Publications

Aerial Manipulator Systems Gain a New Skill: Achieve Contact-based Landing on a Mobile Platform

This paper studies a novel application of an aerial manipulator (AM)-the contact-based landing on a mobile platform. An AM is inherently unstable, under-actuated, and usually loses some DOFs while contacting environments. Meanwhile, the AM's flight state is susceptible to uncertain movements of the mobile platform, such as acceleration, sudden stopping, and reversing. To accomplish the contact-based landing mission, a robust controller is first designed to maintain a steady contact-based flight. Then a hierarchical control framework is applied, integrating the controllers in free-flight and restricted-flight stages. An AM and a mobile platform are developed for contact-based flight experiments. The proposed scheme is reliable and has good repeatability in experiments. To the best of our knowledge, this is the first time an AM has been implemented to conduct a contact-based landing, which is also an innovative landing approach for rotorcraft UAVs.

Stability analysis of opposite singularity in multilayer perceptrons

Effect of Elastic Layer Geometry on the Range and Sensitivity of Flexible Magnetic Tactile Sensor

Tactile sensing is a foundational technology for advancing artificial intelligence and flexible electronics. Magnetically based tactile sensors show considerable promise; however, prior research has predominantly focused on magnetic films, overlooking the optimization of the non-magnetic elastic layer. This study introduces a complementary design strategy that enhances performance through geometric optimization of the non-magnetic elastic layer. A flexible magnetic tactile sensor (FMTS) with a sandwich architecture is presented, and a systematic quantitative analysis is conducted to show how elastic-layer geometry affects sensitivity and measurement range. A clear geometry-performance relationship is established by comparing planar, hemispherical, and directionally anisotropic pyramidal microstructures and integrating magnetic field modeling with mechanical analysis. Relative to the planar baseline, hemispherical and pyramidal microstructures improve normal-force sensitivity by factors of 2.47 and 5.34, and shear-force sensitivity by factors of 2.12 and 4.28, respectively, while the normal-force measurement range can be tuned from 1700 gf (planar) down to 380 gf (pyramidal). The FMTS achieves triaxial decoupling with quantified crosstalk, exhibits long-term stability (as validated by stress-relaxation and creep tests) and environmental robustness (under thermal and humidity tests), and demonstrates practical utility in grip-force monitoring. In summary, this work establishes a new design dimension for high-performance flexible magnetic sensors by tailoring elastic microstructures—an approach conceptually distinct from, yet functionally complementary to, prevailing magnetization strategies.

Improved haptic rendering through tuning the mechanical impedance of human arm

Haptic rendering provides users with the senses of touch during their interacting with the simulated objects in virtual environment. The maximum achievable object stiffness is critical for realistic haptic rendering of rigid objects, and is constrained by several inevitable "non-idealities" in the haptic system as well as the behavior of the human operator, i.e. the mechanical impedance of human arm. By superimposing a position-based force field over traditional haptic feedback, a new impedance tuning (IT) simulation strategy is proposed, which aims to improve haptic system's stability and performance. Based on Delta Haptic Device, comparison experiment is carried out with six individual subjects, and proves the validity of our proposed method.

Chattering suppression and hydrodynamic disturbance estimation of underwater manipulators using adaptive fuzzy sliding mode control

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison’s equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy.

Reward Design for Training Robotic Insertion Tasks Using Low-Dimensional Observation

Interference-Aware Wireless Networks for Home Monitoring and Performance Evaluation

In this paper, a home Internet-of-Things system is analyzed by dividing it into four layers, i.e., the node layer, gateway layer, service layer, and open layer. The gateway layer, which supports a variety of wireless technologies and is the core of home wireless networks access unit, together with the node layer constitutes the home wireless network. A gateway prototype following the proposed architecture has been implemented. A testbed of an interference-aware wireless network which includes the gateway prototype has also been created for testing its user interaction performances. The experimental results show that both Wi-Fi and Bluetooth have an impact on the ZigBee communication. Considering the complex scene of home and building, ZigBee multihop communications are set to reduce the packet loss probability. In addition, an event-level-based transmission control strategy is proposed, in which the packet loss probability of wireless network is reduced by controlling the transmission priority of different levels of monitoring events, and optimizing the ZigBee wireless network channel occupancy.

Review of robotic systems for thoracoabdominal puncture interventional surgery

Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.

EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network

In recent years, there has been a growing research interest in using deep learning to resolve the issue of electroencephalogram (EEG)-based emotion recognition. Current research emphasizes exploiting the useful information from each single EEG channel or each individual set of multichannel EEG, but overlooks the correlation information among different multichannel EEG sets. To explore such discriminative correlation information, we propose a novel and effective method, "trainable adjacency relation driven graph convolutional network (TARDGCN)," which contains two complementary modules: 1) trainable adjacency relation (TAR) and 2) graph convolutional network (GCN). TAR optimizes the local pairwise positions of multichannel EEG sets, which helps form an improved graphic representation for GCN to learn the global correlation among these sets for classification. The proposed method is capable of dealing with the problem of small sample size but large data variation in this issue. Our experimental results conducted on the databases DREAMER and DEAP in the subject-dependent and subject-independent modes show that TARDGCN outperforms the state-of-the-art approaches in classifying all of valence, arousal, and dominance.

Task-Specific Embodied Tactile Sensing for Dexterous Hand

Adaptive Human Movement Compensation Control of Supernumerary Robotic Limb for Overhead Support Task with Non-Zero-Sum Differential Game Theory

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR).

