Search Results (2,240)

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural networks), this adaptive capability enables the ZNN to rapidly and accurately solve time-varying problems. By leveraging dynamic zeroing error functions, the ZNN exhibits distinct advantages in addressing complex time-varying challenges, including matrix inversion, nonlinear equation solving, and quadratic optimization. This paper provides a comprehensive review of the evolution of ZNN model formulations, with a particular focus on single-integral and double-integral structures. Additionally, we systematically examine existing nonlinear activation functions, which play a crucial role in determining the convergence speed and noise robustness of ZNN models. Finally, we explore the diverse applications of ZNN models across various domains, including robot path planning, motion control, multi-agent coordination, and chaotic system regulation. Full article

Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion’s development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields. Full article

Facing the support challenges of short-wall working face (15–40m) roadways in the ‘excavation–backfill–retention’ tunneling method for section coal pillars, traditional equipment struggled to achieve stable, reliable, and efficient support. This paper designed a temporary support robot for the excavation and mining system of section coal pillars to ensure the safety of equipment and personnel in short-wall working faces. The support requirements of the section coal pillar excavation and mining system were analyzed, and a general ‘driving under pressure’ temporary support scheme was proposed. The working principle of the temporary support robot was analyzed. A mechanical model for the stable support of the temporary support robot was established. The mechanical properties of the surrounding rock were analyzed, and the allowable range of the temporary support robot’s supporting force was determined while ensuring the stability of the surrounding rock. Based on the Stribeck friction theory, a dynamic model of the temporary support robot in the driving under pressure state was constructed. The boundary conditions of the dynamic model were set, and the corresponding relationship between the temporary support robot’s supporting force and its maximum static friction force was determined. This accurately described the influence of the supporting force and pushing (pulling) force on the movement during the process of driving under pressure. Through finite element simulation, the stress conditions of the temporary support robot and the floor under maximum load were analyzed, indicating that this load condition would not cause damage to the temporary support robot or the surrounding rock. Through multi-body dynamics simulation, the pushing (pulling) forces required for the temporary support robot’s movement under different supporting force conditions were obtained, verifying the feasibility of the driving under pressure action under different supporting force conditions. Moreover, the model-predicted and simulated values of the required pushing (pulling) forces during the process of driving under pressure were consistent, validating the accuracy of the driving under pressure dynamic model. This research provides a new theoretical framework for the design and dynamic analysis of temporary support equipment for short-wall working faces in section coal pillar mining, holding significant academic value and broad application prospects. Full article

Introduction: Radical cystectomy (RC) is the gold standard for urothelial cT2-4a, N0, M0 muscle-invasive bladder cancer (MIBC). However, bladder-sparing strategies (BSS) such as Trimodality Therapy (TMT) have emerged as alternative treatments for a select group of localized muscle-confined (cT2) urothelial bladder cancers. Accordingly, reliable preoperative staging and a reliable risk factor assessment linked to pathological upstaging play a key role in adequate counselling and patient selection for BSS. Patients and Methods: cT2 MIBC patients undergoing RC at our institution from 2014 to 2024 were reviewed. Preoperative staging modalities, demographics, and tumour and patient characteristics were assessed. Multivariable logistic regression was applied to explore the relative effect of confounders on any pathological upstaging from robot-assisted or open RC specimens. Subgroup analysis according to the local upstaging (>pT2) or nodal dissemination (pN+) was also performed. Results: N = 275 RCs were included (73.5% males, 26.5% females). Upstaging was documented in n = 141 (51%) cases. Of these, n = 125 (45.5%) were upstaged locally (>pT2) and n = 35 (23%) yielded pN+ disease. Preoperative parameters like gender, the number of TURBTs, previous BCG exposure, and concomitant CIS did not significantly influence the risk of any kind of upstaging (p > 0.05). At multivariable analysis, neoadjuvant chemotherapy (NAC) and multi-disciplinary team (MDT) discussion were found protective (odds ratio [OR]: 0.4, 95%CI 0.2–0.7, p = 0.001 and OR: 0.51, 95%CI 0.2–0.9, p = 0.01). Preoperative FDG-PET assessment yielded higher risk for later pN upstaging (OR: 1.8, 95%CI 1–3, p = 0.05). HG/G3 features at TURBT along with mixed/pure histology variants in RC specimens were the most relevant independent predictors for both any and pT upstaging (OR: 4.3, 95%CI 1–34, p = 0.04 and OR: 2.3, 95%CI 1.1–4.6, p = 0.02 for any upstaging and OR: 5.6, 95%CI 1.3–36, p = 0.02 and OR: 2.5, 95%CI 1.3–5, p = 0.01 for pT upstaging, respectively). Conclusions: In this study, over half of the patients undergoing RC for cT2 were upstaged at the final pathology. Therefore, adequate counselling and examining the non-conventional criteria for prognosis is mandatory in the contemporary era of bladder-preservation strategies. Full article

