Search Results (611)

As the application of mobile robots becomes increasingly widespread, many navigation applications now require more than just obstacle avoidance capabilities. There is a growing demand for robots to follow predetermined global paths. Inspired by stamping technology in the metalworking field, this paper proposes a two-stage local path planning algorithm based on sampling and optimization, termed the path stamping forming algorithm. In the exploration stage, the path stamping forming algorithm finds a preliminary path that avoids obstacles and runs parallel to the global path. The subsequent optimization stage determines the optimal local path that meets specific constraints. Finally, we compared the proposed algorithm with existing advanced navigation algorithms through simulations and experiments, demonstrating its superior performance. The results indicate that the proposed algorithm enables mobile robots to avoid obstacles and follow the global path without the need to re-plan the global path. Compared with the traditional local path planning algorithm, the performance of the proposed algorithm in following the global path is improved by up to 52.71%. Full article

This paper proposes an enhanced local path planning method based on the Dynamic Window Approach (DWA), enabling a mobile robot to safely avoid obstacles and efficiently reach its destination. To overcome the limitations of the conventional DWA in handling dynamic obstacles and to improve goal reachability, the velocity term—originally evaluated solely by speed—was redefined as the distance difference between the robot’s predicted future position and the target destination. This modification allows the robot to more effectively anticipate its short-term position while simultaneously considering potential obstacle locations. In particular, a linear prediction model for dynamic obstacle behavior was introduced, which estimates the future positions of obstacles based on their current position, velocity, and heading direction. Under the assumption that obstacles maintain constant speed and direction over short intervals, this model enables the robot to proactively plan avoidance maneuvers before a collision risk arises. Furthermore, a novel risk assessment strategy was incorporated to enhance collision prevention. This approach categorizes obstacles in front of the robot according to both distance and angle, evaluates obstacle density in various directions, and guides the robot toward safer paths with fewer surrounding obstacles. The effectiveness of the proposed method was validated through extensive simulations, comparing the conventional DWA, a modified DWA with the new velocity term, and the proposed DWA with dynamic obstacle behavior prediction. The results demonstrated that the proposed approach significantly reduced the number of collisions and overall travel time, thereby confirming its superiority in highly dynamic and uncertain environments. 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

Traditional ant colony algorithms for mobile robot path planning often suffer from slow convergence, susceptibility to local optima, and low search efficiency, limiting their applicability in dynamic and complex environments. To address these challenges, this paper proposes an improved trimming ant colony optimization (ITACO) algorithm. The method introduces a dynamic weighting factor into the state transition probability formula to balance global exploration and local exploitation, effectively avoiding local optima. Additionally, the traditional heuristic function is replaced with an artificial potential field attraction function, dynamically adjusting the potential field strength to enhance search efficiency. A path-length-dependent pheromone increment mechanism is also proposed to accelerate convergence, while a triangular pruning strategy is employed to remove redundant path nodes and shorten the optimal path length. Simulation experiments show that the ITACO algorithm improves the path length by up to 62.86% compared to the classical ACO algorithm. The ITACO algorithm improves the path length by 6.68% compared to the latest related research results. These improvements highlight the ITACO algorithm as an efficient and reliable solution for mobile robot path planning in challenging scenarios. Full article

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of the design samples. A hierarchical texture design strategy was developed, incorporating complexity gradients (low to high) and color contrast principles, and implemented via VTK-based 3D modeling with automated Euler angle annotations. The framework generated 2297 synthetic images across six texture variants, which were used to train a MobileNet model. The validation tests demonstrated that the high-complexity color textures achieved superior performance, reducing the mean absolute pose error by 64.8% compared to the low-complexity designs. While color improved the validation accuracy universally, the test set analyses revealed its dual role: complex textures leveraged chromatic contrast for robustness, whereas simple textures suffered color-induced noise (a 35.5% error increase). These findings establish texture complexity and color complementarity as critical design criteria for synthetic datasets, offering a scalable solution for vision-based pose estimation. Physical experiments confirmed the practical feasibility, yielding 2.7–3.3° mean errors. This work bridges the simulation-to-reality gaps in symmetric object localization, with implications for robotic manipulation and industrial metrology, while highlighting the need for material-aware texture adaptations in future research. Full article

