Intelligent Robotic Systems Research Articles

Hygrothermic Wood Actuated Robotic Hand.

Stimulus-responsive actuators play a vital role in the new generation of intelligent systems. However, poor mechanical performance, complicated fabrication processes, and the inability to complex deformation limit their practical applications. Herein, these challenges are overcome via designing a strong hygrothermic wood actuator with asymmetric water affinity. The actuator is readily constructed by sandwiching polypyrrole-coated wood with a Ni complex hygroscopic gel top layer for moisture absorption and a polyimide bottom layer as the water barrier. The resulting hygrothermic wood spontaneously stretches and bends itself in response to moisture and thermal/light stimulation. A robotic hand and a series of grippers made of hygrothermic wood demonstrate dexterous object-hand interactions during grasping and holding, while the reversible hygrothermic property allows the actuator to be potentially applied in fire rescue scenarios to rescue trapped objects. A combination of good mechanical properties, multi-stimulus-response, complex deformation, wide working temperature range, low manufacturing cost, and biocompatibility are simultaneously realized by one device. It is thus believed that such a strong wood actuator will open up a new avenue for building intelligent robotic hand systems.

Preface

It is our great pleasure to have you at the 2022 7th International Seminar on Computer Technology, Mechanical and Electrical Engineering (ISCME 2022), which was held in Hangzhou, China from November 25th-27th, 2022 (virtual conference). ISCME has been successfully held for six consecutive years even under the influence of COVID-19, and received unanimous praise from various experts and scholars worldwide.ISCME 2022 is an international forum for academic communications between experts and scholars in the fields of computer technology, mechanical and electrical engineering. And it promoted research and developmental activities in related fields and scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world, establishing business or research relations for all participants to find global partners for future collaboration.There are about 200 participants from all over the world. The conference agenda covered three parts, including a keynote speech delivered by four sophisticated professors from different countries and regions. Among them, Prof. Ting Yang from Tianjin University, China made a speech on Cyber Physics Fusion Technology’s Application in Energy Internet. Energy Internet is to establish and develop a comprehensive energy system based on Internet thinking. It integrates multiple energy sources and realizes the interaction of energy and information. In this speech, he discussed the cyber physics fusion technology and its key technologies applied in Energy Internet, including the requirements for different energy services, the fusion framework, and communication protocols and information security technologies. It provided a great opportunity for all the participants to get some knowledge and some different insights on related fields from the speech and the discussion followed by.We have received tremendous submissions from all over China and abroad. Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility. Based on the expertise review comments, many excellent papers were accepted, and the corresponding authors were invited to submit their papers for final publication. The accepted papers covered topics on: Intelligent Mechatronics, Robotics, Biomimetics, Automation and Control Systems, Industrial Automation, Process Control, and Manufacturing Process, etc.We would like to express our gratitude to the organizers, keynote speakers and all the participants of ISCME 2022, for their fruitful work and contribution to the success of the conference. We are thankful to all the reviewers for providing constructive criticisms, stimulated comments and suggestions to the authors. In particular, our special thanks go to the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We deeply appreciate their efforts in making the conference successful.The Committee of ISCME 2022List of Committee member are available in the pdf.

Development of an Intelligent Vital Sign Monitoring Robot System

We want to solve the problem that the elderly or heart patients will not miss the golden time for rescue incase of sudden death or emergency event at home. Therefore, this work proposes an intelligent vital signmonitoring robot system. Considering data protection and privacy issues, the heart rate of the elderly or heart patient can be measured and monitored through the millimetre wave radar on an autonomous mobilerobot (AMR). Additionally, by object detection technology, the infrared thermal imager is integrated todetect the temperature of a person’s head. The measured head temperature, thermal image and heart ratedata can be uploaded to cloud database with blockchain technology to prevent data tampering. We develop an interrupt method for multi-target navigationof AMR to interrupt the patrol when a custom triggersituation happened and to run the interrupt process for vital signs measurement. While the interrupt process is completed, the robot will patrol again. When the system detects an abnormal situation, familymembers or caregivers can be notified through IFTTT service and Line notification messages. The systemcan help us to monitor the situations of elderly and provide response in time when a sudden emergencyevent or health crisis happened.

