International Journal of Robotics and Automation Technology
International Journal of Robotics and Automation Technology providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the most up-to-date accomplishment, upcoming challenges and thrilling applications in the field of Robotics and Automation Technology. This technology deals with the design, construction, operation, and application of robots as well as control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching in telephone networks, steering and stabilization of ships or aircraft and other applications with minimal or reduced human intervention.
Adaptive Trajectory Planning Method for Robotic Finishing of Concrete Shield Segments
To address the challenges of low efficiency and inconsistent quality in the finishing of concrete shield segments, this paper proposes an adaptive trajectory planning method for a robotic system utilizing point cloud data. The system integrates an automated guided vehicle (AGV), a six-degree-of-freedom serial manipulator, and a 3D vision system to create an intelligent finishing robot. A "rectangular" offline programming trajectory is employed, coupled with multi-coordinate system transformations for precise path mapping. A 3D camera captures the segment's surface point cloud, which is subsequently registered, fused, and analyzed by a neural network to identify surface irregularities and compute a look-ahead tilt angle for adaptive trajectory compensation. Experimental results demonstrate that our method significantly enhances finishing uniformity and surface quality, offering a viable technical solution for the automated finishing of complex curved components.
Copyright (c) 2025 International Journal of Robotics and Automation Technology
A Multimodal and Dynamically Updatable Benchmark for Aviation Question Answering with Large Language Models
With the rapid advancement of artificial intelligence, large-scale language models (LLMs) have demonstrated strong capabilities in open-domain question answering, knowledge retrieval, and decision support. However, in safety-critical and knowledge-intensive industries such as aviation, existing evaluation benchmarks fall short in domain adaptation, comprehensiveness, and dynamic updating. As aviation increasingly integrates intelligent automation and robotic systems for maintenance, inspection, and manufacturing, reliable language-model evaluation becomes crucial for ensuring the safety and autonomy of such systems. This paper proposes a multimodal, multi-level benchmark dataset tailored to aviation QA tasks, alongside an automated updating mechanism and a multi-dimensional evaluation framework. The methodology integrates knowledge extraction from multimodal aviation documents, diverse QA pair generation, iterative complexity enhancement, and quality validation. Furthermore, dynamic updating is achieved via a hybrid strategy combining imitation and expansion, complemented by differentiated filtering and prompt optimization. To ensure rigorous assessment, a ten-dimension evaluation framework is introduced, covering accuracy, completeness, relevance, explainability, and safety, among others. By providing a reliable and dynamically evolvable benchmark, this work supports the integration of LLMs into robotic and automated decision-support systems in aviation, enabling more intelligent, autonomous, and safety-assured operations. Experimental results using aviation textbooks confirm the effectiveness of the proposed approach in generating high-quality, dynamically evolvable QA datasets. This work provides both methodological innovation and practical tools for the evaluation of LLMs in aviation, with potential extension to other knowledge-intensive domains.
Recent Advances in Nanomaterial-Based Flexible Piezoresistive Sensors for Robotic Tactile Sensing
The expansion of industrial automation has underscored the critical role of robotic systems, where pressure sensors serve as essential interfaces for tactile feedback and precise manipulation. Flexible piezoresistive pressure sensors have become especially attractive because of their simple design, straightforward operation, and scalable fabrication. Recent progress in conductive nanomaterials such as carbon- and metal-based fillers, and engineered microstructures, including surface and porous designs in both single and hierarchical configurations, has significantly improved sensor sensitivity and broadened their operational range, demonstrating how material choice and structural engineering directly shape device performance. This review introduces the underlying physics of piezoresistivity and discusses the metrics commonly used to characterize sensor behavior, including sensitivity, gauge factor, and response time, before examining advances in nanomaterial integration and structural strategies that enable enhanced functionality. Applications highlight their integration into robotic fingertips and joints, where they provide force regulation, object recognition, and motion tracking, underscoring their importance in robotic control. Remaining challenges such as uniformity, reproducibility, and large-scale manufacturability continue to hinder widespread adoption, yet ongoing improvements in material systems and fabrication methods promise to address these limitations. Through the integration of material design, structural engineering, and performance evaluation, this work establishes a comprehensive link between fundamental principles and robotic applications, offering insights that can guide the development of next-generation tactile sensing technologies and opening pathways toward next-generation robotic systems empowered by highly sensitive and reliable pressure sensors.