The A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obstacle data to extract environmental information and employing a grid-based environmental modeling method, the proposed approach enhances path smoothness at turns using second-order Bezier curve smoothing. It improves the heuristic function and child node selection process, applying these advancements in experimental path planning scenarios. A simulated 2D map was constructed using point cloud scanning in RViz to validate the hybrid algorithm through simulations and real-world outdoor tests. Experimental results demonstrate that, compared to the A* and DWA algorithms, the improved hybrid algorithm enhances search efficiency by 10.93%, reduces search node count by 32.26%, decreases the number of turning points by 36.36% and the value of turning angle by 34.83%, shortens the total path length by 22.05%, and improves overall path smoothness. Simulations and field tests confirm that the proposed hybrid algorithm is more stable, significantly reduces collision probability, and demonstrates its applicability for mobile robot localization and navigation in real-world environments. Full article

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. In this work, we propose a calibration approach that simultaneously estimates these transformations, enabling precise calibration even when the localization system is imprecise. This approach is integrated into Atomic Transformations Optimization Method (ATOM), a versatile calibration framework designed for multi-sensor, multi-modal robotic systems. By formulating calibration as an extended optimization problem, ATOM estimates both sensor poses and calibration pattern positions. The proposed methodology is validated through simulations and real-world case studies, demonstrating its effectiveness in improving calibration accuracy for mobile manipulators equipped with diverse sensor modalities. Full article

Regarding the issue of high dependency on odometry in the adaptive Monte Carlo localization (AMCL) algorithm, an improved AMCL algorithm based on the normal distributions transform (NDT) and extended Kalman filter (EKF) is proposed. A virtual motion model is introduced into the AMCL framework to enable pose updates even when the robot has not moved. NDT is used for point cloud matching to estimate virtual displacement and calculate virtual control quantities, which are then fed into the motion model to predict and update particle states when the robot has not moved. Additionally, to avoid the negative impacts of encoder errors and wheel slippage on motion state estimation, the EKF algorithm integrates information from the wheel odometer and inertial measurement unit to estimate the robot’s displacement, thereby improving localization accuracy and stability. The performance of the proposed algorithm was experimentally validated in both simulated and real environments and compared with other localization algorithms. Experimental results show that the proposed algorithm can effectively improve localization speed during the cold start phase and enhances localization accuracy and stability throughout the localization process. The proposed method is a potential method for improving the performance of mobile robot localization. Full article

Due to the excellent concealment and high mobility, multiple small spherical underwater robots are essential for near coast defending missions. The formation of multiple small spherical underwater robots is particularly effective for tasks such as patrolling, reconnaissance, surveillance, and capturing sensitive targets. Moreover, some tasks need higher flexibility and mobility, such as reconnaissance, surveillance, or target encirclement at fixed locations. For this purpose, this paper proposes a position-based formation mechanism which is easily deployed for multiple spherical robots. A position planning method during the formation process is designed. This method creatively integrates the virtual linkage strategy with an improved consensus algorithm and the artificial potential field (APF) method. The virtual linkage strategy is in charge of computing the global formation desired target positions for robots according to the predefined position of the virtual leader joint. The improved consensus algorithm and APF are responsible for planning the local desired positions between two formation desired target positions, which is able to prevent collisions and excessive communication distance between robots. In order to verify the effectiveness of the proposed formation mechanism, adequate simulations and experiments are conducted. Thereby, the proposed formation frame offers great potential for future practical marine operations of the underwater multi-small robot systems. Full article

