Journal of Field Robotics最新文献

Unmanned mining technology is essential for enhancing safety, increasing efficiency, and reducing operational costs. The complex and hazardous nature of mining environments demands advanced positioning systems for autonomous vehicles, with laser simultaneous localization and mapping (SLAM) algorithms playing a critical role. This paper provides a systematic review of the core technical modules within laser SLAM algorithms, analyzing their development trends, strengths, and weaknesses. A comprehensive evaluation of fifteen mainstream SLAM algorithms on the AutoMine open-pit mining data set reveals significant insights. Experimental results demonstrate that traditional feature-based algorithms are prone to significant trajectory drift due to feature loss in sparse-feature mining environments. Notably, the study further identifies a specific “Ramp Drift” mechanism where recursive estimators suffer Z-axis instability on monotonic slopes. Comparative analysis suggests that while Light Detection and Ranging (LiDAR)–inertial fusion generally enhances robustness, degeneracy-aware architectures are the decisive factor for stability. Specifically, the LiDAR-only odometry GLO achieves the highest stability in relative pose error due to its Weighted Elastic Matching strategy, while the Adaptive-LIO demonstrates superior global consistency in absolute pose error. This study highlights a current lack of SLAM architectures specifically optimized for the unique challenges of open-pit mines. Future research should focus on feature extraction enhancement in open scenes, the development of optimized LiDAR–inertial–RTK fusion architectures, and the integration of artificial intelligence to improve adaptability in dynamic and degraded scenarios.

This study focuses on the perception of traversable areas and ground materials in complex field environments, driven by the application demands of autonomous ground intelligent agents in critical tasks such as battlefield support and disaster relief. Compared to existing studies on fine-grained segmentation in structured environments (e.g., urban roads), perception in unstructured field environments is far more challenging due to irregular terrain and complex conditions, while research in this area remains limited. To address this, this paper proposes a cross-modal synergistic optimization multi-task segmentation network (CSOM-Net) for ground intelligent agents in field environments, based on an in-depth analysis of the technical requirements in complex field settings. This paper proposes a conflict-consistency guided feature fusion (CCGF) method to resolve the challenge of feature conflicts in multimodal data that hinder effective fusion in complex field environments. A cross-modal bidirectional mutual adaptation (CBMA) learning strategy is proposed to address the feature interference caused by significant differences in optimization objectives in cross-modal tasks under field environments. Extensive comparative and ablation experiments were conducted on both real-world and simulated complex field datasets to evaluate the proposed CSOM-Net model. Experimental results show that CSOM-Net outperforms conventional models, achieving approximately 5% and 2% mIoU improvements in point cloud traversable area and image ground material segmentation tasks, respectively. The model proves effective for joint segmentation tasks of point cloud traversable areas and image ground materials in complex field environments, providing a robust solution to enhance environmental perception capabilities for autonomous ground agents operating under challenging field conditions.

Chile pepper farming in New Mexico faces critical constraints from water scarcity, soil salinity, and labor shortages. Precision agriculture technologies enabling data-driven resource management offer promising solutions. This paper presents an autonomous in-situ soil sensing system integrated with a mobile manipulator robot for automated soil data collection. The main contribution is a unified, failure-aware autonomous soil sensing system that integrates vision-based surface characterization, adaptive force-controlled sensor insertion, and insertion monitoring with failure detection and recovery into a single low-cost field-deployable robotic platform. The system comprises a two-stage visual alignment process using RGB-D camera data to adapt to terrain slope and identify obstacle-free insertion sites, a force-based contact detection mechanism to determine sensor-soil contact, and adaptive impedance control with Kalman filter-based soil stiffness estimation for controlled sensor insertion. The system is implemented on a mobile platform with a six DoF manipulator and TEROS 12 soil sensor. Field evaluation across 41 sensing operations in varying soil conditions during the early chile pepper season demonstrated a 75.6% success rate, with soil measurements correctly obtained upon full sensor insertion. In 90.2% of sensing operations, the system made correct decisions, including aborts when necessary. Main limitations included the inability to detect flush surface obstacles, occasional false contact detections, and incorrect insertion completion verification. Nevertheless, the results demonstrate the feasibility of autonomous in-situ soil sensing in chile pepper cultivation, providing a foundation for fully autonomous soil monitoring. The methods and approaches developed in this work may extend to other crops requiring in-situ soil measurements.

Proprioceptive sensors are crucial for legged robots, as they provide reliable internal state information and are less affected by environmental disturbances. A robust proprioceptive base state estimator is essential for the localization and control capabilities of legged robots. Classical methods for estimation of legged robot state often use IMU integration for prediction and use the assumption of stationary foot contact for updates. However, they suffer from issues like IMU accelerometer noise from foot-end impacts, nonlinear foot-ground interactions, and sensor parameter uncertainties, which leads to estimation drift. To address these limitations, this paper proposes a novel system for estimating the low drift state of legged robots by combining contact and joint state estimation. Specifically, our method i) proposes a physics-informed contact estimation state network to obtain accurate contact states for legged robots, ii) estimates joint states of the legged robot and obtained body accelerations computed from joint accelerations, and iii) updates the base position, orientation, and velocity by gravitational acceleration components and the assumption of static contact points. Under standard operating conditions, experiments on both public and private datasets demonstrate that the proposed method outperforms state-of-the-art algorithms.

