Field Of Intelligent Robots Research Articles

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

A method of dense point cloud SLAM based on improved YOLOV8 and fused with ORB-SLAM3 to cope with dynamic environments

With the development of society and the advancement of technology, intelligent robots have been widely used in various fields. At the same time, Simultaneous Localization and Mapping (SLAM) technology is a key technology in the research field of intelligent robots. However, in dynamic environments, achieving accurate and robust visual SLAM remains a major challenge. In this paper, we propose a method based on improved YOLOv8 fused with ORB-SLAM3 to address dense point cloud SLAM in dynamic environments. Our proposed method successfully integrates real-time object detection and image segmentation technologies of YOLOv8 into the ORB-SLAM3 framework, achieving high-precision and robust visual SLAM in dynamic environments. In the YOLOv8 framework, we use a balanced convolution method, GSConv, instead of some traditional convolution layers (Conv), which balances accuracy with computational load. Based on the GSConv convolution method, we adopt a new feature fusion module, VoVGSCSP, to replace traditional C2f feature fusion modules, thereby improving the Neck structure of YOLOv8 and achieving a lightweight network model. We compare our proposed method with ORB-SLAM3 and some computer vision algorithms on the TUM dataset. Experimental data confirms that our method outperforms existing visual SLAM algorithms in dynamic environments. In fast-moving dynamic environments, the RMSE of absolute pose estimation of our method is 96.28% lower than that of ORB-SLAM3, and the RMSE of relative pose estimation is 51.57% lower than that of ORB-SLAM3. The experimental results demonstrate that our method significantly improves the accuracy of pose estimation in dynamic environments and greatly enhances the performance compared to ORB-SLAM3.

Highly-sensitive, broad-range, and highly-dynamic MXene pressure sensors with multi-level nano-microstructures for healthcare and soft robots applications

Flexible pressure sensors are urgently needed in the fast-growing field of intelligent robots and human healthcare. However, most current pressure sensors can rarely achieve high sensitivity and dynamic behavior within a broad detection range, which greatly hinders their practical application. Herein, we develop a highly-sensitive, wide-range, and highly-dynamic MXene piezoresistive pressure sensor by constructing a multi-level nano-micro (MLNM) sensing structure. The unique structure was formed by combining the accordion-like layered nano-structures of MXene nanosheets and the abundant micro-protrusions transferred from sandpapers. It can dramatically increase the structure compressibility, the interlayer contact area, and thus resistance variations during compression, resulting in a synergistic enhancement in sensitivity, detection range, and dynamic performance. A fully solution-processable process based on sandpaper casting and oxygen plasma treatment enabled tough interface bonding was developed for low-cost and large-area fabrication. As a result, a high sensitivity of 3.94 kPa−1, a broad range of 0–117.5 kPa, rapid response and relaxation times of 70 ms and 84 ms, and long-term stability of > 7500 cyclic compression were obtained, which exhibited significant enhancement in comprehensive performance compared to previous reports. Successful monitoring of diverse human physiological signals and robot movements underscores the considerable practical utility of our MXene-based sensors.

Performance-Enhanced Flexible Self-Powered Tactile Sensor Arrays Based on Lotus Root-Derived Porous Carbon for Real-Time Human-Machine Interaction of the Robotic Snake.

Flexible tactile sensors play an important role in the development of wearable electronics and human-machine interaction (HMI) systems. However, poor sensing abilities, an indispensable external energy supply, and limited material selection have significantly constrained their advancement. Herein, a self-powered flexible triboelectric sensor (TES) is proposed by integrating lotus-root-derived porous carbon (PC) into polydimethylsiloxane (PDMS). Owing to the superior charge capturing capability of PC, the PDMS/PC (PPC)-based TES exhibits an open-circuit voltage (Voc) of 22.8 V when it is periodically patted by skin at the pressure of 2 N and the frequency of 1 Hz, which is 5 times higher than that of a pristine PDMS-based TES. Furthermore, the as-prepared self-powered TES exhibits a high sensitivity of 3.24 V kPa-1 below 15 kPa for detecting human motion signals, such as finger clicks, joint bends, etc. Last but not the least, after the assembly of a PPC-based TES array and construction of an HMI system, the robotic snake can be controlled remotely by recognizing finger touching signals. This work shows broad potential applications for the self-powered TES in the fields of intelligent robotics, flexible electronics, disaster relief, and intelligence spying.

