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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 11348

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Prof. Dr. Yan Shi

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Guest Editor

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: mechatronic engineering; hydraulic transmission and control; precision detection technique
Special Issues, Collections and Topics in MDPI journals

Dr. Shuai Ren

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Guest Editor

School of Automation, Beijing Institute of Technology, Beijing, China
Interests: robotic

Special Issue Information

Dear Colleagues,

This Special Issue focuses on Advanced Intelligent Systems and Techniques for Healthcare Applications.

Nowadays, with the development of computational intelligence and hardware design technologies, advanced robotics, mechatronics, and automation in healthcare have attracted growing research interests in an enormous scope of practical applications, such as in hospitals, homes, rehabilitation robots, and health management and promotion. However, due to the lack of adequate intelligence in traditional healthcare systems, their functional development has been limited. The cross-type research direction, which requires complex design and setup, is a research field that deeply integrates medicine and engineering technology and has extensive developments in medical and rehabilitation devices.

The existing challenges of healthcare mechatronic devices include the shortage of structure complexity, low-end products, the lowness of competitiveness, and non-intelligence. Most previous assistive devices/robots were developed to provide patients with rehabilitation training in hospitals. Furthermore, there have also been limited advanced intelligent systems and techniques available for healthcare applications. With the ageing population rapidly increasing, these assistive devices must have smaller and cheaper production costs. In addition, since every individual's rehabilitation needs are quite different, the advanced healthcare systems must combine multi-modal human rehabilitation data with stochastic analysis, pattern recognition, and machine learning methods to design customized complex healthcare programs for each user. Hence, advanced intelligent systems and techniques for healthcare applications are imminently essential.

Prof. Dr. Yan Shi
Dr. Shuai Ren
Guest Editors

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Keywords

surgery robotics
rehabilitation medical
advanced intelligent
healthcare application
digital diagnosis and treatment

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Published Papers (9 papers)

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Research

16 pages, 1878 KB

Open AccessArticle

Empirical Evaluation of CHOMP for Autonomous Pick-and-Place Manipulation Using a UR5e Robot Arm: A MATLAB–ROS2 Hybrid Framework

by Kingsley Chigozie Eneh and Aytac Ugur Yerden

Appl. Sci. 2026, 16(17), 8370; https://doi.org/10.3390/app16178370 - 22 Aug 2026

Abstract

This study investigated the application of Covariant Hamiltonian Optimization for Motion Planning (CHOMP) in the MATLAB programming environment to an industrially relevant Universal Robots UR5e six-DOF manipulator. The parameters were set to be equal to those of the standard MoveIt2 CHOMP plugin, and the obstacle cost was computed directly in MATLAB using the Robotics System Toolbox’s forward kinematics function to obtain the end-effector position at each trajectory waypoint, which was then evaluated against a piecewise potential field defined over three spherical obstacles in the workspace. We executed five distinct picking task examples and one task over thirty trials, together with a sensitivity analysis over the weight parameter defining the optimization smoothness (i.e., weight/gamma). The mixed empirical results exposed major drawbacks of vanilla CHOMP under our parameter configuration. We achieved a collision-free result for only two of the five tasks, T-03 and T-05, with T-03 converging quickly in five iterations (0.16 s) and T-05 requiring 156 iterations and hitting the planning timeout limit of 10 s. Three tasks did not yield any collision-free result under the 10 s time limit. One of those three tasks, T-01, when running 30 random trial simulations after adding a tiny amount of noise to the start/end poses, yielded 0%, so all trials timed out on its planning 200-iteration limit with an invalid collision result. We analyzed the movement profile (position, velocity, and acceleration over time) of the trajectories generated during the experiments. Several examples exceed the UR5e velocity limit (180 deg/s) and the UR5e acceleration limit (400 deg/s²) by an order of magnitude, and peak values on T-02 reached up to 4731.92 deg/s². With these chosen parameters, basic CHOMP is not industrially suitable for the UR5e robot or for the implementation of the empirical evaluation of CHOMP discussed in this paper. We also identified the modes of failure of basic CHOMP under these parameters and discuss relevant changes. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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42 pages, 29009 KB

Open AccessArticle

A Low-Cost Electronically Controlled Pneumatic Knee with Passive Four-Bar Stance Stability and Semi-Active Swing Damping: A Single-Case Feasibility Study

by Seung-Gi Kim, Jin-Kook Park, Bum-Ki Hong, Na-Yoen Park, Chil-Yong Kwon, Se-Hoon Park and Su-Hong Eom

