Journal of Field Robotics最新文献

The present study proposes a new design for a robot fish which is propelled by periodic vibrations of a flexible-soft coupled beam. This unique, flexible-soft coupled beam can display the first and second-order mode oscillations, which are capable of effectively mimicking the swings of the fish tail, resulting in fast swimming of the robot fish. Firstly, the nonlinear dynamic model for the coupled beam is established based on the absolute node coordinate formulation. The effect of length and stiffness parameters on natural frequency and mode shape of the coupled beam is explored to obtain the linear dynamic characteristics of the fish tail. To reach the maximum vibration amplitude, the optimal values for position, frequency, and magnitude of the applied force and the length and stiffness parameters are determined. In the following, the control system uses a communication mode to receive signals from a wireless communication module, and an inertial sensor is designed. The fuzzy PID algorithm is employed to control vibrations of the coupled beam to realize the swimming forward and turning around of the robot fish. Finally, through 3D printing and the opening mold technique, the robot fish is fabricated with an overall size of 130 × 125 × 70 mm³. Swimming experiments are performed to display the propulsion speed and force of the robot fish. It shows that the swimming speed of 1.17 BL/s can be achieved, which is much higher than most of the previously designed robot fish in BCF mode. In addition, the experiments indicate that the robot fish has an excellent swimming performance even in countercurrent, complex trajectories, and heavy load environments. The present study offers a delicate design and a precise theory of the flexible-soft coupled beam-based fish tail for fast swimming of the robot fish.

This paper presents a novel Recurrent Neural Network (RNN) controller for redundancy resolution and orientation control of the Stewart platform. The Stewart platform features six prismatic actuators, making it a six-degrees-of-freedom (6-DOF) system. When imposing three-dimensional orientation control, the platform retains a redundancy of 3-DOF, which can be utilized to achieve secondary goals. The key novelty of this study lies in the formulation of a Jacobian-free, gradient-free control strategy that directly solves a constrained nonlinear optimization problem at the angular level, thereby significantly improving computational efficiency and robustness compared with conventional controllers. Specifically, we propose the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, a biologically inspired metaheuristic framework that bypasses the computationally intensive Jacobian inversion typically required in redundancy resolution. The orientation control problem is formulated as a constrained optimization task, incorporating an energy-efficient actuator usage objective and mechanical constraints modeled as inequalities. Theoretical stability and convergence guarantees are established for the proposed BAORNN framework, ensuring reliable operation across a wide range of configurations. To validate the approach, we developed a high-fidelity simulation environment using the Simscape Multibody library in Simulink and conducted extensive experiments across multiple time-varying reference trajectories. Quantitative performance comparisons against a state-of-the-art inverse kinematics controller demonstrate the superior accuracy, convergence speed, and constraint-handling capabilities of our method. Furthermore, we showcase a realistic application scenario by integrating the controller with a chair-mounted Stewart platform for immersive driving and flight simulations, demonstrating the potential for real-world deployment in motion simulation and training systems. In summary, this paper introduces a computationally lightweight, robust, and highly accurate RNN-based controller tailored for redundant Stewart platforms, with proven advantages over traditional Jacobian–based methods.

Real-time object recognition is a significant field of research with numerous applications, including object tracking, video surveillance, and autonomous driving. This identifies the smallest bounding boxes that encompass the objects of interest within the input images. Nevertheless, these approaches face challenges, like limited support for quantization and suboptimal trade in achieving accurate object detection. To address these issues, a novel approach called Faster region-based Convoluted Non-monopolize search You Only Look Once neural architecture Search (FCN-YOLOS) is introduced for object detection. This approach merges the advanced feature abstraction abilities of Faster R-CNN with the efficient object recognition strengths of YOLOv8, enhanced by NAS optimization. YOLOv8 is employed for its rapid and accurate real-time detection of abandoned items, while Faster R-CNN contributes sophisticated feature extraction by utilizing statistical, grid, and Histogram of Oriented Optical Flow (HOOF) features to improve object representation and classification. Additionally, NAS optimizes hyperparameters by balancing exploration and exploitation, which helps minimize the loss function, reduce overfitting, and enhance generalization. This results in exceptional real-time object detection performance within the FCN-YOLOS framework. The proposed technique has demonstrated a maximum image of approximately 99%, 96.3%, 94.9%, and 95.2% concerning brightness realization compared to existing methods for accuracy, recall, precision, and F1 score, respectively. These outcomes highlight its extensive applicability across diverse object detection contexts, rendering it a compelling option for both academic and industrial research. Overall, the proposed approach for object recognition techniques in feature extraction and hyperparameter adjustments further improves evaluation in terms of efficiency and object detection accuracy.

This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dispersed sampling points, low sampling efficiency, and excessive path waypoints encountered in traditional Rapidly-Exploring Random Trees (RRT) algorithms, this paper proposes an Optimized Sampling Strategy and Artificial Potential Fields Fusion-based Informed-RRT* global path planning algorithm. Initially, sampling angles are determined based on the position of the target point, and the workspace is partitioned into regions with varying levels of importance. Subsequently, an improved artificial potential fields algorithm is integrated to further refine the resultant forces acting on the nodes. Finally, cubic spline interpolation is utilized to smooth the generated path. The proposed algorithm was validated through simulation and experimental studies conducted on simple, narrow, and complex maps. The results demonstrated significant reductions in search time, path length, and the number of path waypoints compared to conventional A*, Dijkstra, RRT, RRT*, and Informed-RRT algorithms. Additionally, the smoothness of the generated paths was notably improved. In the virtual maze experiments and real-world environment tests, the improved algorithm presented in this paper demonstrates significant advantages over five other algorithms.