This paper presents an enhanced Simultaneous Localization and Mapping (SLAM) framework for mobile robot navigation. It integrates RGB-D cameras and 2D LiDAR sensors to improve both mapping accuracy and localization efficiency. We propose a data fusion strategy where RGB-D point clouds are projected into 2D and denoised alongside LiDAR data. Late fusion is applied to combine the processed data, making it ready for use in the SLAM system. Additionally, we propose the enhanced Gmapping (EGM) algorithm by adding adaptive resampling and degeneracy handling to address particle depletion issues, thereby improving the robustness of the localization process. The system is evaluated through simulations and a small-scale real-world implementation using a Tiago robot. In simulations, the system was tested in environments of varying complexity and compared against state-of-the-art methods such as RTAB-Map SLAM and our EGM. Results show general improvements in navigation compared to state-of-the-art approaches: in simulation, an 8% reduction in traveled distance, a 13% reduction in processing time, and a 15% improvement in goal completion. In small-scale real-world tests, the EGM showed slight improvements over the classical GM method: a 3% reduction in traveled distance and a 9% decrease in execution time. Full article

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m² area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future. Full article

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

SLAM is regarded as a fundamental task in mobile robots and AR, implementing localization and mapping in certain circumstances. However, with only RGB images as input, monocular SLAM systems suffer problems of scale ambiguity and tracking difficulty in dynamic scenes. Moreover, high-level semantic information can always contribute to the SLAM process due to its similarity to human vision. Addressing these problems, we propose a monocular object-level SLAM system enhanced by real-time joint depth estimation and semantic segmentation. The multi-task network, called JSDNet, is designed to predict depth and semantic segmentation simultaneously, with four contributions that include depth discretization, feature fusion, a weight-learned loss function, and semantic consistency optimization. Specifically, feature fusion facilitates the sharing of features between the two tasks, while semantic consistency aims to guarantee the semantic segmentation and depth consistency among various views. Based on the results of JSDNet, we design an object-level system that combines both pixel-level and object-level semantics with traditional tracking, mapping, and optimization processes. In addition, a scale recovery process is also integrated into the system to evaluate the truth scale. Experimental results on NYU depth v2 demonstrate state-of-the-art depth estimation and considerable segmentation precision under real-time performance, while the trajectory accuracy on TUM RGB-D shows less errors compared with other SLAM systems. Full article

This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot navigation. By combining point-line features with a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), the algorithm improves the feature matching’s reliability, particularly in low-texture areas. The method integrates dense point cloud mapping and an octree structure, optimizing both navigation and path planning while reducing storage demands and improving query efficiency. The experimental results using the TUM dataset and conducting tests in a simulated open-pit mining environment show that the proposed algorithm reduces the absolute trajectory error by 44.33% and the relative trajectory error by 14.34% compared to the ORB-SLAM3. The algorithm generates high-precision dense point cloud maps and uses an octree structure for efficient 3D spatial representation. In simulated open-pit mining scenarios, the dense mapping outperforms at reconstructing complex terrains, especially in low-texture gravel and uneven surfaces. These results highlight the robustness and practical applicability of the algorithm in dynamic and challenging environments, such as open-pit mining. Full article

Aiming to solve the problem of clearing obstacles in narrow and complex sewers, this paper introduces a visually assisted Sewer Cleaning Robot (SCR) for cleaning sewers with diameters ranging from 280 to 780 mm. The main work is carried out as follows: (a) A mobile platform is equipped with a pressing mechanism to press against the pipe walls in different diameters. The arm uses high-load linear actuator structures, enhancing load capacity while maintaining stability. (b) A Detection–Localization–Cleaning mode is proposed for cleaning obstacles. The YOLO detection model is used to identify six types of sewer defects. Target defects are then localized using monocular vision based on edge detection within defect bounding boxes. Finally, cutting is performed according to the localized defect positions. The feasibility of SCR in cleaning operations is validated through a series of experiments conducted under simulated pipeline conditions. These experiments evaluate its mobility, visual detection, and localization capabilities, as well as its ability to clear hard obstacles. This paper provides technical reserves for replacing human labor that use vision algorithms to assist in cleaning tasks within sewers. Full article