Application of Robot Obstacle Avoidance Algorithm and Attack Strategy on ROS

China Robot Competition, which integrates the theory and practice of multi-disciplinary intelligent robot system, is one of the most influential robot competitions in China. In this paper, we apply RRT * and APF to the Middle Size Simulation League of soccer on ROS and propose an attack strategy based on extremum. Our experiments showed that the implementation of RRT* and APF optimizes the multiple iterations existing in original obstacle avoidance algorithm and greatly improves the performance of robots when there are fewer obstacles and the maximum speed of obstacle avoidance is limited. Besides, our attack strategy has proved effective on boosting the chances of goal and helped the Central South University to win the championship of China Robot Competition Middle Size Simulation League in 2021.

A Visible and Near‐Infrared Light‐Fueled Omnidirectional Twist‐Bend Crawling Robot

AbstractIn recent years, although light‐driven soft actuators have attracted intense scientific attention and achieved remarkable progress, the design and construction of an intelligent robotic system with maneuverability, self‐adaptability, untethered control, and greater freedom of action, in particular the omnidirectional motion capability on a plane, remains challenging. Herein, four types of photo‐thermal fillers and an unprecedented twist‐bend actuation mode is introduced into a liquid crystal elastomer‐based soft robot. The obtained twist‐bend crawling robot not only exhibits in situ rotation, four‐way turning, and four‐way linear motion under light irradiation with four wavelength bands (520, 655, 808, and 980 nm), but also demonstrates the ability to avoid obstacles in complex geographical environments. This work may bring a new perspective for fabrication and development of soft robots that can adapt to dynamic and complex environmental conditions.

Molecule‐Assembled Plasmonic Gold Nanoparticle Network for Piezophototronic and Human Activity Detections

Strain‐based complex functionalities in response to external physical stimuli are significant for the development of intelligent robotic systems. Herein, an assembly of gold nanoparticles through ligand interaction with short linker molecule 2‐picolylamine (PA) is developed. With subnanometer spacing between the nanoparticles (NPs), the network exhibits strain sensitive carrier tunneling between them. Based on these NP networks and a lithographically patterned polydimethylsiloxane (PDMS) microstructure, a multistimuli‐responsive tactile sensor is developed that effectively transduces mechanical strain to electrical responses, and can identify variable weights, detect finger touch patterns, and is ultrasensitive to vibrational movements. Physical activities such as jogging, leg movements, and sit‐to‐stand postures are identified using the system. A significantly high gauge factor of 243 ± 10 associated with the Au NP–PA network is recorded. In addition, the sensor exhibits strain‐dependent plasmonic photodetection with a responsivity of 309 mA W−1 at a tensile strain of 3.7%. The multistimuli‐responsive Au NP–PA network demonstrated here opens up new perspectives on the development of intelligent multifunctional systems.

A Novel Pet Trajectory Prediction Method for Intelligent Plant Cultivation Robot

Intelligent plant cultivation robots will have certain working obstacles due to various moving objects, such as pets. This letter studies how to predict the trajectory of pets to avoid collisions. In previous intelligent plant cultivation robot systems, target-tracking technology was used to track pets and avoid collisions. However, collisions often occur even after tracking the trajectory of the pet because the trajectory of the pet's movement is difficult to determine. Therefore, the Informer algorithm is used to predict the trajectory of pets in this letter. It is presented that collisions can be avoided based on the predicted trajectory. Experimental results on the data set show that this algorithm has better accuracy than most common algorithms, so trajectory prediction based on this algorithm can make the robot avoid obstacles better. Finally, by verifying two data sets, the public data set and the self-made data set, it is determined that the performance of the proposed method is optimized by 3%–50% compared with other methods.

Four-Dimensional Printing of Stimuli-Responsive Hydrogel-Based Soft Robots.

The present protocol describes the creation of four-dimensional (4D), time-dependent, shape-changeable, stimuli-responsive soft robots using a three-dimensional (3D) bio-printing method. Recently, 4D printing techniques have been extensively proposed as innovative new methods for developing shape-transformable soft robots. In particular, 4D time-dependent shape transformation is an essential factor in soft robotics because it allows effective functions to occur at the right time and place when triggered by external cues, such as heat, pH, and light. In line with this perspective, stimuli-responsive materials, including hydrogels, polymers, and hybrids, can be printed to realize smart shape-transformable soft robotic systems. The current protocol can be used to fabricate thermally responsive soft grippers composed of N-isopropylacrylamide (NIPAM)-based hydrogels, with overall sizes ranging from millimeters to centimeters in length. It is expected that this study will provide new directions for realizing intelligent soft robotic systems for various applications in smart manipulators (e.g., grippers, actuators, and pick-and-place machines), healthcare systems (e.g., drug capsules, biopsy tools, and microsurgeries), and electronics (e.g., wearable sensors and fluidics).

