Search Results (1,743)

RV reducers are vital components in industrial robots and precision equipment, where the fatigue life of the crank arm and support bearings critically influences the overall system longevity. This study presents a comprehensive performance evaluation, with a specific focus on contact mechanics and wear analysis of these critical bearings. A theoretical mathematical model for force analysis is established based on static mechanics, which is further extended to incorporate wear depth prediction based on contact pressure and sliding velocity. To validate this model and investigate bearing behavior in detail, a high-fidelity parametric simulation model is developed using MASTA software. The simulation results, encompassing contact stress, shear stress, and wear patterns, demonstrate good correlation with the predictions from the theoretical mathematical model, effectively verifying its accuracy for performance and life assessment. The systematic analysis confirms that both the investigated tapered roller and needle roller bearings meet the design requirements. This integrated approach of theoretical modeling, which includes wear analysis, and software simulation provides a reliable methodology for assessing bearing performance and fatigue life, offering significant value for the design optimization and reliability enhancement of RV reducers. Full article

To better address the operational requirements for emergency underwater ship maintenance, this study proposes the use of an underwater robotic arm instead of divers for cleaning submerged hull sections. Experimental analyses are conducted to validate the stability and feasibility of the constructed underwater robotic arm cleaning system. Initially, hydrodynamic analysis of the robotic arm was performed using the Morison equation. Through fluent dynamic simulations, the hydrodynamic moments on each robotic arm during cleaning operations were obtained, confirming that stress under typical seawater flow velocities remained within the rated limits. Subsequently, dynamic simulations were carried out to determine the joint driving torques in a fluid environment, quantify the influence of the hydrodynamic resistance on the joint torque, and verify the accuracy of the fluid dynamics model. Finally, motion control and underwater cleaning experiments were implemented on the system. Experimental results further corroborated the correctness of the fluid model and operational environment analysis, demonstrating the expected cleaning performance and providing both data and experimental support for practical underwater maintenance during long-distance ship voyages. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control. Full article

Soft actuators provide a wide range of motion capabilities, allowing for the advancement of novel mobile robots. However, soft actuators that possess the capability required to achieve three-dimensional movement are limited. In addition, the presence of air supply tubes poses a challenge to utilizing pneumatic actuators as mobile robot components. This study presents a long inflatable actuator with a novel structure in which air supply tubes are arranged within its body. This structure enables the extension of the inflatable tube with minimal deformation. The proposed actuator comprises an inflatable tube and a spool mechanism. The length of the actuator is controlled by a motor. The performance of the actuator was evaluated experimentally, validating its alignment with our proposed models. The results showed that the proposed actuator exerted extension and contraction forces of 28 N and 87 N, respectively. Furthermore, the proposed actuator can be equipped with a gripper at its tip, enhancing its functionality. In a demonstration, this gripper-equipped actuator successfully extended to grasp a bar at a height of 1.3 m and contracted while lifting a 1.0 kg base. This demonstration indicated that the proposed actuator could provide the required arm motions of a bi-arm climbing robot. Full article

The growing demand for noise reduction in multi-joint long-reach robotic arms necessitates the development of precise noise measurement methodologies. However, accurate characterization remains challenging due to the robot’s complex kinematics. Specifically, dynamic joint positions and motion trajectories can lead to acoustic occlusion, while the inherent directivity of sound sources further compromises measurement reliability. To address these issues, this study proposes a component-based hybrid measurement approach. First, the noise generated by a single joint was characterized using a simplified 4-point method, with Green’s function applied to correct for variable propagation distances. Subsequently, the total sound power level of the entire robotic arm was synthesized in a virtual environment by integrating the single-joint acoustic data with the arm’s operational kinematic program. Validation results demonstrate that the proposed method achieves a measurement error of only 0.8 dB relative to the reverberation chamber benchmark—an accuracy superior to that of direct measurements of the full robotic arm cycle (2.6 dB). Furthermore, a comparison with the ISO 3744:2025 9-point standard method reveals that while the proposed 4-point approach yields a slightly larger error (0.8 dB vs. 0.2 dB), it significantly reduces experimental complexity. Consequently, this method offers a sufficiently accurate and operationally efficient solution for practical engineering applications. Full article

