Search Results (20)

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs. Full article

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. Full article

SLAM (simultaneous localization and mapping) technology incorporating QR code navigation has been widely used in the mobile robotics industry. However, the particle kidnapping problem, positioning accuracy, and navigation time are still urgent issues to be solved. In this paper, a SLAM fused QR code navigation method is proposed and an improved adaptive Monte Carlo positioning algorithm is used to fuse the QR code information. Firstly, the generation and resampling methods of initialized particle swarms are improved to improve the robustness and weights of the swarms and to avoid the kidnapping problem. Secondly, the Gmapping scan data and the data generated by the improved AMCL algorithm are fused using the extended Kalman filter to improve the accuracy and stability of the state estimation. Finally, in terms of the positioning system, Gmapping is used to obtain QR code data as marker positions on static maps, and the improved adaptive Monte Carlo localization particle positioning algorithm is matched with a library of QR code templates, which corrects for offset distances and achieves precise point-to-point positioning under grey-valued raster maps. The experimental results show that the particles encountered with kidnapping can be quickly adjusted in position, with a 68.73% improvement in adjustment time, 64.27% improvement in navigation and positioning accuracy, and 42.81% reduction in positioning time. Full article

In this paper, bimetallic functionally graded structures were fabricated using wire and arc additive manufacturing (WAAM). The bimetallic walls were built by depositing Si-Bronze and high-strength low-alloy (HSLA) steel, successively. The microstructural evolution of the built structures, especially within the fusion zone between the dissimilar alloys, was investigated in relation to their mechanical properties. The built bimetallic walls showed a high level of integrity. An overall interface length of 9 mm was investigated for microstructural evolution, elemental mapping and microhardness measurements along the building direction. Microhardness profiles showed a gradual transition in hardness passing through the diffusion zone with no evidence for intermetallic compounds. Failure of the tensile specimens occurred at the Si-Bronze region, as expected. Bending tests confirmed good ductility of the joint between the dissimilar alloys. Direct shear test results proved a shear strength comparable to that of HSLA steel. The obtained results confirm that it is appropriate to fabricate HSLA steel/Si-Bronze FGMs using WAAM technology. Full article

The activity concentrations of ²²²Rn were measured in 53 public water supplies of underground (50) and surface (3) origin, and the relation of such with five geological units where these supplies are located, in the central Ecuadorian Andes, was also explored. These units supply drinking water to 10 cities, located between the 1500 and 3120 m.a.s.l. The experimental setup consisted of the RAD7 radon detector and the RAD H₂O degassing system. The ²²²Rn levels measured in groundwater ranged from 0.53 to 14.78 Bq/L while surface waters did not indicate detectable radon levels. The radon concentrations were below the parametric value of 100 Bq/L for water intended for human consumption, recommended by the European Atomic Energy Community (EURATOM) in its Directive 2013/51, and the alternative maximum contamination level (AMCL) of 150 Bq/L, proposed by the Environmental Protection Agency (EPA). The Pisayambo Volcanic unit, mapped as intermediate volcaniclastic to felsic deposits, presented a mean radon concentration higher than the other geological units and lithologies (9.58 ± 3.04 Bq/L). The Cunupogyo well (11.36 ± 0.48 Bq/L) presented a radon concentration more than 70% higher than the neighboring springs, which may be explained by its proximity to the Pallatanga geological fault. The maximum annual effective doses, by cities, due to the ingestion and inhalation of radon, ranged from 0.010 to 0.108 mSv and from 0.008 to 0.091 mSv, respectively; therefore, these waters do not represent a risk to the health of the population. In addition, a correlation was observed between the activity concentration of ²²²Rn and the activity concentration of the parent ²²⁶Ra in samples collected from some springs. Full article

Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag. Full article

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by non-structural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed approach, a robot can automatically correct errors caused by non-structural elements by updating a semantic building map reflecting the current state of the environment. Evaluations in kidnapped robot and traditional localization scenarios indicate that more accurate and robust pose estimation can be achieved with the map updating capability. Full article

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm. Full article

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements’ uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour. Full article

One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy. Full article

Simultaneous localization and mapping (SLAM) is a dual process responsible for the ability of a robotic vehicle to build a map of its surroundings and estimate its position on that map. This paper presents the novel concept of creating a 3D map based on the adaptive Monte-Carlo location (AMCL) and the extended Kalman filter (EKF). This approach is intended for inspection or rescue operations in a closed or isolated area where there is a risk to humans. The proposed solution uses particle filters together with data from on-board sensors to estimate the local position of the robot. Its global position is determined through the Rao–Blackwellized technique. The developed system was implemented on a wheeled mobile robot equipped with a sensing system consisting of a laser scanner (LIDAR) and an inertial measurement unit (IMU), and was tested in the real conditions of an underground mine. One of the contributions of this work is to propose a low-complexity and low-cost solution to real-time 3D-map creation. The conducted experimental trials confirmed that the performance of the three-dimensional mapping was characterized by high accuracy and usefulness for recognition and inspection tasks in an unknown industrial environment. Full article