Method of Sensor Fault Identification Based on High-order Sliding Mode Observers
The problem of sensor fault identification in technical systems described by linear equations under the external disturbances is studied. To solve the problem, sliding mode observer is used which is constructed based on the reduced-order model of the original system. This model is sensitive to the faults and insensitive to the disturbances. It is shown that if some conditions are met, then sliding mode exists which allows obtaining exact estimation of the fault.
Investigating How Robotics Activities Shape Elementary Students’ Attitudes Toward Music Composition and Coding
This mixed methods sequential explanatory study explored how computational thinking influences elementary students' attitudes by engaging the students in coding a music composition with an autonomous robot. Pre and post tests were used to document students’ attitudes toward music composition and coding over the course of six weeks. Eighty fifth-grade students participated in the music composition project, coding for one hour each week in an engineering class. The students were randomly organized into four study groups: individualized, collaborative, traditional, and Use-Modify-Create (UMC). Findings indicate that students experienced a significant increase in positive attitudes toward music composition after the robotics coding activity. Both the individualized and collaborative groups reported enhanced enthusiasm for music composition, while the UMC group showed increased positivity towards coding and greater confidence in their coding abilities. These results suggest that music educators can enhance student attitudes toward both computational thinking and music composition by integrating robotics into the music curriculum. The main contributions of this study include: (1) empirical evidence of positive attitude changes toward music and coding through robotics; (2) comparative analysis of four instructional modes; and (3) a practical framework for integrating robotics into elementary music education.
Signal Injection Test of Spoofing Attack to GNSS-Based Positioning in Railway Train Control
With the continuous development of the railway transportation system, train operation control is becoming more and more significant as the core to guarantee the operational safety and efficiency. Train control based on Global Navigation Satellite System (GNSS) is an important way to improve on-board sub-system autonomy and reduce the dependence on trackside facilities. However, the vulnerability of GNSS makes GNSS-based train positioning susceptible to the spoofing attack, which affects its ability to be used for novel train control systems. For this reason, it is of great significance to conduct specific test and evaluation for train positioning research, development, and applications. In this study, we construct an overall framework of spoofing injection test for GNSS-based positioning in train control, and analyze the detailed contents of test and evaluation, including spoofing attack configuration, test scenario design and generation, test dataset establishment and analysis, and typical evaluation metrics. In order to fully demonstrate the effectiveness of the proposed framework, a complete spoofing injection test environment is established. Through case studies concerning two typical spoofing modes, we successfully illustrate the effectiveness of the proposed scheme in testing and evaluating the spoofing tolerant capability and performance features of GNSS receivers dedicated to train positioning. Finally, we discuss the direction of subsequent trusted applications of GNSS in train control systems using the presented solution and platform. The results provide relevant ideas for the research of novel GNSS spoofing protection techniques for future intelligent railway systems.
Application of The “Smart Manufacturing” System to Detect a Fire in an Industrial Premises
The article considers the issue of identifying patterns between the readings of various sensors to reduce the number of false alarms of the fire safety system in an industrial premises. The identified rules will be used in the “Smart Manufacturing” system being developed.
Study on Deformation Control during Shield Tunnel Construction Beneath Existing Tunnels
This study is based on the background that the tunnel of Ningbo Metro Line 8 passes beneath the tunnel of Line 1. A three-dimensional numerical model was developed using Midas to simulate the deformation induced by the construction of the new Line 8 tunnel beneath the existing Line 1 tunnel. The results show that the maximum lateral horizontal deformation, longitudinal horizontal deformation, and vertical deformation of the Line 1 tunnel occur after the excavation of the Line 8 tunnel is completed. Notably, all deformations comply with the settlement control standards for existing tunnels in urban rail transit systems in China. After the excavation of the Line 8 tunnel is completed, the maximum surface settlement is less than the settlement control value, indicating that the excavation of Line 8 tunnel has less additional impact on the Line 1 tunnel. It is recommended that, during the construction of the new tunnel, intensive monitoring of the existing tunnel is implemented, particularly focusing on settlement in the central section, to mitigate the risk of structural damage due to excessive deformation.
