landing” behavior, contact confirmation, and coil-tilt estimation. Experimental evaluation with five participants showed reliable C3 targeting with valid motor evoked potentials (MEPs) obtained in most trials after initial calibration, and tilt-verification experiments revealed that peak MEP amplitudes occurred near balanced sensor readings in 12 of 15 trials (80%). The system employs a collaborative robot designed in accordance with international human–robot interaction safety standards, including force-limited actuation and monitored stopping. These results suggest that the proposed approach can improve the accessibility, safety, and consistency of TMS procedures while avoiding the complexity of conventional optical tracking systems. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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23 pages, 7910 KB

Open AccessArticle

Automatic Grasping System and Hybrid Controller Towards Multi-Drone Parcel Delivery

by Bruno J. Guerreiro, Francisco Azevedo, Paulo Oliveira and Rita Cunha

Sensors 2026, 26(2), 653; https://doi.org/10.3390/s26020653 - 18 Jan 2026

Viewed by 239

Abstract

This paper presents the development of an autonomous grasping mechanism for drone-based parcel delivery systems towards developing capabilities for in-flight package transfer. The approach integrates a mechanical gripper fitted with sensors and a pose estimation method for parcels, all coordinated through a hybrid [...] Read more.

This paper presents the development of an autonomous grasping mechanism for drone-based parcel delivery systems towards developing capabilities for in-flight package transfer. The approach integrates a mechanical gripper fitted with sensors and a pose estimation method for parcels, all coordinated through a hybrid Model Predictive Control (MPC) architecture. The gripper’s mechanical structure and prototype are developed using 3D printing technology for both the main framework and gear components. A hybrid dynamical model is formulated that integrates the gripper mechanics with simplified drone dynamics, capturing distinct operational phases including package acquisition, transport, and release. The hybrid MPC framework computes reference trajectories for both the gripper arm configuration and the drone’s spatial path toward designated target positions. Experimental validation is conducted using the operational gripper prototype and pose estimation system, while drone behavior is represented through simulation. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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30 pages, 10126 KB

Open AccessArticle

Pose Stabilization Control for Base of Combined System Using Feedforward Compensation PD Control During Target Satellite Transposition

by Zhonghua Hu, Jinlong Yang, Wenfu Xu, Hengtai Chen, Longkun Xu and Deshan Meng

Sensors 2026, 26(1), 206; https://doi.org/10.3390/s26010206 - 28 Dec 2025

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Abstract

During the transposition of a target satellite, dynamic coupling between the target satellite, the manipulators, and the base frequently leads to disturbances in the base’s attitude. To deal with the issue, this paper proposed a pose stabilization method for the base of the [...] Read more.

During the transposition of a target satellite, dynamic coupling between the target satellite, the manipulators, and the base frequently leads to disturbances in the base’s attitude. To deal with the issue, this paper proposed a pose stabilization method for the base of the post-capture combined system using the feedforward compensation PD control. Firstly, the mission sequence for repositioning a target satellite using a discrete-serpentine heterogeneous dual-arm space robot (DSHDASR) was analyzed. The dynamics model of the combined system, composed of the DSHDASR and a target satellite, was established based on the Newton–Euler recursive formulation. Then, the pose stabilization method integrating dynamic feedforward compensation and PD control was developed to stabilize the base of the combined system. Finally, the mission of target satellite transposition was simulated through the co-simulation model. Compared with the traditional control algorithms, the position accuracy and attitude accuracy for the proposed method showed an overall improvement. The results demonstrated that the proposed method significantly reduced base pose errors under high-load and disturbed conditions. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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25 pages, 17971 KB

Open AccessArticle

Kinematic Modeling and Solutions for Cable-Driven Parallel Robots Considering Adaptive Pulley Kinematics

by Zhonghua Hu, Chaowen Deng, Kai Wang and Jianqing Peng

Sensors 2026, 26(1), 39; https://doi.org/10.3390/s26010039 - 20 Dec 2025

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Abstract

Although the use of adaptive pulleys enhances the motion characteristics of cable-driven parallel robots (CDPRs), it significantly increases the complexity of the kinematics model. Conventional methods often fail to account for the influence of adaptive pulley motion on cable length variation, making it [...] Read more.

Although the use of adaptive pulleys enhances the motion characteristics of cable-driven parallel robots (CDPRs), it significantly increases the complexity of the kinematics model. Conventional methods often fail to account for the influence of adaptive pulley motion on cable length variation, making it difficult to establish a precise kinematics model. To deal with the problem, this study presents a kinematic modeling and solution method for CDPRs, which incorporates adaptive pulley kinematics. First, the structural design of the CDPR driven by eight cables is analyzed. Then, the generalized kinematics model and the improved kinematics model with adaptive pulley considerations are established. Furthermore, a hybrid Levenberg–Marquardt and Genetic algorithm is proposed to achieve the efficient and high-precision solution of kinematics equations by combining the rapid global search and precise local optimization. Finally, the proposed method is validated through straight path simulation and elliptical path simulation. The simulation results indicate that the tracking accuracy of the end-effector is better than the 1 × 10⁻⁷ level for the proposed method. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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25 pages, 10242 KB