Retracted] A Voice Recognition Sensor and Voice Control System in an Intelligent Toy Robot System

In order to make the toy robot more entertaining, interesting, and intelligent, a voice recognition sensor and voice control system in the intelligent toy robot system are proposed. The system builds an overall system architecture including a client and a server. Through the camera calibration and data transmission module of the client, it collects images and calculates the internal and external parameters of the camera and transmits the image and external parameter data to the server. With the images and external parameter data transmitted from the terminal, a background image is constructed and the camera position and angle are updated in real time to complete the fusion of virtual and real scenes. Through the motion control part of the user interaction module, hearing‐impaired children can control the movement and rotation of smart toys. The experimental results show that the system has high communication synchronization and stability and can realize high‐precision control of smart toys, and the average frame rate can reach 30.97 f/s. The beneficial effect of the system is that it has various functions, has the effect of speech recognition, and is highly interesting.

Industrial Cross-Robot Transfer Learning

With increased complexity and individualization of processes and products, the manufacturing industry experiences growing pressure to automate. To meet these constantly growing requirements regarding the flexibility of production and logistic systems, intelligent robotic systems that can adapt dynamically to changing process conditions are essential. Deep reinforcement learning provides a solution and offers a paradigm of learning adaptive control strategies through autonomous agents. However, the corresponding training procedure is based on trial-and-error interactions with an environment and is thus very data inefficient and costly in real-world scenarios. Transfer learning approaches can mitigate these costs and improve learning by leveraging control capabilities acquired in other settings, such as simulations, or from previously learning a different task. In this paper, we investigate the transfer of trajectories between different robot models. We propose a methodology that maps trajectory demonstrations from a source robot into trajectories of a target robot by using forward and inverse kinematics models. We introduce a similarity measure, that one the one hand compares different robot models using key poses along the kinematic chains, and on the other hand captures the efficiency of transitions. This similarity measure is used to select from the mapped trajectories those which are feasible, efficient and retain the general shape of the robotic arm. Finally, we perform behavioral cloning to pre-train a deep reinforcement learning policy for the target domain and demonstrate significant benefits compared to learning the target policy from scratch.

Retracted: A Voice Recognition Sensor and Voice Control System in an Intelligent Toy Robot System

Retracted: A Voice Recognition Sensor and Voice Control System in an Intelligent Toy Robot System

Integration of Robotic Vision and Automatic Tool Changer Based on Sequential Motion Primitive for Performing Assembly Tasks

Studies on intelligent robotic manipulation systems have typically focused on the programming efficiency, adaptive control of robotic arms, motion planning of robotic arms, and action diversity of grippers. In this study, a decision tree and visual recognition incorporate into a robotic arm to help it learn complex tasks. This study employed a task tree for the automatic planning of complex tasks, in which a decision-making model was used to generate complex task sets from a pre-built motion dataset in real-time performance. Moreover, the model can analyze the rationality of model steps, introduce new tasks, and perform object analysis. This work applies a support vector machine to identify the state of an object. The model selects a suitable gripper with a rapid gripper switch process by considering the characteristics of the targeting object. This study demonstrated the effectiveness of the proposed approach with suitable intelligence through the assembly task.

A Digital Twin-Based Intelligent Robotic Measurement System for Freeform Surface Parts

This paper proposed a novel framework of a digital twin-based robotic measurement system for freeform surface parts, which can improve the efficiency, autonomy, and intelligence of the robotic measurement system. This framework provides core services for robotic measurement tasks including visibility computation, collision detection, sensor simulation, path planning, and 3D inspection. These services improve the adaptability and the simulation capability of digital twin-based robot measurement system for freeform surface parts. Based on the proposed framework, an effective viewpoint planning method combining viewpoint sampling strategy and nest-best-view planner (VS-NBV) for freeform surface parts is proposed and verified. And case studies demonstrated the advantages of the digital twin-based robotic measurement system in terms of viewpoint planning, measurement path planning, object reconstruction, and 3D inspection.