Modeling and Control Experiments of a Fishtail-Like Pneumatic Soft Actuator
As the exploration of deep-sea resources continues, underwater actuators with conventional motors as the main building blocks can no longer meet the increasingly demanding needs. Inspired by bionics, researchers have started to work on underwater actuators with bionic structures. In this study, we designed and implemented a novel Fishtail-like Pneumatic Soft Actuator (FPSA). This innovative actuator configuration is inspired by the tail structure of Body and/or Caudal Fin (BCF) mode fish. The actuator's motion is achieved by controlling the expansion and contraction of the pneumatic soft muscles on both sides. And by constructing an experimental platform, we conducted an in-depth performance characterization, revealing the existence of a frequency-dependent nonlinear hysteresis characteristic of the FPSA. In order to accurately characterize this property, we built a dynamic model of the FPSA and successfully identified the uncertain parameters in the model by applying the nonlinear least squares method. The validation results show that the constructed model can accurately describe the nonlinear hysteresis characteristics of the FPSA. Finally, we successfully realized the high-precision trajectory tracking control of the endpoint of the FPSA using a PID controller. This result provides relevant ideas for the research of novel underwater bionic actuators.
A Multi-Target Electric Field Location Algorithm for Underwater Electrosense Robots Based on Sparse Bayesian Learning
Extremely Low Frequency (ELF) electric field signal provides a novel and promising solution to the target location problem due to its strong resistance to jamming and long propagation range. However, conventional algorithms such as Multiple Signal Classification (MUSIC) often rely heavily on accurate prior information. In this paper, we propose a novel underwater electric field location algorithm to accurately locate an unknown number of targets. This paper constructs a complete output model of electric field detection array in the spatial domain based on Sparse Bayesian Learning (SBL), and transforms the target location problem into sparse signal reconstruction problem. The experimental results demonstrate the effectiveness of the proposed method and its advantages over the MUSIC algorithm. The proposed location algorithm is capable of accurately locating an unknown number of ships and other targets.
This work presents four control laws applied to path following with a fixed-wing unmanned aerial vehicle, and such control laws are compared to know which of them achieved the control objective with a minor error and control effort. The control laws to compare are Proportional-derivative, Sliding Mode Control (SMC), Nested Saturations, and Fuzzy Logic. The results are obtained after several simulations using the Matlab-Simulink software.
Dynamics Modelling and Adaptive Identification: Towards Improved Human-Robot Interaction in Collaborative Systems
This article presents a novel approach to enhancing human-robot collaboration and safety through advanced dynamic modelling and adaptive identification techniques. We introduce a comprehensive methodology that integrates motion trajectory design with real-time torque detection, addressing the critical limitations of conventional systems that rely on costly joint torque sensors. By simultaneously identifying friction forces in an integrated joint and a simplified two-bar mechanism, our approach leverages existing kinematic and dynamic models to achieve precise dynamic parameter identification. The proposed method significantly advances the fields of drag-teaching and collision detection by eliminating the need for force sensors, thus making it more feasible for mass-produced robotic systems. Our findings demonstrate that accurate dynamic modelling is essential for effective zero-force control, particularly in high-speed drag-teaching scenarios, where inertia and friction present substantial challenges. Experimental validation confirms the efficacy of our dynamic feed-forward controller design and the adaptability of drag-teaching parameters, leading to improved operational flexibility and safety in collaborative environments. This research contributes a critical framework for future developments in intelligent robotic systems, providing a robust basis for integrating advanced human-robot interactions in industrial applications.
Research on Few-Shot Defect Detection Algorithm Based on Federated Learning
The algorithm based on deep learning has been widely used in defect detection in all walks of life, but the performance of the deep learning model depends mainly on rich annotation data. However, in the actual scene, obtaining large-scale, high-quality data to ensure users' privacy and safety is challenging, which limits its further promotion in specific application fields. To solve this problem, we propose a federated few-shot defect detection framework, which uses the privacy protection of the federated framework to jointly train independent few-shot tasks distributed on different clients to obtain a few-shot model that can quickly adapt to new tasks with limited data. We have done many experiments to evaluate our framework's effectiveness, and the results show that our framework is superior to the baseline and achieves the same performance as the model trained with a lot of data.
