Search Results (2,663)

Background/Objectives: For surgical candidates with metastatic renal cell carcinoma with a tumor thrombus (mRCC-TT), surgery is cytoreductive nephrectomy with tumor thrombectomy (CN-TT). This is carried out through an open (OCN-TT), laparoscopic (LCN-TT), or robotic (RCN-TT) approach. The purpose of this study was to compare survival outcomes to CN-TT by operative approach. Methods: This was a retrospective analysis of all patients with a diagnosis of mRCC-TT, who underwent CN-TT from a multi-institutional database from 1999–2024. Metastatic locations were qualified as either lung, bone, brain, liver, retroperitoneum, adrenal, paraaortic nodes, or other nodes. Progression was defined as radiographic evidence of recurrence or metastasis not seen on imaging prior to CN-TT. Progression locations were all metastatic locales previously noted plus the nephrectomy bed. Overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) were calculated. Comparisons were performed between OCN-TT, LCN-TT, and RCN-TT. Results: A total of 131 patients were included in the analysis (97 OCN-TT, 25 LCN-TT, and 9 RCN-TT). The TT level was not different (p-value > 0.05) by approach (p-value > 0.05). Preoperative tumor size, final pathologic tumor subtype, and postoperative tumor size were equivalent between the three surgical approaches (p-value > 0.05). Rates of progression were equivalent as were all locations of disease progression in the study (p-value > 0.05). Median OS was 1.6 years in OCN-TT, 1.5 years in LCN-TT, and 2.5 years in RCN-TT (p-value = 0.42). Median CSS was 2.1 years in OCN-TT, 3 years in LCN-TT, and 2.5 years in RCN-TT (p-value = 0.86). PFS was 0.8 years in OCN-TT, 1.2 years in LCN-TT, and 1.2 years in RNC-TT (p-value = 0.76). Conclusions: The operative approach does not affect survival outcomes for CN-TT. Surgeon comfort and patient preference should weigh heavily in operative decision making. Full article

Simultaneous Localization and Mapping (SLAM) utilizes sensor data to concurrently construct environmental maps and estimate its own position, finding wide application in scenarios like robotic navigation and augmented reality. SLAM systems based on 3D Gaussian Splatting (3DGS) have garnered significant attention due to their real-time, high-fidelity rendering capabilities. However, in real-world environments containing dynamic objects, existing 3DGS-SLAM methods often suffer from mapping errors and tracking drift due to dynamic interference. To address this challenge, this paper proposes BDGS-SLAM—a Bayesian Dynamic Gaussian Splatting SLAM framework specifically designed for dynamic environments. During the tracking phase, the system integrates semantic detection results from YOLOv5 to build a dynamic prior probability model based on Bayesian filtering, enabling accurate identification of dynamic Gaussians. In the mapping phase, a multi-view probabilistic update mechanism is employed, which aggregates historical observation information from co-visible keyframes. By introducing an exponential decay factor to dynamically adjust weights, this mechanism effectively restores static Gaussians that were mistakenly culled. Furthermore, an adaptive dynamic Gaussian optimization strategy is proposed. This strategy applies penalizing constraints to suppress the negative impact of dynamic Gaussians on rendering while avoiding the erroneous removal of static Gaussians and ensuring the integrity of critical scene information. Experimental results demonstrate that, compared to baseline methods, BDGS-SLAM achieves comparable tracking accuracy while generating fewer artifacts in rendered results and realizing higher-fidelity scene reconstruction. Full article

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture. Full article

Exoskeleton robots have been widely applied in military, industrial, and rehabilitation fields, with their practical effectiveness substantially reliant on a comprehensive performance evaluation framework. This paper reviews the prevalent testing methods for exoskeleton robots, including electromyography (EMG), motion capture, human–machine interaction forces, energy consumption monitoring, and both subjective and objective assessments. Through the systematic integration and comparison of these methodologies, this study establishes a methodological foundation for the comprehensive evaluation of performance and provides a theoretical basis for the development of standardized evaluation frameworks in the future. Furthermore, by systematically comparing and integrating these methodologies, this study aims to establish a methodological foundation for the future development of a standardized, multi-dimensional evaluation framework, which is essential for translating exoskeleton technology from laboratory research to practical applications. Full article