With the accelerated pace of human space exploration and the progress of other related researches, there is an increasingly urgent demand for space infrastructure, equipment, and diversified spacecraft construction for space missions, and how to efficiently, intelligently, and autonomously build corresponding facilities and equipment on orbit according to the functional requirements of different missions has become a great challenge in the field of space technology research. As an important means of automated manufacturing, the construction of on-orbit assembly systems centered on space robotics has become an emerging development trend. In view of its importance, space agencies and research institutes have successively proposed and developed a series of related programs. In order to comprehensively understand the progress of on-orbit assembly with space robots (OASR) and scientific problems involved, this paper investigates the current status of research and technological development in OASR. Firstly, the significance of OASR for space exploration and other space missions is analyzed. Secondly, the existing classification forms of on-orbit assembly are outlined and a classification idea is proposed from the point of view of the combination of space robot motion capability and assembly goals. Thirdly, the research and development status of OASR in the United States, Europe, Canada, Japan, and China is investigated. Then, based on a review of the literature on space robots to realize on-orbit assembly in space facilities, some of the key technologies involved are reviewed and discussed. Finally, this paper discusses and looks ahead to the future development trend and application prospect of the technology of OASR, reveals and explains the crucial position it occupies as well as the important role it can play in the process of human space exploration, and is expected to provide useful references for the in-depth research and development of future on-orbit assembly technology. Full article

This research investigates the impact of actuator faults on the formation control of multiple-wheeled mobile robots—a critical aspect in coordinating multi-robot systems for applications such as surveillance, exploration, and transportation. When a fault occurs in any of the robots, it can disrupt the formation and adversely affect the system’s performance, thereby compromising system efficiency and reliability. While numerous studies have focused on fault-tolerant control strategies to maintain formation integrity, there is a notable gap in the literature regarding the relationship between controller gains and settling time under varying degrees of actuator loss. In this paper, we develop a kinematic model of wheeled mobile robots and implement a leader–follower-based formation control strategy. Actuator faults are systematically introduced with varying levels of effectiveness (e.g., 80%, 60%, and 40% of full capacity) to observe their effects on formation maintenance. We generate data correlating controller gains with settling time under different actuator loss conditions and fit a polynomial curve to derive an equation describing this relationship. Comprehensive MATLAB simulations are conducted to evaluate the proposed methodology. The results demonstrate the influence of actuator faults on the formation control system and provide valuable insights into optimizing controller gains for improved fault tolerance. These findings contribute to the development of more robust multi-robot systems capable of maintaining formation and performance despite the presence of actuator failures. Full article

Forest fires endanger ecosystems and human life, making early detection crucial for effective prevention. Traditional detection methods are often inadequate due to large coverage areas and inherent limitations. However, drone technology combined with deep learning holds promise. This study investigates using small drones equipped with lightweight deep learning models to detect forest fires early. A high-quality dataset constructed through aerial image analysis supports robust model training. The proposed YOLO-UFS network, based on YOLOv5s, integrates enhancements such as the C3-MNV4 module, BiFPN, AF-IoU loss function, and NAM attention mechanism. These modifications achieve a 91.3% mAP on the self-built early forest fire dataset. Compared to the original model, YOLO-UFS improves accuracy by 3.8%, recall by 4.1%, and average accuracy by 3.2%, while reducing computational parameters by 74.7% and 78.3%. It outperforms other mainstream YOLO algorithms on drone platforms, balancing accuracy and real-time performance. In generalization experiments using public datasets, the model’s mAP0.5 increased from 85.2% to 86.3%, and mAP0.5:0.95 from 56.7% to 57.9%, with an overall mAP gain of 3.3%. The optimized model runs efficiently on the Jetson Nano platform with 258 GB of RAM, 7.4 MB of storage memory, and an average frame rate of 30 FPS. In this study, airborne visible light images are used to provide a low-cost and high-precision solution for the early detection of forest fires, so that low-computing UAVs can achieve the requirements of early detection, early mobilization, and early extinguishment. Future work will focus on multi-sensor data fusion and human–robot collaboration to further improve the accuracy and reliability of detection. Full article

