Search Results (95)

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

Heavy-duty industrial robots (HIRs) are essential for high-payload operations in the automotive, aerospace, and nuclear industries. However, existing reviews are often limited to specific domains or control methods. This paper provides a concise review of recent advances in HIRs from two perspectives: structural innovation and intelligent control. The review shows that structural design is evolving toward lightweight, robust, and maintainable architectures, while control strategies are increasingly shifting from conventional PID methods to adaptive, robust, and learning-based approaches to handle high inertia, nonlinear dynamics, and uncertainty. Representative applications, including friction stir welding and nuclear operations, are also summarized. Based on the reviewed literature, we identify several key challenges for future research, including structure–control co-design, energy-aware motion planning, robust autonomy in hazardous environments, safe human–robot collaboration, digital-twin-enabled lifecycle optimization, and interpretable fault diagnosis. These findings outline the research agenda for the next generation of HIRs. Full article

Sample return missions are among the most difficult tasks for robotic spacecraft in exploring our solar system. However, the samples they return to Earth have significantly high value for the planetary science community. Thus far, we have only acquired samples from the Moon, three asteroids, a comet’s tail, and the solar wind at the Earth–Sun Lagrange Points. The National Academy’s most recent decadal survey of planetary science at NASA emphasized the value of samples returned to Earth for analysis and called for NASA to prioritize samples returned from Mars, the Moon’s South Pole, a Jupiter-family comet, and Ceres. Currently available rockets and propulsion technology impose severe, and possibly insurmountable, limits to where we can send robot explorers and return samples within a reasonable timescale. Now, the advent of large new rockets offers the potential for very high C₃ (characteristic energy) Earth escape trajectories. Parallel developments in Nuclear Propulsion yield much higher I_SP than chemical propulsion and can operate far away from the Sun. Our novel trajectory modeling results and mission architecture analysis show that, by combining these technologies, sample return from across the solar system becomes feasible within the career lifetime of a planetary scientist. Full article

During the transition of global energy systems toward low-carbon and high-reliability operation, nuclear power plant (NPP) operation and maintenance require environmental perception methods that are safe, energy-efficient, and sufficiently accurate for confined and radiation-risk areas. To address these requirements, this paper proposes an energy-oriented multi-robot collaborative exploration and mapping framework, termed I-WFD-Gmapping-DT. The framework integrates a digital twin (DT) 5+3 model, improved wavefront frontier detection (I-WFD), energy- and risk-aware task allocation, EKF-AMCL-based initial relative pose estimation, and multi-scale Gmapping map fusion. Unlike conventional frontier-based or single-objective exploration methods, the proposed utility function jointly considers discounted information gain, obstacle-sensitive path cost, estimated battery energy, angular dispersion, and safety constraints. A ROS-Gazebo simulation of an NPP-like environment was used for 30 independent runs with randomized seeds and starting perturbations. Compared with WFD-Gmapping, the proposed method increased the three-robot coverage area percentage from 35.6 ± 2.1% to 40.5 ± 1.9%, reduced exploration time by 13.35%, reduced total and used frontier target points by 38.9% and 23.24%, respectively, and reduced estimated energy consumption by 13.9%. Map accuracy was also improved, with AE decreasing from 12.45% to 11.52%, RMSE from 7.85% to 7.18%, and SSIM increasing from 0.78 to 0.83. Additional sensitivity, ablation, runtime, and initial-pose experiments confirm the robustness of the parameter selection and the contribution of the DT-enabled feedback mechanism. The results show that I-WFD-Gmapping-DT can enhance collaborative inspection efficiency, reduce redundant motion and energy consumption, and provide reliable mapping support for intelligent NPP operation and maintenance. Full article