This paper presents a simulation-driven framework employing an Active Transfer Learning Gaussian Process (ATGP) model for accurate trajectory prediction and reliability analysis of the UR5 robotic manipulator. The method integrates transfer learning, Gaussian Process Regression, and active sampling to address challenges under limited target data. Preprocessing steps such as outlier removal, feature scaling, and Principal Component Analysis enhance data quality. A physically informed synthetic source domain facilitates effective knowledge transfer. Using DH-parameters as input, the ATGP predicts 3D end-effector trajectories over time. Results show a mean absolute error below 0.01, demonstrating consistency and scalability for real-time, uncertainty-aware robotic applications. This is the first ATGP-based UR5 framework that unites PCA-guided, physics-informed source synthesis with multi-output transfer GPR as well as coordinate- and time-resolved reliability analysis under scarce target-domain data. Full article

To address the issues of signal loss and insufficient accuracy of traditional GNSS (Global Navigation Satellite System) navigation in agricultural machinery sheds and farm access road environments, this paper proposes a high-precision mapping method for such complex environments and a real-time localization system for agricultural vehicles. First, an autonomous navigation system was developed by integrating multi-sensor data from LiDAR (Light Laser Detection and Ranging), GNSS, and IMU (Inertial Measurement Unit), with functional modules for mapping, localization, planning, and control implemented within the ROS (Robot Operating System) framework. Second, an improved LeGO-LOAM algorithm is introduced for constructing maps of machinery sheds and farm access roads. The mapping accuracy is enhanced through reflectivity filtering, ground constraint optimization, and ScanContext-based loop closure detection. Finally, a localization method combining NDT (Normal Distribution Transform), IMU, and a UKF (Unscented Kalman Filter) is proposed for tracked grain transport vehicles. The UKF and IMU measurements are used to predict the vehicle state, while the NDT algorithm provides pose estimates for state update, yielding a fused and more accurate pose estimate. Experimental results demonstrate that the proposed mapping method reduces APE (absolute pose error) by 79.99% and 49.04% in the machinery sheds and farm access roads environments, respectively, indicating a significant improvement over conventional methods. The real-time localization module achieves an average processing time of 26.49 ms with an average error of 3.97 cm, enhancing localization accuracy without compromising output frequency. This study provides technical support for fully autonomous operation of agricultural machinery. Full article

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies. Full article

Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article

As a pioneering solution for precision agriculture, unmanned, robotics-centred greenhouse farms have become a key technological pathway for intelligent upgrades. The robotic arm is the core unit responsible for achieving full automation, and the level of technological development of this unit directly affects the productivity and intelligence of these farms. This review aims to systematically analyze the current applications, challenges, and future trends of robotic arms and their key technologies within unmanned greenhouse. The paper systematically classifies and compares the common types of robotic arms and their mobile platforms used in greenhouses. It provides an in-depth exploration of the core technologies that support efficient manipulator operation, focusing on the design evolution of end-effectors and the perception algorithms for plants and fruit. Furthermore, it elaborates on the framework for integrating individual robots into collaborative systems analyzing typical application cases in areas such as plant protection and fruit and vegetable harvesting. The review concludes that greenhouse robotic arm technology is undergoing a profound transformation evolving from single-function automation towards system-level intelligent integration. Finally, it discusses the future development directions highlighting the importance of multi-robot systems, swarm intelligence, and air-ground collaborative frameworks incorporating unmanned aerial vehicles (UAVs) in overcoming current limitations and achieving fully autonomous greenhouses. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

This paper presents a dynamic modeling approach for a 3-phase BLDC motor used in a differential-drive serving robot using bond graph (BG) methodology. Designed for structured indoor environments, the serving robot incorporates mechanical, electrical, and control components that require an integrated modeling strategy. Traditional methods often fall short in handling the multi-domain nature of such systems. Bond graphs, with their energy-based modeling capability, offer a unified framework for capturing electromechanical dynamics and physical interactions. This work develops a complete bond graph model of a three-phase BLDC motor-driven robot, simulates its performance under typical operating conditions, and validates the model through current, torque, EMF, and velocity responses. The results demonstrate the model’s effectiveness in reflecting real-world robot behavior, supporting future design optimization and control development. Full article