An Untethered Soft Crawling Robot Driven by Wireless Power Transfer Technology.

Soft robots based on flexible materials have attracted the attention due to high flexibility and great environmental adaptability. Among the common driving modes, electricity, light, and magnetism have the limitations of wiring, poor penetration capability, and sophisticated equipment, respectively. Here, an emerging wireless driving mode is proposed for the soft crawling robot based on wireless power transfer (WPT) technology. The receiving coil at the robot's tail, as an energy transfer station, receives energy from the transmitting coil and supplies the electrothermal responsiveness to drive the robot's crawling. By regulating the WPT's duration to control the friction between the robot and the ground, bidirectional crawling is realized. Furthermore, the receiving coil is also employed as a sensory organ to equip the robot with localization, ID recognition, and sensing capabilities based on electromagnetic coupling. This work provides an innovative and promising strategy for the design and integration of soft crawling robots, exhibiting great potential in the field of intelligent robots.

Preparation of multifunctional self-healing MXene/PVA double network hydrogel wearable strain sensor for monitoring human body and organ movement

In this work, a kind of conductive, self-healing hydrogel was prepared. Then it is assembled into a flexible wearable sensor for human motion detection and human-computer interaction. MXene/PVA-CBA hydrogel has super mechanical properties and excellent self-healing ability (1.8 s). It is assembled into a flexible sensor with high sensitivity, which can accurately detect various movements of the human body (ranging from frowning, speaking, and coughing on the face to bending of fingers and wrists, and body movements). Furthermore, it can be used for handwriting recognition. When it is installed on the artificial limb, it can realize the function of touching the capacitive screen. It solves the problem of using silicone prostheses to control the screen and has broad research potential in the field of intelligent robots. Therefore, the flexible wearable sensor composed of MXene/PVA-CBA hydrogel has great potential in human motion detection, bionic intelligent robot, and intelligent detection.

ICRAIC 2022-Preface

Recent years have seen exciting new research and developments in Robotics, Automation and Intelligent Control. Computer and control technology, intelligent control, robotics and automation are changing many aspects of our productive lives. Thanks to computers and control algorithms brought by intelligent control and artificial intelligence, machines are becoming smarter. ICRAIC 2022 focused on the development and integration of robotics, digitalization, automation and artificial intelligence technologies. Therefore, the 2nd International Conference on Robotics, Automation and Intelligent Control (ICRAIC 2022) took place on December 9th-11th, 2022 in Changsha.ICRAIC 2022 has brought together leading academic scientists, researchers and research scholars to exchange their experience and research results in the field of intelligent robotics and systems and share their discoveries, insight and lessons learned, and take home exciting new ideas to perform further state of the art research to make mind-blowing collaborations and innovations.ICRAIC 2022 accepted 50 full papers which was divided into 3 tracks, including Robotics, Automation and Intelligent Control. And all submissions were peer-reviewed through a single-blind review process by an international panel of expert referees, and decisions were taken based on the criteria.Thanks to the ICRAIC 2022 Committee for their efforts in preparing the conference and ensuring its smooth running.:List of General Chairs, Program Chairs, Technical Chairs, Technical Program Committee, Organizers, Co-Organizer, Editors are available in this pdf

Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces

AbstractWith the rapid development of emerging artificial intelligence technology, brain–computer interfaces are gradually moving from science fiction to reality, which has broad application prospects in the field of intelligent robots. Looking for devices that can connect and communicate with living biological tissues is expected to realize brain–computer interfaces and biological integration interfaces. Brain‐like neuromorphic devices based on memristors may have profound implications for bridging electronic neuromorphic and biological nervous systems. Ultra‐low working voltage is required if memristors are to be connected directly to biological nerve signals. Therefore, inspired by the high‐efficient computing and low power consumption of biological brain, memristors directly driven by the electrical signaling requirements of biological systems (bio‐voltage) are not only meaningful for low power neuromorphic computing but also very suitable to facilitate the integrated interactions with living biological cells. Herein, attention is focused on a detailed analysis of a rich variety of physical mechanisms underlying the various switching behaviors of bio‐voltage memristors. Next, the development of bio‐voltage memristors, from simulating artificial synaptic and neuronal functions to broad application prospects based on neuromorphic computing and bio‐electronic interfaces, is further reviewed. Furthermore, the challenges and the outlook of bio‐voltage memristors over the research field are discussed.