Odometry plays a crucial role in autonomous tasks of field robots, providing accurate position and orientation derived from sequential sensor observations. Odometry based on Light Detection and Ranging (LiDAR) sensors has demonstrated widespread applicability in environments with rich structured features, such as urban and indoor settings. However, for unstructured environments like scrubland and rural roads, the extraction, description, and correct matching of LiDAR features between frames become challenging. Due to the lack of flat surfaces and straight lines, the existing odometry approaches, whether using hand-crafted features such as edge and planar points or learned features through networks, will face the problem of decreased positioning accuracy and potential failure. Therefore, we propose a neural LiDAR odometry based on Trans-frame Association to extract more effective features for pose estimation in unstructured environments. The Trans-frame Association module contains a fully interactive frame Transformer and a scan-aware Swin Transformer. The former applies cross-attention to features extracted from two consecutive frames, thus enhancing the accuracy and robustness of feature correspondences by considering the contextual information. The latter restricts the attention mechanism to shift along the scan lines of LiDAR, thereby leveraging the sensor's inherent higher horizontal resolution. Our Transformer has linear complexity, which guarantees the module can meet real-time requirements. Additionally, we design a Reuse Refinement Pyramid architecture to further improve the accuracy of pose estimation by reusing multiresolution features. We conducted extensive experiments on the RELLIS-3D data set and our Matian Ridge data set collected in a representative unstructured scene. The results demonstrate that our network outperforms recent learning-based LiDAR odometry methods in terms of accuracy. The code is available at https://github.com/qlsinori/FAR-LO.

In the field of high-redundancy manipulators, specifically in the rock drilling manipulator domain, fast and efficient path planning is crucial. Therefore, this paper proposes an improved algorithm, v-BI-RRT, based on the BI-RRT algorithm and oriented vector methods. In this algorithm, the nodes along one path are extended in the direction of the node coordinates of another path as the target direction. When the path collides with an obstacle, new node coordinates are generated using a random sampling method to bypass the obstacle. This approach enhances spatial search efficiency. For high-redundancy manipulators like the rock drilling manipulator, self-collision avoidance is a key component of collision-free path planning. This paper uses oriented bounding boxes (OBB) and capsules to envelope the manipulator's body. Potential self-collisions are detected in two stages: during the rapid detection phase, non-colliding pairs are quickly excluded, and during the precise detection phase, the distance between the remaining potential collision pairs is calculated using Euclidean distance to find the shortest distance. Finally, the self-collision detection algorithm is integrated into the v-BI-RRT algorithm. Simulations and experiments demonstrate that the algorithm responds quickly and performs well in avoiding collisions when applied to path planning for the rock drilling manipulator.

Wall-climbing robots are increasingly being used to inspect and maintain large ship facades, ensuring structural safety and reliability. However, conventional rigid robots often struggle with adaptability and flexibility on complex curved surfaces. To address this, we propose an omnidirectional magnetic-wheel wall-climbing robot with a passive-compliant suspension system. This design allows all magnetic wheels to adhere simultaneously to inclined surfaces with varying curvatures, and each wheel can independently rotate to any angle. We quantitatively analyzed the relationship between configuration parameters and the spatial position mapping of the robot on complex elevations to verify its adaptability to variable curvatures. Based on normalized surface configurations of varying curvatures on ship facades, we establish the robot's kinematic transformation flow. We develop spatial dynamic models for three motion modes on variable-curvature surfaces using energy conservation principles, analyzing driving-wheel motion constraints and friction-type differences across the modes to enable precise calculation of robot motion parameters. The proposed robot enhances ship facade maintenance by enabling stable, flexible motion on variable-curvature surfaces, improving efficiency, safety, and adaptability.

Screw-propelled vehicle (SPV) is a novel multi-terrain vehicle that demonstrates significant potential in military, rescue, and extreme environment applications due to its exceptional terrain adaptability and maneuverability. However, most existing studies primarily focus on performance analysis in a single environment, resulting in a lack of systematic research on vehicle performance across multiple road conditions. In this study, an innovative coupling method combining multi-body dynamics (MBD) and the discrete element method (DEM) was employed to establish a comprehensive model that captures the interaction between the SPV and complex terrain. This model accurately simulates the mechanical behavior of the vehicle under various challenging road conditions, including sand, snow, and hay fields. Using the response surface method (RSM) and the Monte-Carlo method, we optimized key structural parameters of the SPV, such as the height-to-diameter ratio, spiral angle, and number of blades. This optimization process identified the parameter combinations that yield the best performance across multiple road conditions. Experimental results indicate that the adaptability and stability of the optimized SPV in diverse environments have significantly improved, thereby validating the accuracy and reliability of the numerical model. This study provides a solid theoretical foundation for enhancing and optimizing the performance of future SPV and is expected to facilitate ongoing advancements in screw propulsion technology for complex tasks and extreme conditions.