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy of the algorithm proposed in this paper has increased by 31.26%, 14.21%, 3.05%, and 1.56%, respectively. In addition, the comparison experiment results of SLAM mapping with the original Real-Time Appearance-Based Mapping (RTAB-MAP) algorithm prove that the loop closure detection algorithm based on deep learning proposed in this paper can enable the system to better build a globally consistent map, including more environmental information.

Design of intelligent robots for tourism management service based on green computing.

The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method to complete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency wireless communication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol are completed for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing shortcomings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot.

Adaptive Bézier Degree Reduction and Splitting for Computationally Efficient Motion Planning

As a parametric polynomial curve family, Bézier curves are widely used in safe and smooth motion design of intelligent robotic systems from flying drones to autonomous vehicles to robotic manipulators. In such motion planning settings, the critical features of high-order Bézier curves such as curve length, distance-to-collision, maximum curvature/velocity/acceleration are either numerically computed at a high computational cost or inexactly approximated by discrete samples. To address these issues, in this article we present a novel computationally efficient approach for adaptive approximation of high-order Bézier curves by multiple low-order Bézier segments at any desired level of accuracy that is specified in terms of a Bézier metric. Accordingly, we introduce a new Bézier degree reduction method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parameterwise matching reduction</i> , which approximates Bézier curves more accurately compared to the standard least squares and Taylor reduction methods. We also propose a new Bézier metric, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum control-point distance</i> , that can be computed analytically, has a strong equivalence relation with other existing Bézier metrics, and defines a geometric relative bound between Bézier curves. We provide extensive numerical evidence to demonstrate the effectiveness of our proposed Bézier approximation approach. As a rule of thumb, based on the degree-one matching reduction error, we conclude that an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ n^{\rm{th}} $</tex-math></inline-formula> -order Bézier curve can be accurately approximated by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3(n-1)$</tex-math></inline-formula> quadratic and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6(n-1)$</tex-math></inline-formula> linear Bézier segments, which is fundamental for Bézier discretization.

"Design and Development of Intelligent Robotic System for Precision Agriculture"

"Design and Development of Intelligent Robotic System for Precision Agriculture"

Snake robots play an important role in social services and military needs

Intelligent robotic systems have gradually penetrated into the fields of social services and military reconnaissance with the rapid economic development. Snake robots, as multi-redundant bionic robots, play an indispensable and important role in public life and military needs.1 These robots have been widely favored and highly regarded by the academia and industry because of 1) their small size and flexibility, thereby easily entering small spaces for work, 2) their ability to carry a variety of equipment for disaster rescue and military reconnaissance, and 3) their variety of movement modes, which can adapt to various complex terrain environments.

Template-Based Category-Agnostic Instance Detection for Robotic Manipulation

An intelligent robotic system is one of the key pillars of a smart factory that requires flexibility to handle a variety of tasks. Perception is a key enabling technology for robots. Most existing object detection studies have mainly focused on category-specific objects and have achieved impressive performance. However, robotic systems, particularly in industrial scenarios, typically interact with many category-agnostic objects, which the robot must detect instantly without pre-training. Therefore, in this study, we proposed a template-based detection and segmentation approach, which incorporated a multi-level correlation model and a similarity-refine module, for handling the category-agnostic instance. The proposed approach was then validated and demonstrated in an interactive and adaptive robotic application scenario designed for the typical pick-and-place task. Among them, the picking scan path and location were instructed through human guidance with hand tracking. The neural rendering technology was also introduced to render novel views of the template. The proposed approach was evaluated using a benchmark and verified through a real demonstration.

Artificial Intelligence and Civil Liability: Juridical Prospects and Legal Challenges

The benefits to individuals from the implementation and use of robots and other artificial intelligence applications are widely known. However, although the use of intelligent robots is not yet fully widespread, it is possible to anticipate that the introduction of these robots into society will present numerous moral, ethical and legal challenges. Currently, robots are considered products and they fall under Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of the Member States concerning liability for defective products. The European Union itself has expressed skepticism regarding the applicability of this directive to civil liability for damages that these autonomous and intelligent robotic systems may cause. The aim of this article is to identify and analyze what these challenges may be.