End-to-End Light License Plate Detection and Recognition Method Based on Deep Learning

In the field of intelligent robot and automatic drive, the task of license plate detection and recognition (LPDR) are undertaken by mobile edge computing (MEC) chips instead of large graphics processing unit (GPU) servers. For this kind of small computing capacity MEC chip, a light LPDR network with good performance in accuracy and speed is urgently needed. Contemporary deep learning (DL) LP recognition methods use two-step (i.e., detection network and recognition network) or three-step (i.e., detection network, character segmentation method, and recognition network) strategies, which will result in loading two networks on the MEC chip and inserting many complex steps. To overcome this problem, this study presents an end-to-end light LPDR network. Firstly, this network adopts the light VGG16 structure to reduce the number of feature maps and adds channel attention at the third, fifth, and eighth layers. It can reduce the number of model parameters without losing the accuracy of prediction. Secondly, the prediction of the LP rotated angle is added, which can improve the matching between the bounding box and the LP. Thirdly, the LP part of the feature map is cropped by the relative position of detection module, and the region-of-interest (ROI) pooling and fusion are performed. Seven classifiers are then used to identify the LP characters through the third step’s fusion feature. At last, experiments show that the accuracy of the proposed network reaches 91.5 and that the speed reaches 63 fps. In the HiSilicon 3516DV300 and the Rockchip Rv1126 Mobile edge computing chips, the speed of the network has been tested for 15 fps.

Flexible Piezoelectric Tactile Sensor With Cilia-Inspired Structures Based on Electrospun PVDF/Fe3O4 Nanofibers

Flexible tactile sensors have promising applications at the field of intelligent robotics. However, manufacturing flexible tactile sensors with excellent stability and high sensitivity remain a great challenge. In the present work, polyvinylidene fluoride (PVDF)-based nanofibers are prepared by electrospinning loaded with Fe3O4 nanoparticles (NPs). X-ray diffraction (XRD) and attenuated total reflection Fourier transform infrared spectroscope (ATR-FTIR) results indicate that the addition of Fe3O4 NPs promotes the generation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -phase in PVDF polymer, which greatly improves piezoelectric properties of composite nanofibers. Simultaneously, a flexible piezoelectric tactile sensor with cilia-inspired structures (PTSCS) based on PVDF/Fe3O4 nanofibers is proposed, which consists of the substrate with cilia structures, Ag electrodes, composite nanofiber membrane, and nanofiber protective layer. The PTSCS demonstrates a higher output voltage compared to sensors with bump and planar structures, which also exhibits high robustness, with a stable output after 30000 cycles of vibration testing. More importantly, the PTSCS demonstrates superior performance in recognizing surface texture and temperature of objects. The flexible PTSCS has potential use in areas such as intelligent soft robotics and material recognition.

Flexible capacitive pressure sensor array using acoustic-assisted fabrication of microstructures as surface and dielectric layers

Flexible pressure sensors have attracted considerable interests in the fields of intelligent robotics, human-machine interfaces and health monitoring for their portability, flexibility and real-time sensing performance. The microstructures of dielectric layer significantly affect the sensing performance of capacitive pressure sensors, but the common methods to fabricate microstructures always need physical and/or chemical molds, thus may not suit for low-cost fabrication of sensors. The wavy-shaped microstructures on the surface of fluid can be generated with the excitation of standing surface acoustic waves (SSAWs), which is suitable to be used as dielectric layers. Here, we developed a 6 × 6 flexible capacitive pressure sensor array with fingerprint-like surface cover layer and latticed dielectric layer. Both two layers were fabricated through the novel and rapid acoustic-assisted process. The fabricated capacitive sensor array exhibits high sensitivity of 28.8%/N for normal force loading, with spatial resolution of 1.5 mm. It has been successfully used to monitor the vibration of the Adam’s apple during swallowing and detecting the wrist activities in cycles. These studies demonstrate the great potential of the pressure sensor array with acoustically fabricated microstructures in wearable electronics for health monitoring. • A novel flexible pressure sensor array with spatial resolution of 1.5 mm for three-axis distributed force sensing is developed. • A novel and rapid acoustic-assisted process based on SSAWs is used to fabricate the surface layer and dielectric layer with microstructures. • The tactile sensor array features high flexibility and can be easily worn on neck or wrist for health monitoring. • Both Adam’s apple vibration sensing and wrist activity detection can be achieved by the developed tactile sensor array.