In response to the demand for small-size, high-precision, and real-time target distance measurement in platforms such as autonomous vehicles and drones, this paper investigates the multi-focal bionic compound eye (MFBCE) and its associated distance measurement algorithm. MFBCE was designed to integrate multiple lenses with different focal lengths and a CMOS array. Based on this system, a multi-eye distance measurement algorithm based on target detection was proposed. The algorithm derives the application of binocular distance measurement on cameras with different focal lengths, overcoming the limitation of traditional binocular algorithms that only work with identical cameras. By utilizing the multi-scale information obtained from multiple lenses with different focal lengths, the ranging accuracy of the MFBCE is improved. The telephoto lenses, with their narrow field of view, are beneficial for capturing detailed target information, while wide-angle lenses, with their larger field of view, are useful for acquiring information about the target’s environment. Experiments using the least squares method for ranging targets at 100 cm yielded a mean absolute error (MAE) of 1.05, approximately one-half of the binocular distance measurement algorithm. The proposed MFBCE demonstrates significant potential for applications in near-range obstacle avoidance, robotic grasping, and assisted driving. Full article

Conventional visual SLAM systems often struggle with degraded pose estimation accuracy in dynamic environments due to the interference of moving objects and unstable feature tracking. To address this critical challenge, we present a groundbreaking enhancement to visual SLAM by introducing an innovative architecture that integrates advanced feature extraction and dynamic object filtering mechanisms. At the core of our approach lies a novel Multi-Scale Feature Consolidation (MSFConv) module, which we have developed to significantly boost the feature extraction capabilities of the YOLOv8 network. This module enables superior multi-scale feature representation, leading to significant improvements in object detection accuracy and robustness. Furthermore, we have developed a Dynamic Object Filtering Framework (DOFF) that seamlessly integrates with the ORB-SLAM3 architecture. By leveraging the Lucas-Kanade (LK) optical flow method, DOFF effectively distinguishes and removes dynamic feature points while preserving the integrity of static features. This ensures high-precision pose estimation in highly dynamic environments. Comprehensive experiments on the TUM RGB-D dataset validate the exceptional performance of our proposed method, demonstrating 93.34% and 94.43% improvements in pose estimation accuracy over the baseline ORB-SLAM3 in challenging dynamic sequences. These substantial improvements are achieved through the synergistic combination of enhanced feature extraction and precise dynamic object filtering. Our work represents a significant leap forward in visual SLAM technology, offering a robust solution to the long-standing problem of dynamic environment handling. The proposed innovations not only advance the state-of-the-art in SLAM research but also pave the way for more reliable real-world applications in robotics and autonomous systems. Full article

Nursing robots are designed to serve users, and their ability to interact with humans, as well as to make task-related decisions and decompositions based on such interactions, is a fundamental prerequisite for autonomous execution of nursing tasks. Large language models offer an effective approach to facilitating human–robot interaction. However, their global perspective can lead to confusion or reduced precision when coordinating the execution of tasks by a dual-arm robot, often generating execution sequences that are inconsistent with real-world conditions. To address this challenge, this study proposes a multi-agent framework, wherein each arm of the nursing robot is conceptualized as an independent agent. Through the application of geometric constraints, these agents maintain appropriate relative positional relationships and achieve coordinated collaboration via a large language model. This approach enhances the task planning capabilities of the robot and improves its efficiency in delivering nursing services. Full article

A partial Lagrangian method is proposed as an inverse dynamics analysis method for multi-link systems. This method, combined with automatic differentiation, allows for the derivation of equations of motion and analytical extraction of motion-induced torque components. We introduce the concept of motion propagation force to describe joint torque components generated by the motion of other joints. This concept aligns with existing notions such as interaction torque, while providing a novel analytical perspective. The effectiveness of the proposed method is confirmed through simulations using a three-DoF arm model, where motion propagation torques are visualized and validated. This method is useful for motion analysis and impedance control in complex robotic systems. Full article