Consumer Feedback Sentiment Classification Improved Via Modified Metaheuristic Optimization Natural Language Processing
This study investigates the synergy between the virtual and real-world economies through e-commerce, where seller reputation is critical in guiding consumer decisions. As traditional businesses shift towards online retail, user reviews become essential, offering feedback to both sellers and potential buyers. Sentiment analysis through machine learning (ML) techniques presents significant advantages for consumers and retailers alike. This research proposes a novel approach combining bidirectional encoder representations from transformers (BERT) embeddings with an optimized XGBoost classification model to enhance sentiment analysis performance. A modified metaheuristic algorithm, derived from the firefly algorithm (FA), is introduced to optimize the model. Testing on publicly available datasets demonstrates that models optimized by this algorithm achieved a peak accuracy of .881336. Further statistical analyses substantiate these improvements, and SHAP interpretation on the best-performing model identifies key features impacting model predictions, shedding light on factors driving customer sentiment insights.
Investigation and Fabrication of Brilliant Green Dye Material-Based Organic Heterojunction: Dielectric Response and Impedance Spectroscopy
Brilliant green (BG) dye material-based heterojunction is fabricated by a spin coating route and characterized by impedance analyzer. Here, we study the impedance spectroscopy of such organic material based thin film onto silicon substrate. The capacitance and conductance versus voltage (C-V), (G-V) characteristics are plotted. So, the dielectric response of BG based heterojunction is studied via the plotting of the dielectric constant, modulus components, complex impedance and Nyquist diagram. Real and imaginary parts of electrical conductivity are also plotted at several frequencies. Real and imaginary parts of electric modulus M’-V and M”-V are investigated for all experimental frequencies. The Z’-V characteristics of our device-based BG organic heterojunction exhibit a peak of 1383 W. It is confirmed that an anomalous peak of Z’ is recorded within 0.5-1 V range. The drastic decay in Z”-V plot occurs for lower 100 and 200 kHz frequency range and Z” values become constant beyond 1V for all frequencies. Ac and Dc conductivity curves demonstrate a growth with frequency and angle phase approaches to 90º within reverse voltage. This result reveals a capacitive conduct of our device.
Lethal Autonomous Weapon Systems: Ethical Dilemmas and Legal Compliance in the Era of Military Disruptive Technologies
Lethal Autonomous Weapon Systems (LAWS) have emerged as one of the most significant advancements in military technology, leveraging artificial intelligence (AI) and machine learning to execute missions without direct human control. As these systems become central to modern warfare, they raise critical questions about their compliance with International Humanitarian Law (IHL) and International Human Rights Law (IHRL). This paper delves into the legal and ethical debates surrounding LAWS with particular attention to the discussions within the Group of Governmental Experts on LAWS (GGE on LAWS). We analyze whether these technologies can adhere to fundamental human rights while maintaining their operational efficacy. Through the application of the Autonomy Spectrum Framework to real-world scenarios, the study highlights both the strategic advantages of LAWS and the risks of dehumanizing warfare. The need for robust legal frameworks to ensure accountability and human oversight remains paramount.
Emerging and Disruptive Technologies (EDTs)
Self-Organized Multi-Robot Path Planning and Chain Distribution Based on Improved DWA and A* Fusion in Unknown Space
With the rise of autonomous driving in recent years, path planning has gained widespread attention. Traditional path planning needs to be based on a large amount of known information, which is not available for confined environments. Taking the complex indoor space where GPS cannot be used as the research background, the article designs a self-organised motion scheme for multi-intelligent body trolleys that includes exploration and path planning. By improving the DWA and A* algorithms, the multi-robot self-organisation achieves reasonable path planning, and the fusion of the two algorithms solves the contradictory problems of global planning being unable to avoid dynamic obstacles and local planning possibly falling into local optimum. After that, the pilot-following algorithm is added to guide the multi-intelligence body to operate in formation. By studying the constraints of hardware such as LiDAR and machine trolleys, the chain distribution of multiple intelligences is proposed to solve the problem of information loss caused by the discontinuous monitoring field of view. Eventually, when the carts are all in position, the whole area is covered and monitored using sensor fusion with multiple viewpoints. The feasibility of the explored scheme is verified by simulation experiments, and the feasibility and robustness of multi-sensor fusion is verified by specific hardware.
