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23 pages, 2953 KB

Open AccessArticle

Probabilistic Sampling Networks for Hybrid Structure Planning in Semi-Structured Environments

by Xiancheng Ji, Jianjun Yi and Lin Su

Sensors 2025, 25(20), 6476; https://doi.org/10.3390/s25206476 - 20 Oct 2025

The advancement of adaptable industrial robots in intelligent manufacturing is hindered by the inefficiency of traditional motion planning methods in high-dimensional spaces. Therefore, a Dempster–Shafer evidence theory-based hybrid motion planner is proposed, in which a probabilistic sampling network (PSNet) and an enhanced artificial [...] Read more.

The advancement of adaptable industrial robots in intelligent manufacturing is hindered by the inefficiency of traditional motion planning methods in high-dimensional spaces. Therefore, a Dempster–Shafer evidence theory-based hybrid motion planner is proposed, in which a probabilistic sampling network (PSNet) and an enhanced artificial potential field (EAPF) cooperate with each other to improve the planning performance. The PSNet architecture comprises two modules: a motion planning module (MPM) and a fusion sampling module (FSM). The MPM utilizes sensor data alongside the robot’s current and target configurations to recursively generate diverse multimodal distributions of the next configuration. Based on the distribution information, the FSM was used as a decision-maker to ultimately generate globally connectable paths. Moreover, the FSM is equipped to correct collision path points caused by network inaccuracies through Gaussian resampling. Simultaneously, an augmented artificial potential field with a dynamic rotational field is deployed to repair local paths when worst-case collision scenarios occur. This collaborative strategy harmoniously unites the complementary strengths of both components, thereby enhancing the overall resilience and adaptability of the motion planning system. Experiments were conducted in various environments. The results demonstrate that the proposed method can quickly find directly connectable paths in diverse environments while reliably avoiding sudden obstacles. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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22 pages, 5165 KB

Open AccessArticle

Spatial Error Prediction and Compensation of Industrial Robots Based on Extended Joints and BO-XGBoost

by Bingran Yang and Xuedong Jing

Sensors 2025, 25(20), 6422; https://doi.org/10.3390/s25206422 - 17 Oct 2025

Viewed by 137

Abstract

Robotic positioning accuracy is paramount in complex tasks. This accuracy is influenced by both geometric and non-geometric factors, making error prediction a significant challenge. To address this, this paper introduces two key contributions. First, we propose a novel input feature, the robot’s “extended [...] Read more.

Robotic positioning accuracy is paramount in complex tasks. This accuracy is influenced by both geometric and non-geometric factors, making error prediction a significant challenge. To address this, this paper introduces two key contributions. First, we propose a novel input feature, the robot’s “extended joint angles,” which incorporates joint reversal information to better capture non-geometric errors like gear backlash. Second, we develop a high-accuracy spatial error prediction model by combining the Extreme Gradient Boosting (XGBoost) algorithm with Bayesian Optimization (BO) for hyperparameter tuning. The BO-XGBoost model establishes a direct non-linear mapping from the extended joint angles to the positioning error. Experimental results demonstrate that after compensation, the mean position error was reduced from 1.0751 mm to 0.1008 mm (a 90.62% decrease), the maximum error from 3.3884 mm to 0.4782 mm (an 85.88% decrease), and the standard deviation from 0.5383 mm to 0.0832 mm (an 84.54% decrease). A comparative analysis against Decision Tree, K-Nearest Neighbors, and Random Forest models further validates the superiority of the proposed method in reducing robot position error. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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17 pages, 4584 KB

Open AccessArticle

Construction and Experimental Analysis of a Multipurpose Robotic Fin Ray Gripper for Manipulator Robots

by Anselmo Rafael Cukla, Rafael Crespo Izquierdo, Lucas Strapazzon, Joaquín Ezequiel Taverna, Claudenir Rocha Alves Filho, Sergio Omar Lapczuk, Jorge Antonio Szydlowski, Solon Bevilacqua and Daniel Fernando Tello Gamarra

Sensors 2025, 25(18), 5782; https://doi.org/10.3390/s25185782 - 17 Sep 2025

Viewed by 514

Abstract

This article presents a methodology for estimating the gripping forces in a Fin Ray-type gripper, based on the integration of experimental and computational approaches. The development and validation methods includes (1) mechanical modeling and material selection; (2) experimental tests to relate FG finger [...] Read more.

This article presents a methodology for estimating the gripping forces in a Fin Ray-type gripper, based on the integration of experimental and computational approaches. The development and validation methods includes (1) mechanical modeling and material selection; (2) experimental tests to relate FG finger displacement to maximum applied force using a load cell; (3) validation of the computational model through finite element method (FEM) simulations in ABAQUS using experimental data; and (4) experimental analysis of the FG handling a chicken egg, with the FEM determining the stress applied to the egg. The computational results showed a maximum stress of approximately 7 MPa on the egg, with no signs of damage, demonstrating the FG’s suitability for handling delicate objects in both the experimental and computational procedures, thus enabling safe object handling without causing damage. This work advances research on Fin Ray-type flexible end-effectors, emphasizing their utility in manipulating fragile objects without requiring complex force and pressure control algorithms. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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17 pages, 18945 KB

Open AccessArticle

Collaborative Robot Control Based on Human Gaze Tracking

by Francesco Di Stefano, Alice Giambertone, Laura Salamina, Matteo Melchiorre and Stefano Mauro

Sensors 2025, 25(10), 3103; https://doi.org/10.3390/s25103103 - 14 May 2025

Viewed by 1078

Abstract

Gaze tracking is gaining relevance in collaborative robotics as a means to enhance human–machine interaction by enabling intuitive and non-verbal communication. This study explores the integration of human gaze into collaborative robotics by demonstrating the possibility of controlling a robotic manipulator with a [...] Read more.

