Journal of Field Robotics最新文献

The cover image is based on the article Tracking mooring lines of floating structures by an autonomous underwater vehicle by Sehwa Chun et al., 10.1002/rob.70076.

The image illustrates a jet-powered personal aerial system (PAV) performing vertical take-off and landing within a post-disaster urban environment. The system integrates five micro turbojet engines and a dual-degree-of-freedom vector control mechanism to achieve high maneuverability, stability, and fault tolerance. This work demonstrates the feasibility of a compact, human-scale VTOL platform capable of safe operation in complex field conditions, with potential applications in rescue and rapid response missions.

This study presents a novel method for tracking mooring lines of Floating Offshore Wind Turbines (FOWTs) using an Autonomous Underwater Vehicle (AUV) equipped with a tilt-controlled Multibeam Imaging Sonar (MBS). The proposed approach enables the AUV to estimate the 3D positions of mooring lines and safely track them in real-time, overcoming the limitations of traditional Remotely Operated Vehicle (ROV)-based inspections. By utilizing the tilt-controlled MBS and a pre-trained You Only Look Once (YOLO) model, the AUV identifies the mooring lines within sonar imagery and dynamically adjusts its velocities to maintain a safe distance during the inspection. A re-navigation method using the Rauch-Tung-Striebel (RTS) smoother enhances the AUV's localization accuracy by correcting its trajectory using post-processed data from sensors such as the Doppler Velocity Log (DVL), Super Short Baseline (SSBL) system, and Global Navigation Satellite System (GNSS). Additionally, reconstruction with catenary curve fitting is employed to estimate the mooring line's catenary parameters, offering insights into its potential deformation. The approach was validated using a hovering-type AUV, Tri-TON through both tank experiments and a sea experiment at an FOWT Hibiki in Kitakyushu, Japan. In the sea experiment, the AUV successfully tracked the mooring lines for 423 s, demonstrating its ability to estimate the position and catenary parameters of the mooring lines. The experimental results highlight areas for future improvement, particularly in enhancing localization accuracy, developing robust control algorithms, and expanding the analysis of mooring line conditions. This method lays the groundwork for future advancements in automated mooring line inspections and enables the integration of additional techniques, such as visual inspection.

Typical landing gears of small uninhabited aerial vehicles (UAV) limit their capability to land on vehicles moving at more than 20–50 km/h due to high drag forces, high pitch angles and potentially high relative horizontal velocities. To enable landing at higher speeds, a combination of lightweight friction shock absorbers and reverse thrust was developed. This allows for rapid descents (i.e., 3 m/s) toward the vehicle while leveling at the last instant. Simulations show that the proposed system is (1) more robust at higher descent speeds contrary to traditional configurations, (2) can touchdown at almost any time during the leveling maneuver, thus reducing the timing constraints, and (3) is robust to many environmental, design and operational factors, maintaining a success rate above 80% up to 100 km/h. Compared to standard multirotors, this approach expands the possible state envelope at touchdown by a factor of 60. A total of 38 experimental trials were conducted where a drone successfully landed on a pickup truck moving at speeds ranging from 10 to 110 km/h. The increased touchdown envelope was shown to improve the multirotors' robustness to external disturbances such as winds and wind gusts, sensor errors and unpredictable motion of the ground vehicle. The increased landing capabilities also expand the flight envelope at the start of the leveling maneuver by a factor of 38 compared to a standard multirotor, thereby allowing the drone to fly in tougher conditions and initiate its leveling maneuver from a broader range of altitudes, vertical and horizontal velocities, as well as pitch angles and rates.

Inertial Measurement Units (IMUs), comprising gyroscopes and accelerometers, are fundamental for motion estimation in navigation and robotics. However, their performance is often degraded by nonlinear and time-varying errors, such as bias drift, scale-factor deviations, and sensor noise. Traditional compensation methods based on linear assumptions or static models struggle to address the dynamic and correlated nature of these errors, limiting real-time calibration robustness. To address this challenge, we propose a transformer-based framework for gyroscope error compensation, which dynamically models temporal dependencies and nonlinear error characteristics using self-attention mechanisms. Our approach incorporates a sliding window to exploit historical sensor data and applies a geometrically constrained loss function defined on the SO(3) Lie group, ensuring physically consistent orientation estimates. Comprehensive experiments on the public EuRoC and Technical University of Munich data sets demonstrate that our method achieves a mean orientation error of 1.56°, outperforming state-of-the-art approaches including robust 3-D orientation estimation with a single particular IMU (9.46°), DenoiseIMU (2.09°), and Gyro-Net (1.49°). Additionally, our framework reduces the Absolute Trajectory Error (ATE) by 45.6% (average 0.070 m) and the Relative Pose Error by 48.2% (average 0.0043 m) compared with established baselines. These results highlight the effectiveness and robustness of our method, particularly in challenging scenarios with rapid motion and low-texture environments. Overall, our transformer–based approach significantly enhances the reliability and accuracy of IMU-based systems, offering a promising solution for autonomous navigation and related applications.

Farmers frequently suffer from musculoskeletal disorders, particularly lower back pain (LBP), mainly due to occupational ergonomic factors such as repetitive stooping and lifting. This study proposes using an exoskeleton to reduce LBP risk by providing external torque to the hip joints. The grasshopper-leg-inspired exoskeleton (GIExo) incorporates a spring actuator mimicking the lump mechanism of grasshopper legs and an actively controlled clutch to toggle assistance. Fifteen farmers (n = 15) participated in rice field experiments under three conditions: without assistance, wearing GIExo without activation and with assistance. Electromyography (EMG) was used to monitor muscle activation while farmers performed lifting and rolling tasks. The Anybody software estimated compression and impact forces on the body before and after wearing GIExo. A body tracking experiment using YOLOv8 was conducted to evaluate the influence of GIExo on the natural movement of users. Results from Friedman test showed that GIExo reduced average activation of the upper thoracic erector spinae (TES) and lumbar erector spinae (LES) by (28.5%−35.5%) and (31.9%−35.2%), respectively, during lifting tasks, and by (52.1%−45.4%) and (64.6%−68.2%), respectively, during rolling tasks. The compression force between the 5th lumbar vertebra (L5) and sacrum (S1) decreased by 7.7% with GIExo. Furthermore, when the exoskeleton was worn in the without activation state, it did not restrict the user's range of motion. Additionally, perceived exertion and acceptance surveys evaluated user feedback on physical effort, comfort, and usability. Responses indicated reduced fatigue and overall acceptance of GIExo in real working conditions. These findings suggest that GIExo is a promising solution for reducing fatigue and musculoskeletal disorders, improving ergonomic conditions in labor-intensive agricultural tasks such as lifting and stooping.