Path planning for inspection and maintenance robots in nuclear power plants often suffers from limited adaptability, high computational cost, and unstable convergence in obstacle-dense confined environments. To address these issues, this paper proposes an improved Bi-RRT–APF path optimization framework for complex industrial scenarios. The method integrates (1) a hybrid sampling strategy combining random, goal-biased, and potential-field-guided sampling to enhance global exploration and convergence efficiency; (2) a potential-field-guided perturbation and stagnation detection mechanism to improve escape capability from local minima; and (3) a dynamic target switching and constrained segmented connection strategy to improve path feasibility and safety. A digital twin-based simulation platform is further developed to validate the engineering applicability of the proposed approach. Simulation results demonstrate significant quantitative improvements over baseline methods. Compared with conventional RRT and Bi-RRT, the proposed method reduces iteration count by 65.3% and 43.8%, respectively, and decreases computation time by 76.1% and 48.4%, respectively, while increasing the success rate to 95% (from 82% and 93%) and improving path smoothness (reduced from 5.3 and 3.3 to 2.9). Compared with advanced variants (Quad-RRT and KB-RRT*), the method further reduces computation time by 25.2% and 10.3% and iteration count by 29.3% and 8.4%, respectively. These results indicate that the proposed method achieves a balanced improvement in efficiency, robustness, and path quality. This work provides an efficient and reliable solution for autonomous path planning of robots in complex nuclear power plant environments. Full article

Microelectromechanical systems are being increasingly deployed in nuclear industry robotics, where their great sensitivity and mechanically stable silicon structures enable reliable sensing in radiation-exposed environments. An ultra-thin silicon strain gauge without an oxide substrate layer designed for robotic electronic skin is evaluated under Co-60 γ irradiation, representative of nuclear decommissioning conditions. The sensor performance is evaluated based on electrical measurements conducted before and after irradiation, focusing on cumulative radiation-induced effects. The results show that silicon strain gauge signal maintains a high linearity (R² > 0.99) under strain. Across an accumulated dose range up to approximately 15 Gy, only minor variations are observed, including a resistance increase within 1.3% and a reduction in gauge factor within 5% for most specimens. The radiation-induced resistance increases and sensitivity degradation results in a maximum strain estimation error of approximately 22.5 με (≈3.5%) within the tested operating range below 700 με. Full article

This paper details the development of an autonomous robotic solution for the long-term surveillance of low-level radiation in nuclear facilities. Implementing such a system mitigates personnel health risks by minimizing radiation exposure and automating a mundane, repetitive task. To address the inherent challenges of deploying robots in highly unstructured environments, the core contribution of this work is a novel, error-tolerant behavioral architecture. Specifically, a custom behavior tree is designed to absorb execution imperfections and tolerate environmental uncertainties. This allows the robot to adapt and continue its mission rather than experiencing a hard failure. Bayesian optimization is utilized to perform adaptive mapping via a manipulator-equipped Spot quadruped robot, which features a Kromek Sigma50 gamma spectrometer attached to its end effector. Experiments were conducted in an obstacle-rich testbed using a Cesium-137 source. The results demonstrate the feasibility of the proposed system and its behavioral design approach, as the robot successfully performed adaptive mapping and correctly identified the location and approximate intensity of the radiation source. Full article

At the end of its last experimental campaign, in December 2023, the Joint European Torus (JET) became available for testing a compact and lightweight Laser-Induced Breakdown Spectroscopy (LIBS) system to be mounted on its robotic arm. The purpose of the test was the in situ chemical characterization of its internal walls and plasma-facing components (PFCs). Among the areas measured, special attention was devoted to the PFCs of the divertor, as this area is most affected by the re-deposition of material eroded from the first wall and unburned nuclear fuel (deuterium and tritium). In this article, we present the results of the LIBS characterization of a PFC of the High Field Gap Closure (HFGC), highly subjected to these phenomena. The in-depth distribution of several ITER-relevant chemical species is discussed through in-depth and correlation analyses, and the interpretation of the results is explained in terms of erosion and re-deposition of materials from the first wall. The study allowed us to estimate the thickness of the ablated layers by each laser shot, which is on the order of a few tens of nanometers, and to outline a mapping of the thickness of the re-deposited material. Full article