Gaze tracking is gaining relevance in collaborative robotics as a means to enhance human–machine interaction by enabling intuitive and non-verbal communication. This study explores the integration of human gaze into collaborative robotics by demonstrating the possibility of controlling a robotic manipulator with a practical and non-intrusive setup made up of a vision system and gaze-tracking software. After presenting a comparison between the major available systems on the market, OpenFace 2.0 was selected as the primary gaze-tracking software and integrated with a UR5 collaborative robot through a MATLAB-based control framework. Validation was conducted through real-world experiments, analyzing the effects of raw and filtered gaze data on system accuracy and responsiveness. The results indicate that gaze tracking can effectively guide robot motion, though signal processing significantly impacts responsiveness and control precision. This work establishes a foundation for future research on gaze-assisted robotic control, highlighting its potential benefits and challenges in enhancing human–robot collaboration. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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20 pages, 8922 KB

Open AccessArticle

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning

by Juntao Chen, Zhiqing Zhang, Wei Guan, Xinxin Cao and Ke Liang

Sensors 2024, 24(22), 7359; https://doi.org/10.3390/s24227359 - 18 Nov 2024

Cited by 1 | Viewed by 1571

Abstract

Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, [...] Read more.

Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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17 pages, 6860 KB

Open AccessArticle

Online Flow Measurement of Liquid Metal Solutions Based on Impact Force Sequences: Modeling Analysis, Simulation, and Validation of Experimental Results

by Qiguang Li, Xiru Zheng, Yu He, Fangmin Xu, Yulin Zhuang, Bingji Zeng and Bofang Duan

Sensors 2024, 24(14), 4553; https://doi.org/10.3390/s24144553 - 14 Jul 2024

Cited by 1 | Viewed by 1502

Abstract

Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process [...] Read more.

Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process as an observational variable series. Fluid mechanics theory and simulation software are used to analyze and verify the feasibility of the impact force as an observable variable to measure the flow rate, followed by the construction of the CNN-LSTM-CNN-Double (CLCD) flow measurement model of impact force and flow rate based on the parameters of the learning rate and the number of training times, and finally the construction of a test platform for the flow measurement, and the validity of the method is verified through actual operation. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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Review

Jump to: Research, Other

25 pages, 487 KB

Open AccessReview

Deformable and Fragile Object Manipulation: A Review and Prospects

by Yicheng Zhu, David Yang and Yangming Lee

Sensors 2025, 25(17), 5430; https://doi.org/10.3390/s25175430 - 2 Sep 2025

Viewed by 1515

Abstract

Deformable object manipulation (DOM) is a primary bottleneck for the real-world application of autonomous robots, requiring advanced frameworks for sensing, perception, modeling, planning, and control. When fragile objects such as soft tissues or fruits are involved, ensuring safety becomes the paramount concern, fundamentally [...] Read more.

Deformable object manipulation (DOM) is a primary bottleneck for the real-world application of autonomous robots, requiring advanced frameworks for sensing, perception, modeling, planning, and control. When fragile objects such as soft tissues or fruits are involved, ensuring safety becomes the paramount concern, fundamentally altering the manipulation problem from one of pure trajectory optimization to one of constrained optimization and real-time adaptive control. Existing DOM methodologies, however, often fall short of addressing fragility constraints as a core design feature, leading to significant gaps in real-time adaptiveness and generalization. This review systematically examines individual components in DOM with a focus on their effectiveness in handling fragile objects. We identified key limitations in current approaches and, based on this analysis, discussed a promising framework that utilizes both low-latency reflexive mechanisms and global optimization to dynamically adapt to specific object instances. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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Other

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22 pages, 829 KB

Open AccessSystematic Review

What Ranges of Probe Pressure Are Applied During Ultrasound Examinations? A Systematic Review

by Sławomir Suchoń, Michał Burkacki, Miłosz Chrzan and Mateusz Winder

Sensors 2025, 25(11), 3415; https://doi.org/10.3390/s25113415 - 29 May 2025

Cited by 1 | Viewed by 2065

Abstract

The number of US exams has nearly doubled in the last ten years. Many researchers point out the probe pressure force influence on image quality and other aspects of examination. This review aims to identify the range of applied probe pressure during US [...] Read more.

The number of US exams has nearly doubled in the last ten years. Many researchers point out the probe pressure force influence on image quality and other aspects of examination. This review aims to identify the range of applied probe pressure during US examinations and gather information on probe compression force values during various US examinations (examination types, body regions, etc.). Methods: A systematic review following PRISMA guidelines was conducted using IEEE Xplore, Web of Science, Scopus, and PubMed/MEDLINE. Studies with quantitative data on probe pressure during US by human operators or RUSs (robotic ultrasound systems) were included. Results: From the 26 included studies, force ranges varied up to 34.5 N for abdominal exams. Robotic systems applied slightly higher maximum forces (34.5 N) than human operators (30 N). Most studies reported positive impacts of force monitoring on image quality and diagnostic precision, with no adverse effects on patient comfort. Conclusions: The evidence collectively emphasizes the critical role of applied pressure in US. The nonuniformity of the reviewed studies does not allow for identifying a clearly defined range of probe pressure forces or force monitoring protocols. Integrating RUS and standardized pressure protocols could improve diagnostic consistency and accuracy. Full article

(This article belongs to the Special Issue Advanced Robotic Manipulators and Control Applications)

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