Appl. Sci. 2026, 16(15), 7850; https://doi.org/10.3390/app16157850 - 6 Aug 2026

Viewed by 296

Abstract

Microprocessor-controlled knee prostheses (MPKs) face limited accessibility in resource-constrained environments due to high implementation costs and excessive power consumption associated with complex actuators. This study examines the technical feasibility of a low-cost electronically controlled pneumatic knee (ECPK) that combines structural mechanics with minimal electronic control. A functional decoupling strategy was implemented: stance-phase stability is provided by passive kinematic locking of a four-bar linkage over the near-extended stance range, while a lightweight feedforward controller driven by a single joint-axis Hall sensor segments the gait cycle continuously, updates its speed estimate once per step, and adjusts the valve only for swing-phase damping. From the stance duration of the preceding steps, this controller presets the pneumatic valve orifice to compensate for mechanical response delays, so that link rotation speed is regulated semi-actively without powered actuation. System integration and control viability were evaluated in a single-case feasibility study (N = 1), in which the ECPK was compared within subject with a commercial mechanical prosthesis after a 4-week adaptation period. Despite a 400 g distal mass penalty, the semi-active control algorithm was associated with a smaller increase in step-length asymmetry at the highest speed tested. Furthermore, net oxygen cost was lower with the ECPK during high-speed walking. Because the conditions were compared at unmatched self-selected speeds and the ECPK condition reached a respiratory exchange ratio (RER) of 1.13, this observation is hypothesis-generating. Coupling passive four-bar stance stability with minimal electronic swing regulation is therefore a viable engineering basis for accessible prostheses, and the present study establishes its technical feasibility rather than its clinical effectiveness. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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22 pages, 7359 KB

Open AccessArticle

Design and Experimental Validation of a Passive Following System for a Mecanum-Wheel Mobile Platform Based on Gimbal Posture Perception and Orthogonal Odometry Fusion

by Xinyang Yu, Zhenhua Wang, Haoyan Duan and Xiaoyun Yang

Appl. Sci. 2026, 16(13), 6827; https://doi.org/10.3390/app16136827 - 7 Jul 2026

Viewed by 430

Abstract

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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26 pages, 2881 KB

Open AccessArticle

Adaptive RBF Neural Network-Based Self-Tuning PID Control for BLDC Motor-Driven Robotic Joints

by Caixia Xue, Hui Bi and Lun Zhu

Appl. Sci. 2026, 16(9), 4469; https://doi.org/10.3390/app16094469 - 2 May 2026

Viewed by 625

Abstract

Accurate and robust control of robotic joints is essential for high-performance robotic systems. However, conventional proportional–integral–derivative (PID) controllers suffer from limited adaptability when applied to brushless direct current (BLDC) motor-driven joints operating under nonlinear and time-varying conditions. To address this issue, this paper proposes a Radial Basis Function (RBF) neural network-enhanced self-tuning PID control strategy. The RBF neural network serves as an online identifier to approximate the nonlinear dynamics of the BLDC motor and to estimate the system Jacobian online. Based on the estimated Jacobian, the PID gains (K_p, K_i, and K_d) are adaptively updated using a gradient descent mechanism, enabling continuous adjustment to varying operating conditions. Simulation and experimental results demonstrate that the proposed method achieves negligible overshoot, faster settling performance, and improved steady-state accuracy compared with conventional PID and PI controllers. In addition, the proposed controller exhibits enhanced disturbance rejection capability and robust performance under abrupt speed variations and start–stop conditions. The proposed approach effectively combines the simplicity of PID control with the adaptability of neural networks, providing a practical and efficient solution for high-precision robotic joint control. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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26 pages, 7673 KB

Open AccessArticle

Deep Deterministic Policy Gradient-Based Parameter Adaptation for Synchronous Sliding-Mode Control with Time-Delay Estimation in Dual-Arm Robot Manipulators Under System Uncertainties

by Duc Thien Tran, Thanh Nha Nguyen, Thi Kim Tram Huynh and Kyoung Kwan Ahn

Appl. Sci. 2026, 16(4), 2042; https://doi.org/10.3390/app16042042 - 19 Feb 2026

Viewed by 873

Abstract

This paper presents a synchronous sliding-mode control with time-delay estimation (SSMC-TDE)-based adaptive control framework for coordinated motion control of dual-arm robotic manipulators operating under system uncertainties. The baseline SSMC-TDE scheme is constructed using synchronization and cross-coupling errors to ensure precise coordinated motion among robot joints, while sliding-mode control effectively handles strong nonlinearities, and the time-delay estimation technique approximates lumped uncertainties arising from external disturbances, modeling errors, and payload variations. The stability of the closed-loop system is rigorously analyzed and guaranteed using the Lyapunov theory. To overcome performance degradation caused by manually tuned control gains, a deep reinforcement learning-assisted parameter adaptation mechanism is integrated into the SSMC-TDE structure. Specifically, a Deep Deterministic Policy Gradient (DDPG) algorithm is employed to adapt selected control gains online through a reward function designed to simultaneously enhance motion synchronization and reduce trajectory-tracking errors, while preserving the stability properties of the underlying controller. Simulation studies are conducted within a co-simulation framework integrating MATLAB/Simulink and ROS/Gazebo for a dual-arm robotic platform. Quantitative evaluations based on the root mean square error (RMSE) of trajectory-tracking and synchronization errors across all six joints demonstrate that, averaged over both scenarios, the proposed DDPG-assisted SSMC-TDE achieves an overall RMSE reduction of 35.52% and 99.3% compared with conventional SSMC and SSMC-TDE controllers, respectively, confirming its superior performance and robustness under system uncertainties. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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22 pages, 3768 KB