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight. Full article

Trajectory optimization of manipulators is crucial for achieving efficient, low-energy-consumption, and stable operation. The standard Chicken Swarm Optimization (CSO) algorithm and its variants tend to fall into local optima during optimization, making it difficult to obtain the optimal trajectory. Therefore, this paper employs multiple strategies to collaboratively improve the chicken swarm optimization algorithm: Tent chaotic mapping is used for population initialization, the position update strategies of the three populations are respectively reconstructed to expand the search scope of the population, and a genetic evolution strategy is introduced to escape from local optimal solutions. Simulation outcomes based on the CEC2022 benchmark functions show that the Multi-strategy Improved Chicken Swarm Optimization (MICSO) algorithm outperforms commonly used optimization algorithms at present in both convergence speed and solution accuracy. The MICSO is applied to the 3-5-3 polynomial interpolation trajectory planning of manipulators, and a comprehensive optimal objective function is constructed by combining time, energy consumption, and jerk. Simulation and experimental results demonstrate that the MICSO algorithm can flexibly adjust the optimization tendency according to practical requirements. The optimized trajectory effectively reduces running time, energy consumption, and joint impact. Full article

Background and Objectives: Learning curves robotic arm-assisted total knee arthroplasty (TKA) are well-documented for semi-active systems, but evidence for advanced fully active robotic systems remains scarce. This study aimed to characterize the learning curve for operative time, implant positioning, and lower-limb alignment using a fully active robotic TKA system, specifically accounting for the impact of rotating resident involvement in a tertiary center. Materials and Methods: Sixty consecutive primary TKAs were performed using the advanced active robotic system (CUVIS-Joint^®). The learning curve for operative time was evaluated using cumulative summation (CUSUM) analysis. To identify independent predictors of surgical duration and radiographic precision, a multivariate linear regression model was constructed, including case number, implant type, and resident rotation period as variables. Results: CUSUM analysis identified a statistically significant inflection point at the 39th case. Beyond this point, mean operative time decreased approximately 20 min (133.3 ± 13.5 vs. 113.8 ± 7.9 min, p < 0.001). Multivariate regression confirmed that case number was the sole independent predictor of operative time (p < 0.001). Notably, implant positioning and lower-limb alignment showed no detectable difference across the sequential cases (p > 0.05), maintaining high precision from the outset. Conclusions: Active robotic TKA demonstrated a learning curve for operative time that stabilized after 39 cases within a clinical setting of rotational resident participation. Radiographic accuracy remained consistent despite these educational requirements, supporting the technical feasibility and reliability of this advanced system for the management of end-stage knee osteoarthritis Full article

The widespread deployment of service robots in domestic and professional environments demands structural solutions that simultaneously achieve high stiffness, low mass, and intrinsic safety. Traditional metallic structural designs face a fundamental physical conflict: achieving high stiffness typically results in excessive mass, which compromises operational safety and battery life. To solve this, this paper presents a critical review of an integrated lightweighting strategy combining material selection, structural design, and additive manufacturing for Carbon-Fiber-Reinforced Polymer (CFRP) service robot structures. Three critical findings are presented. First, specific stiffness is established as the governing criterion for material selection, providing a unified basis to resolve the stiffness–mass conflict. Second, among current 3D printing techniques, Fused Deposition Modeling (FDM) with continuous-fiber reinforcement overcomes the geometric constraints of traditional molding, enabling the fabrication of complex, customized structures. Third, to realize the full potential of 3D-printed CFRP, we highlight the importance of integrating material properties (anisotropy), structural design (topology optimization), and manufacturing processes (path planning) into a concurrent framework. This integrated approach is validated through a collaborative robotic-arm case study, achieving a 30% reduction in structural mass. Full article

Multi-joint hydraulic robotic arms are core equipment in intelligent mining, yet their performance is often limited by strong dynamic coupling and nonlinear hydraulic effects. Traditional control methods struggle to achieve high-precision trajectory tracking and coordinated motion under high loads and flow-coupling constraints. To address these challenges, this paper establishes a coupled hydraulic–mechanical dynamic model for a multi-joint robotic arm. The mechanical dynamics are derived using the Lagrangian formulation, while the hydraulic dynamics account for flow coupling among cylinders. An improved deviation coupling control (IDCC) strategy is proposed, integrating feedforward–feedback compensation, coupling error regulation, and a flow-limiting correction term. Co-simulation in Simulink (2024b) and Amesim (2020) shows that under flow-saturation conditions, the improved strategy reduces the peak trajectory errors by approximately 47.88%, 28.08%, and 49.89% for Joints 1–3, respectively, and shortens the settling time by 27.93%. Experimental results from a three-joint hydraulic test platform confirm error reductions of 10.20–15.58% and a 31.50% decrease in overall adjustment time. The study demonstrates that the proposed control strategy effectively suppresses multi-joint coupling interferences, enhances trajectory tracking accuracy, and improves the adaptability of hydraulic robotic arms under flow-limited conditions, providing a viable solution for high-precision control in intelligent mining applications. Full article