A SLAM-based 6DoF controller with smooth auto-calibration for virtual reality

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Currently there still remains a critical need of human involvements for multi-robot system (MRS) to successfully perform their missions in real-world applications, and the hand-controller has been commonly used for the operator to input MRS control commands. However, in more challenging scenarios involving concurrent MRS control and system monitoring tasks, where the operator's both hands are busy, the hand-controller alone is inadequate for effective human-MRS interaction. To this end, our study takes a first step toward a multimodal interface by extending the hand-controller with a hands-free input based on gaze and brain-computer interface (BCI), i.e., a hybrid gaze-BCI. Specifically, the velocity control function is still designated to the hand-controller that excels at inputting continuous velocity commands for MRS, while the formation control function is realized with a more intuitive hybrid gaze-BCI, rather than with the hand-controller via a less natural mapping. In a dual-task experimental paradigm that simulated the hands-occupied manipulation condition in real-world applications, operators achieved improved performance for controlling simulated MRS (average formation inputting accuracy increases 3%, average finishing time decreases 5 s), reduced cognitive load (average reaction time for secondary task decreases 0.32 s) and perceived workload (average rating score decreases 15.84) with the hand-controller extended by the hybrid gaze-BCI, over those with the hand-controller alone. These findings reveal the potential of the hands-free hybrid gaze-BCI to extend the traditional manual MRS input devices for creating a more operator-friendly interface, in challenging hands-occupied dual-tasking scenarios.

Engagement Enhancement Based on Bayesian Optimization for Adaptive Assist-as-Needed Controller

Assist-as-needed (AAN) control strategy is applied in robot-aided therapy to promote the subject's engagement with minimal assistance. Current methods are based on physical measurements or employ a model to estimate the subject's engagement, which indirectly reflects the physiology of the subject or needs a complicated modeling process. Besides, manual adjustments are required to tune the parameters of the AAN controller to adapt to the estimated engagement of subjects. Such two drawbacks of previous studies have limited their effectiveness for promoting the subject's engagement during the training. In this paper, we propose an engagement enhancement method based on Bayesian Optimization for the adaptive AAN controller. Firstly, we evaluate the subject's engagement by using sEMG-based muscle activation, which directly relates to the physiology of the subject and eliminates the need for complicated modeling of engagement. Next, Bayesian optimization, which is tolerant of noisy measurements and human adaptation, is used to search for the optimal parameter of the AAN controller for each training trial. Finally, we conduct a study with sixteen healthy human subjects to verify the effectiveness of the proposed method. The experimental results showed that subjects' engagement in the experiment group improved more significantly and was maintained at a higher level than that in the control group during the training. The performance improvement after the training obtained in the experiment group was higher than that in the control group (45.42% v.s. 30.40%). The experimental results indicate that the proposed method may be promising for transferring to the rehabilitation of post-stroke patients.

An ultra-sensitive core-sheath fiber strain sensor based on double strain layered structure with cracks and modified MWCNTs/silicone rubber for wearable medical electronics

A Control Strategy with Tactile Perception Feedback for EMG Prosthetic Hand

To improve the control effectiveness and make the prosthetic hand not only controllable but also perceivable, an EMG prosthetic hand control strategy was proposed in this paper. The control strategy consists of EMG self-learning motion recognition, backstepping controller with stiffness fuzzy observation, and force tactile representation. EMG self-learning motion recognition is used to reduce the influence on EMG signals caused by the uncertainty of the contacting position of the EMG sensors. Backstepping controller with stiffness fuzzy observation is used to realize the position control and grasp force control. Velocity proportional control in free space and grasp force tracking control in restricted space can be realized by the same controller. The force tactile representation helps the user perceive the states of the prosthetic hand. Several experiments were implemented to verify the effect of the proposed control strategy. The results indicate that the proposed strategy has effectiveness. During the experiments, the comments of the participants show that the proposed strategy is a better choice for amputees because of the improved controllability and perceptibility.

Design and Characterization of a Novel Compact Hand Exoskeleton Robot for Telerehabilitation and Muscle Spasticity Assessment

Rehabilitation robots can aid patients in performing hand exercises in their own home. However, existing rehabilitation equipment is bulky and difficult to wear and carry, and therapists are unable to remotely assess a patient's finger muscle spasticity. This article describes a lightweight exoskeleton robot that facilitates hand rehabilitation exercises and enables muscle spasticity assessment at home. A hand exoskeleton with one degree of freedom assists patients in flexion and extension movements of their fingers. Its motor has a reduction ratio of 19:1, allowing passive back-driving. The exoskeleton's link lengths are determined by an optimization algorithm. The proposed device has a total weight of 0.356 kg and the torque of the dynamic structure to the metacarpophalangeal joint is 1.832 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula> m, reflecting its lightweight and portable nature. To aid remote assessments of patients' muscle spasticity, the exoskeleton is controlled using a finger tension feedback algorithm. This enables the patient's rehabilitation process to be managed remotely. Experiments involving ten patients and three therapists are conducted to evaluate the robot's feasibility. The results demonstrate that the robot can flex and extend the fingers with a mean angle error of 1.16 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and a mean contact force error of 0.25 N. Moreover, the robot achieves 75% accuracy in assisting therapists with remote assessment of the patient's finger muscle spasticity.