Open AccessArticle

A Collaborative Navigation Model Based on Multi-Sensor Fusion of Beidou and Binocular Vision for Complex Environments

by Yongxiang Yang and Zhilong Yu

Appl. Sci. 2025, 15(14), 7912; https://doi.org/10.3390/app15147912 - 16 Jul 2025

Cited by 1 | Viewed by 1585

Abstract

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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20 pages, 3411 KB

Open AccessArticle

Energy-Efficient Hybrid PID Control with Exponential Trajectories for Smooth Setpoint Transitions: Applications in Robotics and Aeronautics

by Jesús Alberto Meda-Campaña, Israel Isaías Lizardo-Parra, Juan Carlos García-Hernández, Jonathan Omega Escobedo-Alva, Luis Alberto Páramo-Carranza and Ricardo Tapia-Herrera

Appl. Sci. 2025, 15(13), 7223; https://doi.org/10.3390/app15137223 - 26 Jun 2025

Cited by 2 | Viewed by 1853

Abstract

In this paper, a modification of the classical PID controller scheme for position control is presented. The resulting controller incorporates an exponential trajectory that smoothly guides the system towards the setpoint and a hybrid mechanism to dynamically reset the exponential signal, allowing an adaptive response to discontinuous reference signals. This combination leverages the benefits of exponential trajectories to reduce overshoot and transient oscillations, while the hybrid system ensures robust performance over a wide range of operating scenarios. Among the advantages of the proposed approach, two stand out: (1) significant improvements in energy savings can be achieved in some cases, and (2) closed-loop system performance can be improved even considering poorly tuned PIDs. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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16 pages, 5421 KB

Open AccessArticle

Design and Development of Hugging Mechanism for Capturing Chest and Back Auscultatory Sounds

by Ryosuke Tsumura, Takuma Ogawa, Reina Umeno, Ryuta Baba, Yuko Morishima, Kiyoshi Yoshinaka and Hiroyasu Iwata

Appl. Sci. 2025, 15(3), 1669; https://doi.org/10.3390/app15031669 - 6 Feb 2025

Cited by 1 | Viewed by 1926

Abstract

Robotic auscultation has the potential to solve problems associated with gender issues by allowing examinations that eliminate the need for physical contact between doctor and patient. Aiming toward a robotic auscultation device capable of safely acquiring chest and back auscultatory sounds simultaneously, this study aimed to develop a unique actuator-less hugging mechanism with a multi-acoustic sensor array that can be transformed to wrap around the chest and back to fit the patient’s body shape. The mechanism consists of a twin-articulated arm with multi-layer gear coupling and a cam mechanism for power transmissions. The hugging motion is generated by pushing the cam mechanism by the patient. The force applied to the cam mechanism acts as the driving force for the twin-articulated arm. The trajectory of the arm changes depending on the distance that the cam mechanism is pressed, and it was designed to fit typical body types (obese, standard, and slender). Our results demonstrated that the proposed mechanism was able to be transformed for each body type, and its positional error was less than 15 mm in all body types. This means that the proposed mechanism is capable of safely acquiring chest and back auscultatory sounds whilst simultaneously fitting to various body shapes. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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17 pages, 16205 KB

Open AccessArticle

On-Site Implementation of External Wrench Measurement via Non-Linear Optimization in Six-Axis Force–Torque Sensor Calibration and Crosstalk Compensation

by Jiyou Shin, Jinjae Shin, Hong-ryul Jung, Jaeseok Won, Eugene Auh and Hyungpil Moon

Appl. Sci. 2025, 15(3), 1510; https://doi.org/10.3390/app15031510 - 2 Feb 2025

Cited by 5 | Viewed by 2565

Abstract

This study introduces a novel calibration method for accurate external wrench measurement using a six-axis FT (force–torque) sensor. We propose a sensor model and calibration method for FT sensors that enable precise separation of the force and torque components without the need for additional devices or sensors by estimating essential parameters: bias, crosstalk, CoM (center of mass), and inclination. By directly utilizing manufacturer-provided data, our approach eliminates the complexities of traditional calibration processes while achieving higher accuracy in force–torque measurements. This method simplifies the calibration workflow and enhances the practicality of FT sensor applications. A mobile manipulator installed with an FT sensor and a gripper is used to demonstrate calibration effectiveness across varying postures and incline conditions, with non-linear optimization based on the gradient descent method applied to minimize sensor-data errors. The tilt of the base is implemented by placing a step under the wheels of the mobile base to simulate roll or pitch scenarios. A digital level was used to measure the angle and verify that our predicted results were accurate. The proposed method addresses typical calibration challenges, including the effects of the end tool and base incline, which are not commonly covered in existing methods. The results show that, on a non-inclined base, crosstalk and CoM calibration reduces the MSE (mean squared error) by 55.8%, 56.2%, and 14.5% for the external force with respect to data without any calibration conducted. On an inclined base, our full calibration process reduces the MSE by a maximum of 98.6% for external mass measurement with respect to no calibration method applied. These findings highlight the importance of incline calibration for achieving accurate external force estimations, especially in mobile manipulator applications where the environment frequently changes. Full article

(This article belongs to the Special Issue Advanced Robotics, Mechatronics, and Automation)

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