Computer Vision Based Areal Photographic Rocket Detection using YOLOv8 Models
Advances in aerospace engineering and aerodynamics have pioneered space exploration and helped support telecommunication infrastructure. But these same developments have also aided in the creation of weapons of devastating impact. This necessitates the development of ways for detecting and tracking rockets. While several methods, mostly based on Doppler radar exist, the need for active radio emissions limits the applicability of these systems. A passive system has several advantages over traditional techniques, however their potential is largely unexplored. This work seeks to tackle this research gap by exploring the potential of emerging computer vision townies applied to rocket detection and tracking. The advantages of such a system are the relatively low cost as well as passive nature making observation stations harder to detect and easier to deploy. This work explores the potential of pre-trained, lightweight YOLOv8 architectures for rocket detection in real-world situations. A publicly available dataset is utilized and a comparative analysis is carried out between nano and small models. Both models demonstrate favorable outcomes with an accuracy of 0.90 for rocket body detection and 0.93 for engine flame detection. Nevertheless, rocket detection into space is still difficult, with a precision of 0.64 for this class. This paper indicates areas for additional refinement and demonstrates the potential of computer vision technology in passive rocket detection.
Concept Design of Reproductive Worm Robot: A Natural Inheritance of C.elegans Biological Worm
Abstract: Although research in the field of worm robotics has taken some stride in recent past, the connotation of such designs was missing the feel of biology in true sense hitherto. Design and firmware of bio-inspired robots have been attempted by several research groups globally but none of those seriously intrude different physiologoical systems of the said biological specimen. Our attempt on technology-driven ideation of the reproductive system of a celebrated biological worm, namely, Caenorhabditis elegans (C.elegans) has culminated into concept-designs of Reproductive Worm Robot. Incidentally, C.elegans is an interesting biological entity that evokes imagination and assertion to create miniature robotic systems, especially by mimicing its reproductive system. In this paper, we have reported the technological concept designs as well as part-hardware of the working prototype of representative Reproductive Worm Robots by naturally inheriting the reproductive mechanism of the biologocal C.elegans worm (notwithstanding the size effect).
Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures
Abstract: This work aims to test the performance of the you only look once version 8 (YOLOv8) model for the problem of drone detection. Drones are very slightly regulated and standards need to be established. With a robust system for detecting drones the possibilities for regulating their usage are becoming realistic. Five different sizes of the model were tested to determine the best architecture size for this problem. The results indicate high performance across all models and that each model is to be used for a specific case. Smaller models are suited for lightweight system approaches where some false identification is tolerable, while the largest models are to be used with stationary systems that require the best precision.
Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function
Abstract: This paper proposes a novel finite horizon memory control (FHMC) design framework for networked systems by using input delay approach. A chain-like input delay model is established to characterize the networked control system (NCS) under memory control architecture in order to make full use of historic sampled-data. Based on the proposed chain-like delay model, the corresponding chain-like Lyapunov-Krasovskii function, which records the historic sampling information of NCS, is well constructed for facilitating further analysis and synthesis of the proposed FHMC scheme. Both state feedback controllers and static output feedback controllers are derived by solving LMIs (Linear matrix inequalities). The proposed FHMC scheme is skilled in improving control performance of networked systems. Simulations show the effectiveness of the presented FHMC scheme.
Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving
Abstract: Fisheye cameras, valued for their wide field of view, play a crucial role in perceiving the surrounding environment of vehicles. However, there is a lack of specific research addressing the processing of significant distortion features in segmenting fish-eye images. Additionally, fish-eye images for autonomous driving face the challenge of few datasets, potentially causing over fitting and hindering the model's generalization ability. Based on the semantic segmentation task, a method for transforming normal images into fish-eye images is proposed, which expands the fish-eye image dataset. By employing the Transformer network and the Across Feature Map Attention, the segmentation performance is further improved, achieving a 55.6% mIOU on Woodscape. Additionally, leveraging the concept of knowledge distillation, the network ensures a strong generalization based on dual-domain learning without compromising performance on Woodscape (54% mIOU).