The Application of Interactive Humanoid Robots in the History Education of Museums Under Artificial Intelligence

The purpose is to improve the application of museum robots in museum scenes, enhance the service capabilities of robots in museums, break tourists’ boring concepts of museum environment, manual explanation, services, etc., and promote tourists’ exhibition experience. A method for sentiment analysis of humanoid robots in museums is proposed by studying the transformation of museums with the help of artificial intelligence (AI) technology, as well as the function and significance of museums in history education. First, the function of museums in history education and the role of AI in constructing intelligent museums are described. Second, on account of the multimodal sentiment analysis method of speech and emotion, a scenario model of the visitor museum is established. An uncertain reasoning method for robot service tasks based on Multi-entity Bayesian network (MEBN) is also proposed. Finally, the proposed model is validated by experiments. The results show that compared with the recognition rates of Arousal and Valence dimensions, the consistency correlation coefficient value of the Kalman filter is higher. The Consistency Correlation Coefficient (CCC) value of the Arousal dimension is 0.703, and the CCC value of the Valence dimension is 0.766. Besides, in different tour times, the proportion of services that tourists want to be provided with varies in different emotional states. From time t1 to time t2, the proportion of tourists who want to hear explanations of cultural relics dropped by 11.5%, while the proportion of tourists who want to be provided with tea service increased by 24%. This indicates that when the Kalman filter algorithm performs continuous emotion recognition of a multimodal fusion, the final emotion recognition accuracy is higher, and emotion analysis can help humanoid robots to be more intelligent and humanized. The proposed sentiment analysis based on the multimodal analysis and MEBN’s uncertainty reasoning method for robot service tasks not only broadens the practical application field of intelligent robots under human–computer interaction technology but also has important research significance for the innovative education development of museum history education.

A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm

The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively.

ISoIRS Preface

2021 International Symposium on Intelligent Robotics and Systems (ISoIRS 2021) took place in June. 10-12, 2021 in Chengdu China.The field of Intelligent Robotics and Systems has attracted over the years the attention of numerous research and industrial groups and has by now arrived at an advanced level of development via synergetic use of concepts, techniques, and technologies drawn from the electrical and mechanical engineering, control engineering, system science, computer science, and management science.What’s more, robots can be used in many situations and for lots of purposes, nowadays many are used in dangerous environments (including bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g., in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). But robots can take on any form and some are even made to resemble humans in appearance.ISoIRS 2021 has brought together leading academic scientists, researchers and research scholars to exchange their experience and research results in the field of intelligent robotics and systems and share their discoveries, insight and lessons learned, and take home exciting new ideas to perform further state of the art research to make mind-blowing collaborations and innovations. ISoIRS Committee List of titles ISoIRS committee, Conference Chair: Advisory Committee, Program Chair, Program Committee, Technical Committee, are available in this Pdf.

Action Planning and Design of Humanoid Robot Based on Sports Analysis in Digital Economy Era

<p style="text-align: justify;"><span style="font-family: "times new roman"; font-size: 16px;">With the rapid development of digital economy era, robot will become an important part of the future social development. At present, many researchers at home and abroad are also engaged in the field of robotics. Humanoid robot is one of the most active research hotspots in the field of intelligent robot because of its unique walking mode. Based on the above background, the purpose of this paper is to study the motion planning and design of humanoid robot based on sports analysis. In this study, the motion mode of the robot is generated based on the analysis of human sports movement, and the stability of its gait is controlled based on the ZMP control strategy, and the stable walking of the robot is realized. Firstly, the stable walking conditions of humanoid robot are analyzed, and the ZMP position of multi particle and single particle humanoid robot model is solved; secondly, the real-time ZMP measurement of robot is completed based on the pressure sensor of robot sole, and the algorithm optimization solution of robot hip joint angle is carried out based on ZMP control strategy, The dynamic simulation experiments in webots show that the ZMP point of the robot is always in the stable region during the walking process, which proves the validity of the gait pattern generated based on ZMP. Finally, the humanoid evaluation of the robot walking process is completed. The results of similarity function show that the gait similarity between robot and human body is more than 70% in most of the time, It can meet the human requirements of robot gait.</span>

Deep Learning Applications in Simultaneous Localization and Mapping

Simultaneous Location and Mapping (SLAM) is a research hotspot in the field of intelligent robots in recent years. Its processing object is the visual image. Deep learning has achieved great success in the field of computer vision, which makes the combination of deep learning and slam technology a feasible scheme. This paper summarizes some applications of deep learning in SLAM technology and introduces its latest research results. The advantages and disadvantages of deep-learning-based-SLAM technology are compared with those of traditional SLAM. Finally, the future development direction of SLAM plus deep learning technology is prospected.