Motion and path planning are fundamental to intelligent robotic systems, enabling navigation. The objective is to generate collision-free trajectories in obstacle-rich configuration spaces (C-spaces) while meeting performance constraints. In environments with narrow passages planning becomes especially difficult, as feasible regions have low measure and are rarely reached by random sampling. Classical sampling-based planners are probabilistically complete but inefficient in such regions. Learning-based planners like MPNet offer fast inference but often produce infeasible paths in cluttered areas, requiring expensive postprocessing. To address this trade-off, we propose a hybrid framework that combines improved sampling, structural abstraction, and neural prediction. A modified bridge-test sampler applies directional perturbations and corridor checks to generate reliable narrow passage samples. These are clustered into a sparse set of representative bridge points, which serve as nodes in a global graph. At query time, a greedy heuristic search explores this graph, using a neural local segment generator to connect nodes. We validate the approach on 2D maze maps, 3D voxel environments, and a 12-DOF manipulator performing a plugging task inside a simulated nuclear steam generator. Across all tasks, our method significantly outperforms classical and learning-based baselines in terms of success rate and planning time in narrow-passage-dominated scenarios. The inclusion of the repair module, under relaxed assumptions, also allows the framework to retain a generalized form of probabilistic completeness. Full article

This work focuses on wire arc additive manufacturing for the rapid prototyping of shell-type parts such as sealed containers/capsules required in the manufacturing of metal components using hot isostatic pressing (HIP) of powder. The selected material was AISI 316L. The automatic generation step of robot trajectories from the CAD design of the part to be manufactured was addressed first. The mechanical and metallurgical properties of WAAM samples were then evaluated. Finally, a hollow cylindrical capsule manufactured by WAAM was used for the HIP sintering of powder to demonstrate the relevance of the hybrid technology. The main results are as follows: 1. The Ultimate Tensile Strength (UTS) of AISI 316L WAAM samples was measured be-19 tween 540 MPa (longitudinal direction) and 600 MPa (transverse direction). 2. The as-manufactured WAAM parts present a residual (δ) ferrite content of 5–7%. 3. HIP processing permitted to reset a fully austenitic structure within the WAAM wall/shell. 4. The grain size was found to be coarser in the WAAM walls and finer in the core of the part (made of sintered powder). Finally, the suggested hybrid process may become an alternative technology for the manufacture of medium-size metal components in the nuclear industry. Full article

In scenarios involving radiation such as decommissioning of nuclear disasters and operating nuclear power plants, it is necessary to perform tasks including maintenance, demolition, and inspection using robots in order to protect human workers from harm. LIDAR (LIght Detection And Ranging) sensors are used for many demanding real-time tasks in robotics such as obstacle avoidance, localisation, mapping, and navigation. Standard silicon-based electronics including LIDAR fail quickly in gamma radiation, however, high-radiation areas have a critical need for robotic maintenance to keep people safe. Sensors need to be developed, which can cope with this environment. A prototype including most required transmitter and receiver circuits is designed utilising components expected to provide up to (1 MGy) gamma radiation tolerance. Initial results testing the concepts of the laser transmission and detection in a lab environment shows reliable signal detection. Performance tests utilising multiple receivers show a linear relationship between receiver separation and measured time difference, allowing for the possibility of calibration of a sensor using the time difference between pulses. Future work (such as radiation testing trials) is discussed and defined. These results contribute to de-risking the feasibility of long-term deployment of LIDAR systems utilising these approaches into environments with high gamma dose rates, such as nuclear fission decommissioning, big science facilities such as the Large Hadron Collider, and remote maintenance systems used in future nuclear fusion power plants such as STEP and EU-DEMO. Full article