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation. Full article

Map-based, high-precision dynamic pose tracking and rapid relocalization in the case of unknown poses are very important for indoor navigation robots. This paper aims to propose a robust and high-precision indoor robot positioning algorithm that combines vision and laser sensor information. This algorithm mainly includes two parts: initialization and real-time pose tracking. The initialization component is mainly to solve the problem of the uncertainty of a robot’s initial pose and loss of pose tracking. First, the laser information is added to the posenetLSTM neural network that only considers image information as a geometric constraint, and the loss function is redesigned thereby improving global positioning accuracy. Second, on the basis of visual rough positioning, the branch and bound method is used to quickly search the high-precision pose of the robot. In the real-time tracking component, small-scale correlation sampling is performed on the high-resolution environment grid map, and the robot’s pose is dynamically tracked in real time. When the score of the tracking pose is lower than a certain threshold, the method of nonlinear graph optimization is used to perform the pose optimization. In order to prove the robustness, high precision, and real-time performance of the algorithm, this article first builds a simulation environment in Gazebo for evaluation, and then verifies the relevant performance of the algorithm through the Mir robot platform. Both simulations and experiments show that the introduction of laser information into the neural network can greatly improve the accuracy of vision relocalization and the system can quickly perform high-precision repositioning when the camera is not severely blocked. At the same time, compared with the pose tracking performance of the adaptive Monte Carlo localization (AMCL) algorithm, the proposed algorithm has also improved in accuracy and in real-time performance. Full article

Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is suitable and easy for path planning, and the adaptive Monte Carlo localization (AMCL) method can realize localization in most of the rooms in indoor environments. However, the conventional method fails to locate the robot when there are similar and repeated geometric structures, like long corridors. To solve this problem, we present Text-MCL, a new method for robot localization based on text information and laser scan data. A coarse-to-fine localization paradigm is used for localization: firstly, we find the coarse place for global localization by finding text-level semantic information, and then get the fine local localization using the Monte Carlo localization (MCL) method based on laser data. Extensive experiments demonstrate that our approach improves the global localization speed and success rate to 96.2% with few particles. In addition, the mobile robot using our proposed approach can recover from robot kidnapping after a short movement, while conventional MCL methods converge to the wrong position. Full article

Uranium and radon concentrations in groundwater from the Goesan area of the Ogcheon Metamorphic Belt (OMB), central Korea, whose bedrock is known to contain the highest uranium levels in Korea, were analyzed from 200 wells. We also measured the uranium concentrations in the bedrock near the investigated wells to infer a relationship between the bedrock geology and the groundwater. The five geologic bedrock units in the Goesan area consist of Cretaceous granite (Kgr), Jurassic granite (Jgr) and three types of metasedimentary rocks (og1, og2, and og3). The percentages of the groundwater samples over 30 μg/L (maximum contaminant level, MCL of US EPA) were 2.0% of the 200 groundwater samples; 12% of Kgr and 1.8% of Jgr exceeded the MCL, respectively. Overall, 16.5% of the 200 groundwater samples exceeded 148 Bq/L (alternative maximum contaminant level, AMCL of US EPA); 60.0% of Kgr and 25.0% of Jgr exceeded the AMCL, but only 0% of og1, 7.9% of og2, and 2.6% of og3 exceeded the value, respectively. No direct correlation was found between uranium concentration and radon concentration in water samples. Radon has a slightly linear correlation with Na (0.31), Mg (−0.30), and F (0.36). However, uranium behavior in groundwater was independent of other components. Based on thermodynamic calculation, uranium chemical speciation was dominated by carbonate complexes, namely the Ca₂UO₂(CO₃)_3(aq) and CaUO₂(CO₃)₃²⁻ species. Although uraniferous mineral phases designated as saturation indices were greatly undersaturated, uranium hydroxides such as schoepite, UO₂(OH)₂ and U(OH)₃ became possible phases. Uranium-containing bedrock in OMB did not significantly affect radioactive levels in the groundwater, possibly due to adsorption effects related to organic matter and geochemical reduction. Nevertheless, oxidation prevention of uranium-containing bedrock needs to be systematically managed for monitoring the possible migration of uranium into groundwater. Full article