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-robot formation motion, a path-deformation mapping mechanism is proposed, which deeply integrates artificial potential field and affine transformation, and drives formation adaptive adjustment in real time through path information. By using the non-uniform scaling characteristics of the affine transformation, the limitation of traditional conformal transformation is broken through, and the unity of flexibility and integrity is realized. The effectiveness of the algorithm is verified by experiments, which provide a practical solution for cooperative obstacle avoidance of multi-robot systems in complex environments. In order to verify the performance of the algorithm, a numerical simulation is carried out, and an experimental platform composed of seven omnidirectional mobile robots is built for physical verification. The simulation and experimental results show that the formation can complete the obstacle avoidance task in the complex static obstacle environment, and the average formation tracking error is maintained below 0.05 m. Compared with the traditional local obstacle avoidance or formation switching method, this algorithm significantly improves the fluency of the obstacle avoidance process and the integrity of the formation while ensuring a success rate of 100% obstacle avoidance. Full article

To address the challenges in concrete vibration during the construction of concrete-faced rockfill dams, this study proposes a multi-objective-driven lightweight and high-frequency vibrating robotic arm (VRA). The proposed system aims to improve adaptability and performance under harsh site conditions, such as inclined slab surfaces and confined rebar layouts. Based on the geometric structure and task characteristics of the VRA, a multi-objective topology optimization framework was established, integrating compromise programming and average frequency strategies. This method simultaneously achieves mass reduction, stiffness enhancement, and modal frequency improvement to avoid resonance during high-frequency operations. The workspace of the VRA was verified using kinematic modeling and Monte Carlo sampling, and a critical physical posture—where the arm is fully extended horizontally, producing maximum span and joint loads—was identified to extract dynamic load boundaries. Finite element analysis was then conducted under worst-case conditions, and the optimization results were validated by modal analysis and flexibility metrics. The optimized VRA demonstrated substantial improvements in structural performance, reducing overall mass, lowering flexibility, and increasing modal frequencies. The proposed framework provides a transferable approach for designing high-frequency robotic arms in vibration-intensive scenarios, supporting intelligent construction in concrete-faced rockfill dams and similar complex environments. Full article

As artificial intelligence continues to evolve, reinforcement learning (RL) has shown remarkable potential for solving complex sequential decision problems and is now applied in diverse areas, including robotics, autonomous vehicles, and financial analytics. Among the various RL paradigms, multi-agent reinforcement learning (MARL) stands out for its ability to manage cooperative and competitive interactions within multi-entity systems. However, mainstream MARL algorithms still face critical challenges in training stability and policy generalization due to factors such as environmental non-stationarity, policy coupling, and inefficient sample utilization. To mitigate these limitations, this study introduces an enhanced algorithm named MATD3_AHD, developed by extending the MATD3 framework, which integrates TD3 and MADDPG principles. The goal is to improve the learning efficiency and overall policy effectiveness of agents operating in complex environments. The proposed method incorporates three key mechanisms: (1) an Adaptive Exploration Policy (AEP), which dynamically adjusts the perturbation magnitude based on TD error to improve both exploration capability and training stability; (2) a Hierarchical Sampling Policy (HSP), which enhances experience utilization through sample clustering and prioritized replay; and (3) a Dynamic Delayed Update (DDU), which adaptively modulates the actor update frequency based on critic network errors, thereby accelerating convergence and improving policy stability. Experiments conducted on multiple benchmark tasks within the Multi-Agent Particle Environment (MPE) demonstrate the superior performance of MATD3_AHD compared to baseline methods such as MADDPG and MATD3. The proposed MATD3_AHD algorithm outperforms baseline methods—by an average of 5% over MATD3 and 20% over MADDPG—achieving faster convergence, higher rewards, and more stable policy learning, thereby confirming its robustness and generalization capability. Full article

The quality of underwater imagery is inherently degraded by light absorption and scattering, a challenge that severely limits its application in critical domains such as marine robotics and archeology. While existing enhancement methods, including recent hybrid models, attempt to address this, they often struggle to restore fine-grained details without introducing visual artifacts. To overcome this limitation, this work introduces a novel hybrid U-Net-Transformer (UTR) architecture that synergizes local feature extraction with global context modeling. The core innovation is a Recurrent Multi-Scale Feature Modulation (R-MSFM) mechanism, which, unlike prior recurrent refinement techniques, employs a gated modulation strategy across multiple feature scales within the decoder to iteratively refine textural and structural details with high fidelity. This approach effectively preserves spatial information during upsampling. Extensive experiments demonstrate the superiority of the proposed method. On the EUVP dataset, UTR achieves a PSNR of 28.347 dB, a significant gain of +3.947 dB over the state-of-the-art UWFormer. Moreover, it attains a top-ranking UIQM score of 3.059 on the UIEB dataset, underscoring its robustness. The results confirm that UTR provides a computationally efficient and highly effective solution for underwater image enhancement. Full article