Ultrathin Superhydrophobic Flexible Tactile Sensors for Normal and Shear Force Discrimination.

Flexible tactile sensors, with the ability to sense and even discriminate between different mechanical stimuli, can enable real-time and precise monitoring of dexterous and complex robotic motions. However, making them ultrathin and superhydrophobic for practical applications is still a great challenge. Here, superhydrophobic flexible tactile sensors with hierarchical micro- and nanostructures, that is, warped graphene nanosheets adhered to micron-height wrinkled surfaces, were constructed using ultrathin medical tape (40 μm) and graphene. The tactile sensor enables the discrimination of normal and shear forces and senses sliding friction and airflow. Moreover, the tactile sensor exhibits high sensitivity to normal and shear forces, extremely low detection limits (15 Pa for normal forces and 6.4 mN for shear forces), and cyclic robustness. Based on the abovementioned characteristics, the tactile sensor enables real-time and accurate monitoring of the robotic arm's motions, such as moving, gripping, and lifting, during the process of picking up objects. The superhydrophobicity even allows the sensor to monitor the motions of the robotic arm underwater in real time. Our tactile sensors have potential applications in the fields of intelligent robotics and smart prosthetics.

The ethical issues of social assistive robotics: A critical literature review

Along with its potential contributions to the practice of care, social assistive robotics raises significant ethical issues. The growing development of this technoscientific field of intelligent robotics has thus triggered a widespread proliferation of ethical attention towards its disruptive potential. However, the current landscape of ethical debate is fragmented and conceptually disordered, endangering ethics’ practical strength for normatively addressing these challenges. This paper presents a critical literature review of the ethical issues of social assistive robotics, which provides a comprehensive and intelligible overview of the current ethical approach to this technoscientific field. On the one hand, ethical issues have been identified, quantitatively analyzed and categorized in three main thematic groups. Namely: Well-being, Care, and Justice. On the other hand –and on the basis of some significant disclosed tendencies of the current approach–, future lines of research and issues regarding the enrichment of the ethical gaze on social assistive robotics have been identified and outlined.

Application of Neural Network and Computer in Intelligent Robot

Autonomous mobile robot is widely used in security patrol, freight, autopilot, and family life and other fields, improve the application effect is one of the most important factor is to be able to accurate navigation path, so how to design a highly efficient, intelligent autonomous mobile robot navigation path algorithm has been a popular research topic in the field. This paper mainly studies the application of the combination of neural network and computer in the field of intelligent robot. The Pninnet model is used to simulate the 3D simulation environment built in this paper, and the path navigation method of the autonomous mobile robot based on the end-to-end model is realized, and an algorithm based on ray detection is proposed to achieve obstacle avoidance. Through several groups of comparative experiments (such as comparison with Pilotnet model, etc.) and analysis, it is proved that the algorithm in this paper improves the efficiency of robot navigation research and the robot developed has a better performance in navigation in unknown or familiar environments.

Absolute Depth Measurement of Objects Based on Monocular Vision

With the continuous development of computer vision technology and the continuous upgrading of digital imaging equipment, image depth measurement method is widely used in the fields of intelligent robotics, traffic assistance, three-dimensional modeling and three dimensional video production. The following are the drawbacks of the traditional depth information measurement method: the operation is complex, the cost is high, and the measuring equipment occupies a large space and the load. In this paper, based on the Harris-SIFT corner detection algorithm, a technique is proposed to measure the absolute depth information of the object in the image using monocular vision. First of all, after the monocular camera is used to obtain the image of the target object, the image segmentation algorithm based on the LBF model is used to preprocess the image. Then, Harris algorithm in multi-scale space and SFIT algorithm to reconstruct feature descriptors are used to extract feature information in the image. Finally, by comparing the feature information between image groups, the depth information of target object is calculated by using the formula of convex hull principle and camera imaging principle. The test platform is applied to carry out measurement tests for different depth measurement methods, and the actual depth data and measurement data of the target object are compared, so as to evaluate the accuracy of the measurement method. The comparison results show that the error rate between the actual distance and the measured distance is less than 3.5%, which can accurately measure the absolute depth of the object in static and short distance, and is superior to other measurement methods.