Open AccessArticle

Real-Time 6D Pose Estimation and Multi-Target Tracking for Low-Cost Multi-Robot System

by Bo Shan, Donghui Zhao, Ruijin Zhao and Yokoi Hiroshi

Sensors 2025, 25(23), 7130; https://doi.org/10.3390/s25237130 - 21 Nov 2025

Viewed by 998

Abstract

In the research field of multi-robot cooperation, reliable and low-cost motion capture is crucial for system development and validation. To address the high costs of traditional motion capture systems, this study proposes a real-time 6D pose estimation and tracking method for multi-robot systems [...] Read more.

In the research field of multi-robot cooperation, reliable and low-cost motion capture is crucial for system development and validation. To address the high costs of traditional motion capture systems, this study proposes a real-time 6D pose estimation and tracking method for multi-robot systems based on YolPnP-FT. Using only an Intel RealSense D435i depth camera, the system achieves simultaneous robot classification, 6D pose estimation, and multi-target tracking in real-world environments. The YolPnP-FT pipeline introduces a keypoint confidence filtering strategy (PnP-FT) at the output of the YOLOv8 detection head and employs Gaussian-penalized Soft-NMS to enhance robustness under partial occlusion. Based on these detection results, a linearly weighted combination of Mahalanobis distance and cosine distance enables stable ID assignment in visually similar multi-robot scenarios. Experimental results show that, at a camera height below 2.5 m, the system achieves an average position error of less than 0.009 m and an average angular error of less than 4.2°, with a stable tracking frame rate of 19.8 FPS at 1920 × 1080 resolution. Furthermore, the perception outputs are validated in a CoppeliaSim-based simulation environment, confirming their utility for downstream coordination tasks. These results demonstrate that the proposed method provides a low-cost, real-time, and deployable perception solution for multi-robot systems. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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16 pages, 2934 KB

Open AccessArticle

A Universal Tool Interaction Force Estimation Approach for Robotic Tool Manipulation

by Diyun Wen, Jiangtao Xiao, Yu Xie, Tao Luo, Jinhui Zhang and Wei Zhou

Sensors 2025, 25(21), 6619; https://doi.org/10.3390/s25216619 - 28 Oct 2025

Viewed by 1163

Abstract

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge [...] Read more.

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with

x

,

y

, and

z

axes and 0.03 Nm with

τ_{x}

,

τ_{y}

, and

τ_{z}

axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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20 pages, 8723 KB

Open AccessArticle

A Sensor Based Waste Rock Detection Method in Copper Mining Under Low Light Environment

by Jianing Ding, Fuming Qu, Weihua Zhou, Jiajun Xu, Lingyu Zhao and Yaming Ji

Sensors 2025, 25(19), 5961; https://doi.org/10.3390/s25195961 - 25 Sep 2025

Viewed by 913

Abstract

During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine [...] Read more.

During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine waste rock is inherently complex, particularly within the conveyor belt section of the sorting machine, where insufficient and uneven lighting significantly impairs the performance of vision-based detection systems. To address the above challenges, a deep-learning-based copper mine waste rock detection algorithm under low-light environments is proposed. Firstly, an Illumination Adaptive Transformer (IAT) module is added as a preprocessing layer at the beginning of the Backbone to enhance the brightness of the images acquired by the vision sensor. Secondly, a Local Enhancement-Global Modulation (LEGM) module is integrated after the A2C2f and C3k2 modules in the Neck to enhance the detection accuracy. Finally, to further improve the model performance, MPDIoU is introduced to optimize the original loss function CIoU. As a result, the proposed algorithm achieved an mAP@0.5 of 0.957 and an mAP@0.5:0.95 of 0.689, outperforming advanced methods by 1.9% and 8.6%, respectively. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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Review

Jump to: Research

43 pages, 3753 KB

Open AccessReview

Comprehensive Review of Deep Learning Approaches for Single-Image Super-Resolution

by Zirun Liu, Shijie Jiang, Shuhan Feng, Qirui Song and Ji Zhang

Sensors 2025, 25(18), 5768; https://doi.org/10.3390/s25185768 - 16 Sep 2025

Viewed by 2581

Abstract

Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and [...] Read more.

Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and explores it from three aspects: theoretical basis, technological evolution, and domain-specific applications. Firstly, the basic concepts, development trajectory, and practical value of SISR are introduced. Secondly, in-depth research is conducted on key technical components, including benchmark dataset construction, a multi-scale upsampling strategy, objective function optimization, and quality assessment indicators. Thirdly, some classic SISR model reconstruction results are listed and compared. Finally, the limitations of SISR research are pointed out, and some prospective research directions are proposed. This article provides a systematic knowledge framework for researchers and offers important reference value for the future development of SISR. Full article

(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)

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