Journal of Field Robotics最新文献

Bridge inspection constitutes a critical yet labor-intensive task in civil infrastructure maintenance, often requiring access to confined, structurally complex environments. Conventional manual inspection suffers from low efficiency and high operational risks, and the robotic solutions encounter limitations in GNSS-denied and low illumination environments with texture-deficient surfaces. This study proposes QRIVAS (quadruped robot based intelligent visual acquisition system), an autonomous framework for structural component image acquisition without relying on prior maps to reduce the workload for manual close-proximity inspection. QRIVAS integrates 3D LiDAR SLAM with real-time semantic segmentation, enabling reliable navigation and precise structural component identification. In this paper, we focus on the exploration and inspection of bridge column—a representative and critical structural component of bridge systems. Experimental validation across simulated concrete railway viaducts and physical laboratory-scale bridge models (1:3 scale) shows that QRIVAS achieved 100% navigation success rate in simulation environments and 96.7% average task navigation success rate across six bridge columns in laboratory-scale bridge specimen. Compared to existing research, QRIVAS shows consistent performance improvements across varying tolerance conditions (25 cm and 50 cm radius), maintaining robust operation under both flat concrete floor and rough artificial grass terrain conditions. This work demonstrates the potential of AI-driven robotic systems to transform traditional infrastructure maintenance practices.

In robotic applications, the HTC VIVE tracker is frequently used for Learning from Demonstration. This solution and similar devices are not industrial-grade, meaning that their accuracy in tracking movements in three-dimensional space needs to be refined to ensure it is sufficient for programming precise robot positioning tasks. Several different methods are often used to improve position tracking accuracy, such as polynomial correction and neural networks. Various methods of parameterizing position and orientation are also frequently used, such as Euler angles or quaternions, although trade-offs between compactness and numerical stability mean that they are suitable for different correction methods. In this study, we explore four different ways of representing the pose orientation (axis-angle representation, quaternion, rotation matrix, Zhou representation) for the purpose of implementing pose tracking accuracy correction for the HTC VIVE tracker. The paper investigates the applicability of these representations for the Neural Network correction methods and compares the results with the classical polynomial correction method. As part of the study, three experiments were conducted involving measurements of the actual and measured positions of the robot and the tracker. An industrial UR5e robot arm from Universal Robots was used as the reference system for collecting measurement data, with an HTC VIVE tracker mounted on its wrist. The obtained results confirm that both the representation and neural network architecture significantly influence calibration effectiveness of HTC VIVE tracker. The results of the experiments showed that the Zhou parametrization of the orientation, combined with neural network rectification, performs best and results in a 17-fold improvement in the pose estimation accuracy of the HTC VIVE tracking system. The PMC algorithm offers a valuable alternative when fast calibration is required, providing significant accuracy improvements with minimal computational cost.

Currently, research on cooperative navigation of multi-robot systems in uneven terrain environments is still relatively scarce. The complex and rugged terrain features often pose significant threats to the navigation performance of each robot, thereby presenting considerable challenges for conducting safe and efficient collaborative operations in such environments. This article proposes a multi-robot cooperative navigation framework aimed at addressing these challenges on uneven road surfaces. Based on three-dimensional Voronoi partitioning, a terrain-aware partitioning strategy (TAPM-IVP) is introduced, enabling each robot to perform real-time terrain analysis, categorizing the space into traversable space, non-traversable space, and free space, thus facilitating task allocation in complex uneven terrain. Based on the partition results of TAPM-IVP, a greedy heuristic sub-goal decision method is proposed, which selects safe, non-redundant, and collision-free target points, employing a hierarchical generation strategy to guide the robotic system in efficiently navigating in unknown environments, thereby significantly enhancing navigation adaptability and task completion efficiency in uneven scenarios. Finally, both simulations and real-world experiments validate the feasibility, safety, and efficiency of the proposed framework for cooperative navigation in uneven road conditions.

With the rapid advancement of mobile robotics, the demand for safe and efficient path planning has become increasingly prominent. This research aims to address the challenges of path redundancy and the lack of stable obstacle avoidance strategies encountered by mobile robots during path planning in dynamic environments. A novel hybrid enhanced path-planning algorithm is proposed, which integrates obstacle information, robot safety data, path-simplification techniques, and dynamic-obstacle avoidance strategies. By combining global and local path planning and introducing a path evaluation system based on robot status and dynamic-obstacle motion information, the algorithm achieves efficient and flexible path planning. The effectiveness and feasibility of the algorithm are validated through simulation experiments and real-world testing. The results demonstrate that the algorithm can achieve safe and efficient path planning under different speeds and dynamic-obstacle scenarios, significantly reducing the frequency of path switching and waiting times, thus enhancing the efficiency and autonomy of the robot's actual scene navigation. In the experimental scene, the overall travel time is saved by 8.3%, the traffic obstacle area time is saved by 48.9%, and the obstacle avoidance route is stable and efficient. Compared with the current advanced algorithm, the proposed algorithm can improve the driving efficiency of the robot by more than 12%, and the robot's behaviour is more secure in the face of dynamic obstacles. This research provides an effective solution for mobile robot path planning in complex environments, with significant theoretical and practical implications.

With the aging population and labor shortages, the proportion of labor costs in tomato harvesting is increasing, making the development of tomato harvesting robots imperative. This study developed an integrated tomato bunch harvesting robotic system for cherry tomatoes. A combined cutting and gripping end-effector powered by a single actuator, achieving a cutting success rate of 93.33% and a gripping capacity of 1600 g. A parameterized camera arrangement was employed to match the robotic arm's field of view, thereby avoiding mutual interference. A tomato bunch and stalk recognition model was constructed based on the YOLOv4 algorithm to enable precise localization of harvesting points. The proposed tomato bunch-stalk matching method achieved a recall rate of 99.22%, while the stalk growth-direction qualitative discrimination method attained an accuracy of 97%. Field experiments demonstrated that the system achieved an average harvesting time of 12.23 s per tomato bunch and an overall harvesting success rate of 70.77% in unstructured environments, improving automation and operational efficiency compared to existing solutions. This research offers a solution integrating hardware optimization and perception algorithms for greenhouse harvesting robots, demonstrating potential for commercial application.

This paper presents an integrated vertical control method for online identification and compensation of residual buoyancy, designed for full-ocean-depth (FOD) autonomous underwater vehicles (AUVs) that rely on vertical thrusters for depth-keeping or height-keeping hovering tasks. To address the residual buoyancy drift caused by vehicle hull compression and seawater density variations under extreme hydrostatic pressure, the proposed method constructs a condition-triggered bidirectional switching framework between velocity and position control modes: During the large-error stage, a velocity control mode is employed to rapidly drive the vehicle into a low-speed region. Once the depth error and vertical velocity satisfy predefined threshold conditions, the system switches to a position control mode. In this mode, the equivalent residual buoyancy is estimated online via thruster force equilibrium and applied as feedforward compensation to achieve precise and stable hovering. If operating conditions change, the switching and identification mechanisms can be retriggered to maintain control performance. The proposed method was validated through test-tank experiments and sea trials for the 1000-, 7000-, and 11,000-m classes using the Wukong FOD AUV. Field results demonstrated that the system achieved average height tracking errors of 0.0124 and 0.0668 m in the test tank and 11,000-m class trials, respectively. These results verify the effectiveness and engineering applicability of the proposed method in rejecting unknown residual buoyancy disturbances and achieving stable hovering in hadal environments.