Linear Active Disturbance Rejection Lateral Controller for Unmanned All-Terrain Vehicle
Abstract: To address the disturbance of model uncertainty, a linear active disturbance rejection controller (LADRC) was designed for robust lateral control of unmanned all-terrain vehicle. In terms of relative motion of target node and current state, first-order lateral tracking model is established. According to the developed model, linear tracking differentiator (LTD), linear extended state observer (LESO) and linear state error feedback (LSEF) are designed in turn. LESO could observe the uncertainty of system and LSEF could compensate the uncertainty to make system robust. In order to verify the effectiveness, two typical scenarios, circle and double lane tracking, were designed for test. And the uncertainties of wheelbase and steering ratio were considered. Results illustrate that the designed LADRC can stably control the unmanned all-terrain vehicle tracking reference trajectory under both scenarios and has the advantages of small tracking error and small overshoot compared with the conventional pure tracking methods.
Linear active disturbance rejection control
MRAC with SMC Applied to Lateral Control of a Fixed-Wing MAV
Abstract: This paper presents a PD control law with adaptive gains with the MIT (Massachusetts Institute Technology) rule with different sliding modes; that is, the MIT rule has been designed with is known in the literature with first order sliding mode, second order sliding mode and high order sliding mode (HOSM) to obtain a better gain scheduling taking advantage the sliding modes techniques-the PD control law with adaptive gains that is designed for the lateral dynamics of a fixed-wing MAV. To apply the methodology of the model reference adaptive control (MRAC), sometimes called model reference adaptive system (MRAS), to the adaptive gains of the PD control, a sliding manifold is proposed considering the output of the lateral dynamics and with the output of the reference model.
MIG-Assisted Kernel-Enabled Robot (MAKER) Arm for Seamless Automobile Maintenance and Service
Abstract: There is a definitive increase of excellence in the field of robotic automation, with automated vehicles that can drive people anywhere to automated robots that can perform high-risk surgeries remotely. Robotic automation was initiated as an industrial-grade asset capable of performing complex tasks and replacing humans limited by fatigue. With the advent and rise of Industry Revolution 4.0 (IR 4.0), in the modern world, one of the major markets that IR 4.0 occupies is the automobile industry. The automobile industry heavily employs robotic technology for vehicle manufacturing and assembly, yet post-sale servicing and maintenance remain predominantly manual. This discrepancy results in a gap in the efficient and timely maintenance of vehicles once they’re in the hands of customers. According to a report of Allied Market Research [1], the expected Compound Annual Growth Rate (CAGR) of the global automobile repair and service market from 2022 - 2031 is expected at 7.6%, corresponding to 1,656.21 billion US dollars by 2031. This establishes the scale of the market that the proposed solution is going to be primarily based on. This paper proposes a MIG-based solution for providing welding service to damaged and sheared automobiles, thus reducing the stated discrepancy. The proposed solution creates a kernel environment where the twin-headed robotic arm can assess its surroundings and perform appropriate action from its operation pool using Reinforcement Learning and Machine Learning. The twin-headed robotic arm holds a torch and lead (Al) on the heads to perform the desired operations. This innovative approach is equipped with advanced sensors and programming to accurately detect, diagnose, and service vehicles by leveraging On Board Diagnostic (OBD) systems. This study delves into the theoretical and technical complexities of building an automated welder that explores the practical application of robotic technology in the automobile aftermarket. Notably, this technology promises improved accuracy, consistency, and timeliness in car maintenance, significantly reducing human error, improving service times, increasing productivity, and inducing economic growth.
Research on Force Interaction Devices Specialized for Blind Guidance Robot
Abstract: As a new type of human-computer interaction, physical human-robot interaction is very common in guide robots and is the mainstream of future development. However, the force interaction device is hard to choose according to actual application requirements. In this paper, we compared the force transmission characteristics of three interaction mediums: rigid rod, spring-damping, and cable. The result shows that the rigid rod will bring a force impact on blind users when the robot’s motion state switches, while the spring-damper can not perform well in its response and stability. In contrast, the cable is more suitable in this area for its fast response and stability, moreover, it does not impose a dramatic impact when imposing an excitation force.