High-fidelity models play an essential role in advancing the structural optimization and motion simulation of amphibious vehicles. However, the complexity of hydrodynamics poses significant challenges in dynamic modeling, parameter identification, and experimental validation. To address these challenges, this research derives a six-degree-of-freedom dynamic model for a vector propelled amphibious vehicle based on maneuvering theory, including a dedicated propulsion system dynamic model. Given the system identification challenges posed by the highly coupled multi-parameter dynamics, a systematic experimental framework is devised, featuring decoupled measurements of the propulsion and maneuvering dynamics. A staged parameter identification methodology integrating the genetic algorithm and the least squares method is proposed. The methodology initially identifies a subset of parameters through decoupled reduced-order models, and subsequently performs a systematic identification of the remaining parameters based on the complete coupled model. For model validation, a simulation platform based on numerical integration methods is developed, with real-time visualization implemented in Unreal Engine 4 (UE4). Field tests and hardware-in-the-loop (HIL) validation demonstrate that the established model with identified parameters can accurately capture the motion characteristics of the amphibious vehicle.

To address autonomous underwater vehicle (AUV) path tracking under time-varying disturbance, this paper proposes an Extended State Observer (ESO)-based Line-of-Sight (LOS) guidance strategy that accounts for time-varying ocean currents, model uncertainties, and drift angles. The disturbances are treated as a lumped term without assumptions on their magnitude or variation. A kinematic error model incorporating these effects is established, and an ESO is designed for real-time disturbance estimation and compensation. An adaptive look-ahead distance is introduced to enhance responsiveness to the AUV's tracking state. A virtual control input is then integrated into the traditional LOS framework to formulate an improved guidance law, forming a closed-loop cascaded system of tracking and estimation errors. Using input-to-state stability (ISS) theory and the cascaded systems theorem, the overall system is proven to be ISS, and the ultimate bound of the cross-track error is derived under bounded disturbances. The proposed method was compared with several existing approaches in simulations, demonstrating its superiority. Finally, three field experiments were conducted to track straight-line, polyline, and circular paths, respectively, achieving a mean cross-track error of no more than 0.48 m across three tested path types and an average estimation error of ESO no greater than 0.0163 m.

In this paper, a real-time Gaussian potential B-Spline planner (RGPB-Planner) is proposed to address the problem of safe flight planning for unmanned aerial vehicles (UAVs) in complex environments. Existing trajectory optimization approaches, such as B-Spline and safe-corridor methods, can generate smooth trajectories. However, these approaches often suffer from high computational cost, local collisions near obstacles, or instability in dense environments. To fill this gap, the proposed RGPB-Planner integrates a Gaussian potential with B-Spline optimization, which ensures real-time performance, smoothness, and safety by constraining control points within the safe potential region. The proposed RGPB-Planner comprises two modules: the front-end and back-end modules. In the front-end module, a local dynamic A-star graph search is used to find the shortest path for UAVs to safely reach target points in complex environments. In the back-end module, the front-end shortest path is optimized using a Gaussian potential B-Spline trajectory optimization method that considers the UAV's kinematics and dynamics constraints. Finally, simulation results demonstrate that the proposed RGPB-Planner reduces flight time by 15%–19%, increases average velocity by 13%–21%, and shortens trajectory length by 2%–3% compared with Fast-Planner and Ego-Planner, while real-world experiments validate its real-time capability and flight safety. Therefore, the proposed RGPB-Planner provides a reliable solution to ensure safe flight planning in complex environments.

Autonomous navigation for inspection robots in cattle barns critically depends on localization and path-planning algorithms. To address the issues of low navigation accuracy, long planning time, and insufficient trajectory smoothness in barn environments, this study proposes a navigation framework that combines LiDAR–inertial odometry and a rapidly-exploring random tree (LIO-RRTNav). For relocalization, this study develops a Fast_LIO2 with relocalization and pose optimization method (RP-Fast_LIO2). It exploits the geometric structure of the barn to associate the current frame point cloud with historical point clouds to suppress drift. Meanwhile, stable features are extracted from dynamic scenes and registered to the global map using the Iterative Closest Point (ICP) algorithm; the registration result is used as the initial state for an iterated extended Kalman filter (IEKF) to refine pose estimation. For path planning, this study proposes a highly efficient, robust, and smooth rapidly exploring random tree method (HRS-RRT). By incorporating a goal-oriented random sampling strategy and a safety-distance constraint, it enables safe and efficient planning, and the resulting path is further optimized through path pruning and cubic B-spline smoothing based on a spring potential energy model. Simulation test results showed that the HRS-RRT algorithm reduced 22.17%, 75.00%, and 83.09% in terms of path length, planning time and number of iterations, respectively, when compared with the traditional RRT algorithm. The experimental results revealed that RP-Fast_LIO2 algorithm reduced the mean and root mean square error of the absolute position error by 83.21% and 79.89%, when comparing with the traditional Fast_LIO2 algorithm. In navigation experiments conducted in two cattle yards, when the robot operated at speeds of 0.3, 0.5, and 1.0 m/s, the maximum lateral and longitudinal deviations did not exceed 0.13 and 0.08 m, respectively, and the maximum heading error did not exceed 7.24°. The results acquired verified the adaptability of the LIO-RRTNav algorithm in the cattle yard environment, meeting the requirements of cattle yard inspection robot operation.

Path-following control for large-inertia surface vessels remains challenging due to slow yaw dynamics and environmental uncertainties. This paper proposes a hybrid framework integrating Line-of-Sight (LOS) guidance, drift-angle compensation, and an Adaptive Gated Interaction Twin Delayed Deep Deterministic Policy Gradient (AGI-TD3) controller. The primary innovation lies in the hierarchical feature-processing architecture of the AGI-TD3 agent. By implementing decoupled encoding for heterogeneous state-action subspaces, the network isolates interference from disparate physical semantics. Furthermore, an adaptive gated interaction mechanism is introduced to selectively modulate information flows, reinforcing the causal relationship between heading errors and control actions. Simulation results and full-scale experiments on a 40 m-class vessel demonstrate that the proposed method significantly improves path following performance and robustness under realistic operating conditions.