In agricultural automation, trajectory planning for fruit-picking robot arms must satisfy dynamic obstacle avoidance and real-time control constraints in complex orchards, forming a high-dimensional, constrained optimization problem. Due to strong nonlinearity and steep gradients, traditional planners often yield high-cost trajectories with unstable quality. This paper introduces a Reinforced Arctic Puffin Optimization (RAPO) algorithm for trajectory planning in high-dimensional, complex, constrained scenarios. RAPO improves Arctic Puffin Optimization (APO), which uses a two-stage foraging strategy but may suffer premature convergence, insufficient population diversity, and weak boundary handling. Dynamic fitness–distance balance (DFDB) adaptively coordinates exploration and exploitation. An elite-pool dynamic search strategy (DEPSS) combines t-distribution perturbation and Lévy flight to maintain diversity and enhance exploitation. A convex-lens opposition-learning boundary control method (CLOBC) improves out-of-bounds handling and reduces invalid search. Stochastic centroid opposition learning (SOBL) further suppresses premature convergence and expands coverage. On the CEC2017 benchmark (30/50/100 dimensions), RAPO outperforms nine algorithms in convergence speed and solution quality, verified by Wilcoxon and Friedman tests. In dense, narrow, and dynamic obstacle scenarios, RAPO achieves the lowest path cost, converges within 30 iterations, reduces variance, and generates smoother trajectories. This case study demonstrates RAPO’s robust mathematical performance, providing a robust and efficient framework for agricultural picking robots. Full article

Electrochemical machining (ECM) offers precise shaping by material dissolution with negligible mechanical or thermal impact on the workpiece. Metal parts with three-dimensional shapes, such as freeform surfaces or additively manufactured parts, can be addressed by robots with up to six degrees of freedom without significant mechanical impacts on the end-effectors and robots. This study summarizes the state-of-the-art of the use of robots in ECM by assessing the relevant literature. Several investigations were found that implemented or conceptualized the use of robotic arms in ECM sinking, jet-ECM or wire ECM, mainly for effective utilization of the processes. This study includes results of pure ECM, as well as hybrid ECM processes and the use of robots considering their accuracy, degrees of freedom and their application potential. Special emphasis is given to the role of robots in improving machining accessibility and their usability for valuable components in the aerospace, biomedical, and tooling industries. Furthermore, the review provides insights into electrolyte delivery mechanisms and pump configurations that facilitate efficient process performance. Overall, the utilization of robots in ECM not only enhances the process flexibility and surface quality but also aligns well with the aim of intelligent, automated, and high-precision manufacturing. Full article

High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots. Full article

Tunnel lighting cleaning is of significant practical importance for improving driving safety. To address the low operational efficiency of tunnel lighting cleaning tasks, a trajectory planning method based on the chaotic adaptive whale–particle swarm optimization (CAW-PSO) algorithm is proposed. Taking the SIASUN GCR16-2000 robotic arm as the research object, the trajectory is constructed using a 3-5-3 polynomial interpolation, with the objective of achieving time-optimal trajectory planning. In the CAW-PSO algorithm, a tent chaotic map is introduced to improve the quality of the population; a linearly decreasing inertia weight is designed to strike a balance between local and global search; dynamic learning factors are defined to strengthen the individual learning ability and global cognitive capability of particles; finally, the exploitation mechanism of the whale optimization algorithm is employed to avoid getting trapped in local optima and improve convergence accuracy. The simulation time is 3.661 s, a reduction of 69.94%. The experimental results yielded a mean relative error of 1.16%, indicating good agreement with the simulation results. The results of the simulation and experiment indicate that the CAW-PSO effectively reduces the motion time of the robotic arm, exhibiting superior applicability in trajectory planning for tunnel lighting cleaning robotic arms. Full article