Design of Crop Irrigation Decision-Making System Based on ZigBee Technology
Abstract: To promote the high-quality development of agriculture, meet the needs of agricultural products grown in greenhouses, and achieve precise monitoring of greenhouse plants, a small greenhouse micro-environment multi-parameter monitoring system was designed and implemented. The system consists of three parts: a monitoring node, a gateway node, and a remote management platform. The monitoring node used the ESP32 microcontroller as the main control chip, combined ZigBee technology, and embedded different sensors to complete the collection and transmission of environmental parameters. In the gateway node, the 4G Data Transfer Unit module was used as the carrier, and the communication protocol was used to realize data communication between the monitoring terminal and the gateway. The remote management platform was based on the PyCharm development platform. It used the PyQt5 graphical user interface (GUI) toolkit to complete the design of the host computer monitoring platform, establish a database, and realize the storage and analysis of environmental parameters. The remote management platform embedded the crop reference evapotranspiration, online calculation model, to provide irrigation decisions for greenhouse crop management and improve the applicability and accuracy of irrigation decisions. After the experimental test platform was built to test the system communication distance, communication reliability, control reliability, and data reliability, the small greenhouse micro-environment multi-parameter monitoring system designed in this paper can operate stably for a long time. Its functions meet the expected requirements and are in line with modern requirements for multi-parameter monitoring of smart greenhouses.
Design and Experiment of a Reconfigurable Bypass Downlink Surface Equipment for Transmitting the Surface Information to Deep Well
Abstract: In order to achieve the transmission of the control information from the surface to the downhole in the deep well during drilling, a design method of a new downlink surface equipment for generating negative pulse signals is presented in this paper. The equipment uses adjustable throttle control valve and pressure drop module (PDM) to realize bypass flow control. The PDM uses multistage variable nozzles to reduce drilling fluid pressure and meet drilling requirements under different pressure and flow rates. Design theory of the downlink surface equipment is established, and a specific design instance is given according to actual drilling conditions. Both laboratory and field experimental results verify the correctness of the proposed design method. The research results show that the design equipment can meet the needs of the transmission of the surface information, and the downlink transmission rate can reach at least one-bit data per 8 sec. The maximum pressure drop is realized by the PDM and the pressure drop at the control valve will be reduced according to drilling demand, the fluid velocity in the equipment is reduced as much as possible, the erosion of the drilling fluid to the equipment is reduced, and the service life can be improved effectively.
Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning
Abstract: The objective of mobile robot path planning (MRPP) is to devise the shortest obstacle-free path for autonomous mobile robots based on a given terrain. Numerous MRPP methods have been extensively researched. This paper presents a novel approach called Opposition-based Learning Equilibrium Optimizer (OEO) for generating smooth paths for mobile robots. The fundamental idea behind OEO is to introduce an opposition-based learning mechanism while maintaining the overall framework of the basic EO algorithm. This modification alleviates the susceptibility of the basic EO algorithm to local optima. The OEO algorithm is employed to provide smooth paths for autonomous mobile robots, and the results are compared with several classical metaheuristic algorithms. Comparative analysis across different environments demonstrates that the proposed OEO-based path planning method consistently yields the shortest and most collision-free paths with superior stability.
A Review on Variation Modeling of Aircraft Assembly
Abstract: Purpose; The purpose of this paper is to provide a state-of-art review on variation modeling and propagation in aircraft assembly based on process-oriented strategy. And the main focus is on classifying and delineating different approaches, methods, and techniques with critical appraisals of their theories, applications, and limitations, based on which future research directions and corresponding methodologies are proposed. Design/methodology/approach; To facilitate understanding of the background, this paper starts with a brief description of aircraft assembly. Afterwards, this paper presents a comprehensive review of practical solutions in aircraft variation modeling. Their characteristics are summarized, which serves as a basis for the discussion of deviation control strategies. Thereafter, the possible trends are discussed to facilitate assembly quality control in future research.