Journal of Field Robotics最新文献

Terrain-aided navigation (TAN) is a viable method to achieve long-term underwater navigation for long-range autonomous underwater vehicles (AUVs). However, the high-accuracy positioning results of most TAN systems rely on precise a priori seabed terrain maps, which restricts their applicability to a few areas with accurate bathymetric measurements of the seabed terrain. This article introduces a TAN system based on the General Bathymetric Chart of the Oceans (GEBCO) data set for global marine applications. Specifically, to address the low accuracy and poor robustness of the TAN system with imprecise bathymetric measurement and low-resolution data from the GEBCO data set, this article proposes a multizonotope TAN method based on set-membership filter (SMF) theory. The SMF theory is employed to handle the unknown distribution of the measurement noise from the GEBCO data set, introducing a multizonotope measurement update model to achieve more precise navigational results while addressing the perceptual ambiguity caused by self-similar terrain. The smoothness of the terrain is incorporated as a parameter in the generation ranges of multizonotope, enabling adaptive adjustment based on terrain smoothness to reduce costs and enhance navigational performance. The accuracy and robustness of the proposed method are verified through all shipboard experiments, publicly available data sets, and AUV experiments. Compared with state-of-the-art TAN methods, the average and maximum positioning errors have decreased by 64.83% and 48.84%, respectively. Finally, based on the experimental results, a preliminary distribution of suitable areas in the oceans is provided.

This paper aims to develop a miniature mobile robot suitable to assist archeologists in their first exploration of unknown underground tombs. Due to the rather complex and irregular terrains in the tombs and inspired by the classic RHex design, we have developed a two-segment articulated robot (A-RHex) with two RHex design units. The robot is compact and lightweight, with dimensions of 25 cm long, 6.5 cm wide, 7 cm high, and weighs 283 g. To assist the robot in entering the tomb, we have also designed a deployment platform that can take the robots underground through a 10-cm exploration hole. We introduce the overall design, control, and communication methods of A-RHex, and theoretically analyze how the articulated design can improve the stability of the robot on slopes. Laboratory experiments and field testings at two real archeological excavation sites in China have validated A-RHex's mechanical design, control strategies, communications, and capabilities for pre-exploration of open and closed tombs. We believe that this kind of robot with high terrain adaptability and a small profile may become an important tool for field archeology in the future.

Given the demanding and unpredictable operational conditions, autonomous underwater vehicles (AUVs) often encounter different propulsion faults, leading to significant economic losses and mission impairments. To address this challenge, vibratory time-series features can be extracted for the precise propulsion fault diagnosis of AUVs. A squeeze-and-excitation (SE) attention residual network (SEResNet) is therefore put forward to enhance the feature extraction for AUV propulsion fault diagnosis. By leveraging the vibratory time-series data obtained from the AUV, an SE attention mechanism is embedded into a residual network. This integration facilitates the extraction of pertinent vibratory fault features, subsequently utilized for accurate diagnosis of any propulsion faults. The effectiveness of the proposed SEResNet was validated through its application to an actual experimental AUV, with comparison against the state-of-the-arts. The results reveal that the present SEResNet outperforms all other comparison methods in terms of diagnosis performance for AUV propulsion faults.

This work presents an investigation of robotic technologies' effectiveness in construction activities. Sixty-four highly relevant publications were identified from the database. By systematically reviewing the publications, the secondary data that are of interest to the review theme were retrieved and further evaluated. It is found that robotic technologies for automated construction is a growing field, where the taxonomy of robot was reflected in a diversified manner in the existing studies, ranging from the muscular guy—robotic manipulator—to the dexterous ones—unmanned aerial vehicle, autonomous mobile robot, automated guided vehicle, autonomous construction machinery and quadruped robot. In addition, the existing studies have provided substantial evidence to reveal the robotic technologies' effectiveness against traditional human methods in construction scenarios, and the measures for effectiveness consisted of productivity, precision, and success rate. With the evidence, it seems that the construction sector could benefit from robotic technologies to achieve intelligent workflows. Furthermore, based on the existing knowledge foundation in the current literature, a theoretical framework for future research direction is proposed. The framework envisages the integration of large models with construction robots to address operational inefficiencies, reduce costs, and simplify management.

This article investigates the control problem of unmanned surface vessels with sensor measurement sensitivity under deception attacks, and proposes a novel self-triggered adaptive neural control scheme under the backstepping design framework. To solve the control design problem of unknown time-varying gains caused by deception attacks and measurement sensitivity in kinematic and kinetic channels, the parameter adaptive and neural network technology are involved. In addition, to decrease actuator wear caused by the high-frequency wave and sensor measurement sensitivity and reduce the computational burden caused by continuous monitoring of the triggered condition, a self-triggered mechanism is constructed in the controller–actuator channel. Finally, a self-triggered adaptive neural control solution is proposed, which can guarantee that all signals in the whole closed-loop system are bounded by theoretical analysis. The effectiveness and superiority are verified by numerical simulations.