Autonomous Networked Robot (ANR) systems feature multi-robot systems (MRSs) and wireless sensor networks (WSNs). These systems help to extend coverage, maximize efficiency in data routing, and provide practical and reliable task management, among others. This article presents the development and implementation of an IoT-based hybrid ANR system integrated with different cloud platforms. The system comprises two main components: the physical hybrid ANR, the simulation development environment (SDE) with hardware in the loop (HIL), and the necessary core interfaces. Both are integrated to facilitate system component development, simulation, testing, monitoring, and validation. The operational environment (local and/or distributed) of the designed system is divided into zones, and each zone comprises static IoT-based sensor nodes (SSNs) and a mobile robot with integrated onboard IoT-based sensor nodes (O-SSNs) called the mobile robot sensor node (MRSN). Global MRSNs (G-MRSNs) navigate spaces not covered by a zone. The mobile robots navigate within/around their designated spaces and to any of their SSNs. The SSNs and the O-SSN of each zone are supported by the ZigBee protocol, forming a WSN. The MRSNs and G-MRSNs communicate their collected data from different zones to the base station (BS) through the IoT base station gateway (IoT-BSG) using wireless serial protocol. The base station analyzes and visualizes the received data through GUIs and communicates data through the IoT/cloud using the Wi-Fi protocol. The developed system is demonstrated for event detection and surveillance. Experimental results of the implemented/simulated ANR system and HIL experiments validate the performance of the developed IoT-based hybrid architecture. Full article

This paper presents a visual localization system for underwater robots, aimed at achieving high-precision relative positioning and dynamic target tracking. A 3D AprilTag reference structure is constructed using a cubic configuration, and a high-resolution camera module is integrated into the AUV for real-time tag detection and pose decoding. By combining multi-face marker geometry with a fused state estimation strategy, the proposed method improves pose continuity and robustness during multi-tag transitions. To address pose estimation discontinuities caused by viewpoint changes and tag switching, we introduce a fusion-based observation-switching Kalman filter, which performs weighted integration of multiple tag observations based on relative distance, viewing angle, and detection confidence, ensuring smooth pose updates during tag transitions. The experimental results demonstrate that the system maintains stable pose estimation and trajectory continuity even under rapid viewpoint changes and frequent tag switches. These results validate the feasibility and applicability of the proposed method for underwater relative localization and tracking tasks. Full article

With the popularity of quadruped robots, the main challenge they must overcome is traversing unstructured environments. Current methods that allow modern robots to traverse challenging terrain are unsuitable for situations at the edge of robot performance, where torque limits and contact forces must be carefully considered. This paper will investigate a way of generating feasible center of mass (CoM) trajectories applicable in such cases. A feasible CoM trajectory is one that the robot can perform considering contact, torque, and reachability constraints. We improve the existing method for finding feasible CoM regions, yielding a thirty times speedup so that it can run under 1 ms. Based on that improvement, we introduce a new iterative CoM planner that sequentially solves prioritized constrained IK and computes feasible regions. That way, we guarantee the satisfaction of contact constraints, torque constraints, and reachability. The planned motion was performed using a whole-body controller. We tested the approach on high-fidelity simulation and on real Solo12 quadruped, achieving the control loop frequency of 1 kHz. The whole codebase has been disclosed on GitHub. Full article

With the increasing demand for efficiency and profitability in industrial applications, modularity offers significant advantages such as system reconfiguration, reduced acquisition costs, and enhanced versatility. However, achieving compatibility across multi-vendor modular systems remains a challenge, particularly in motion control. This study focuses on improving motion control and sensing compatibility and performance, partly using open-source tools to enhance performance in modular systems. In such systems, effective motion coordination between modules is crucial; without it, operations are constrained to sequential execution, limiting efficiency. This paper quantifies the performance benefits of collaborative motion compared to sequential motion in modular mechatronic systems for pick-and-place operations. The experimental validation, conducted on a robotic manipulator mounted on a linearly sliding platform, demonstrates a substantial improvement. The results show time savings of 36% to 52% and an approximate 35% reduction in energy consumption, highlighting the potential for improved productivity and sustainability in modular automation solutions. Full article