Regular ultrasonic full-coverage inspection of reactor pressure vessels (RPVs) is critical to ensuring the safe operation of nuclear power plants. However, due to the extreme operating conditions and complex internal geometry of RPVs, most existing inspection technologies face significant challenges in achieving convenient and efficient full-coverage traversal detection. To address these limitations, this study proposes a novel nondestructive inspection robot equipped with four symmetrically arranged inspection arms for comprehensive RPV ultrasonic inspection. By considering the structural symmetry and motion characteristics of the inspection arms, a corresponding kinematic analysis is conducted, resulting in a precise kinematic model that enables real-time computation of both forward and inverse kinematic solutions with high accuracy. Furthermore, an adaptive full-coverage inspection method is developed by leveraging the vessel’s axisymmetric geometry and by partitioning the RPV into seven distinct detection zones, allowing the four inspection arms to independently complete inspections across the maximum number of zones, thereby significantly enhancing both detection coverage and operational efficiency. Experiments demonstrated the practical feasibility of the proposed robotic system and validated the effectiveness of the full-coverage inspection method. Full article

In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically automate such processes, an adaptive online fault diagnosis framework is required, which consecutively selects variables to measure and updates its inference with additional information at each measurement step. In this paper, adaptive online fault detection models—grafted composite decision trees—are proposed for such a framework. While conventional decision trees themselves can serve two required objectives of the framework, information from monitored variables can be less utilized because decision trees do not consider if required input variables are always monitored when the models are trained. On the other hand, the proposed grafted composite decision tree models are designed to fully utilize both monitored and robot-measured variables at any stage in a given measurement sequence by grafting two types of trees together: a prior-tree trained only with observed variables and sub-trees trained with robot-measurable variables. The proposed method was validated on a cooling water system in a nuclear power plant with multiple leak scenarios, in which improved measurement selection and increase in inference confidence in each measurement step are demonstrated. The performance comparison between the proposed models and the conventional decision tree model clearly illustrates how the acquired information is fully utilized for the best inference while providing the best choice of the next variable to measure, maximizing information gain at the same time. Full article

In this paper, an underwater salvage robot is developed to retrieve foreign objects scattered in nuclear reactor pools. The robot mainly consists of an ROV platform and a 3-DOF Delta robotic arm. Utilizing fused IMU and LED beacon visual data for localization, it achieves pool traversal via six dynamically controlled thrusters. An improved YOLOv8s algorithm is employed to identify foreign objects in underwater environments. During traversal, the robot identifies and retrieves foreign objects along the way. The prototype of the robot was subjected to a series of experiments in an indoor pool. Results show that the improved YOLOv8 algorithm achieves 92.2% mAP, surpassing the original YOLOv8s and Faster-RCNN by 3.7 and 3.3 percentage points, respectively. The robot achieved a foreign-object identification rate of 95.42% and a retrieval success rate of 90.64% under dynamic traversal conditions, indicating that it meets the operational requirements and has significant engineering application value. Full article

During the operation of steel cable-driven radiation-resistant robots in nuclear industrial environments, the tensile force of a steel cable is influenced by temperature variations, which can cause significant detection errors. To address this problem, this study proposes a temperature-compensated axial force characterization method for steel cables based on the magnetoelastic effect, aiming to ensure the measurement accuracy of magnetoelastic sensors. The principle of the magnetoelastic measurement method involves magnetizing the steel cable. When subjected to tensile forces, the magnetization characteristics of the steel cable change, thereby altering the detection signal of the magnetoelastic sensor. By analyzing the relationship between steel cable tension and variations in the detection signal, effective force measurement can be achieved. First, experiments are conducted to investigate the influence of temperature on the detection signals of a magnetoelastic sensor under zero-load conditions. Then, additional tests are performed to examine the combined effects of a tensile force and temperature on the sensor’s signals. Finally, based on the experimental data, axial force prediction models are constructed using both surface fitting and a backpropagation neural network (BPNN). The results demonstrate that, compared to the resistance values, inductance exhibits superior stability under temperature variations. In the temperature range of 20–50 °C, the inductance variation is approximately 0.15 μH, which indicates improved suitability for characterizing the axial force of steel cables. It is also shown that under isothermal conditions, the inductance increases linearly with the applied tensile force, exhibiting a slope of approximately 0.025 μH/kN. Both the surface fitting-based and BPNN-based axial force prediction models demonstrate high accuracy, with absolute prediction errors consistently below 5% compared to actual data. Full article