This paper presents the design, manufacturing and testing of a tail-sitting vertically takeoff and landing fixed-wing hybrid aerial underwater vehicle (HAUV) called Nezha-SeaDart. Nezha-SeaDart can vertically take off and land from ground and water, cruise in the air with lift generated by the wings, seamlessly cross the water–air interface and operate underwater like an autonomous underwater vehicle. Nezha-SeaDart underwent a 10-day field test in China's Thousand Islands Lake of Zhejiang Province, proving its ability to perform full cross-domain missions. This research has the following contributions to the field of HAUV. (i) A working prototype of vertical takeoff and landing tail-sitting HAUV with all basic functions verified and full mission cycle capability demonstrated in a field test. (ii) An HAUV that travels fast both in the air and underwater. (iii) An HAUV capable of autonomous and seamless water exit that does not rely on a dedicated propulsion system. (iv) A method of sizing the vehicle's wing and thrust considering aerial cruises, underwater operations, and seamless water exits.

Despite various proposed control schemes for uncertain bilateral teleoperation systems under time delays, optimally restricting the system's overshoot has remained an overlooked issue in this realm. For this aim, we propose two novel control architectures based on robust L₁ theory, entitled position-based adaptive L₁ controller and transparent adaptive L₁ controller, with the former focusing on position synchronization and the latter concerning system transparency. Since developing L₁-based controllers for nonlinear telerobotic systems encompassing uncertainty and round-trip delays puts significant theoretical challenges forward, the main contribution of this paper lies in advancing L₁ theory within the field of delayed teleoperation control. To formulate the theories, the asymptotic stability of the closed-loop system for each controller is first proved utilizing the Lyapunov method, followed by transformation, along with the L₁ performance criterion, into linear matrix inequalities. Ultimately, the control gains are attained by solving a convex optimization problem. The superiority of the designed controllers over a benchmark transparent controller for teleoperators is demonstrated via simulation. Furthermore, experimental tests on a two-degrees-of-freedom nonlinear telerobotic system validate the efficient performance of the proposed controllers.

The wall-climbing robot is a growing trend for robotized intelligent manufacturing of large and complex components in shipbuilding, petrochemical, and other industries, while several challenges remain to be solved, namely, low payload-to-weight ratio, poor surface adaptability, and ineffective traversal maneuverability, especially on noncontinuous surfaces with internal corners (non-CSIC). This paper designs a high payload-to-weight ratio wheeled wall-climbing robot which can travel non-CSIC effectively with a payload capacity of up to 75 kg, and it can carry a maximum load of 141.5 kg on a vertical wall. By introducing a semi-enclosed magnetic adhesion mechanism, the robot preserves a redundant magnetic adsorption ability despite the occurrence of significant gaps between localized body components and the wall surface. In addition, by ingeniously engineering a passive adaptive module into the robot, both the surface adaptability and crossability are enhanced without increasing the gap between the body and the wall, thereby ensuring the optimization of the adsorption force. Considering the payload capacity and diversity when climbing on vertical walls, inclined walls, ceilings, and internal corner transitions, control equations for internal corner transitions and comprehensive simulations of magnetic adsorption forces are performed using FEA tools. Finally, a functional prototype was developed for rigorous experimental testing, with the results confirming that the robot successfully meets the desired functionality and performance benchmarks.

The current mainstream approaches for plant organ counting are based on convolutional neural networks (CNNs), which have a solid local feature extraction capability. However, CNNs inherently have difficulties for robust global feature extraction due to limited receptive fields. Visual transformer (ViT) provides a new opportunity to complement CNNs' capability, and it can easily model global context. In this context, we propose a deep learning network based on a convolution-free ViT backbone (tea chrysanthemum-visual transformer [TC-ViT]) to achieve the accurate and real-time counting of TCs at their early flowering stage under unstructured environments. First, all cropped fixed-size original image patches are linearly projected into a one-dimensional vector sequence and fed into a progressive multiscale ViT backbone to capture multiple scaled feature sequences. Subsequently, the obtained feature sequences are reshaped into two-dimensional image features and using a multiscale perceptual field module as a regression head to detect the overall scale and density variance. The resulting model was tested on 400 field images in the collected TC test data set, showing that the proposed TC-ViT achieved the mean absolute error and mean square error of 12.32 and 15.06, with the inference speed of 27.36 FPS (512 × 512 image size) under the NVIDIA Tesla V100 GPU environment. It is also shown that light variation had the greatest effect on TC counting, whereas blurring had the least effect. This proposed method enables accurate counting for high-density and occlusion objects in field environments and this perception system could be deployed in a robotic platform for selective harvesting and flower phenotyping.

This paper presents a comprehensive study of dynamics identification-driven diving control for unmanned underwater vehicles (UUVs). Initially, a diving dynamics model of UUVs is established, serving as the foundation for the development of subsequent algorithms. A noise-reduction least squares (NRLS) algorithm is then derived for parameter identification, demonstrating convergence under measurement noise from a probabilistic perspective. A notable feature of this algorithm is its skill in correcting raw data, thereby improving parameter identification accuracy. Based on the identified model, an improved fast terminal sliding mode control (FTSMC) algorithm is introduced for diving control, consistently ensuring rapid convergence under 16 scenarios. Importantly, the proposed diving control algorithm effectively mitigates chattering by incorporating a dedicated filter, adaptively adjusting the switching gain, and substituting saturation function for sign function. Through experimental validation, the NRLS algorithm's advantage over the traditional least squares method becomes evident, with depth errors consistently below 3.5 cm. This indicates that the identified model closely aligns with the actual model, showcasing a commendable fit. Additionally, when compared to the traditional sliding mode controller and the proportional-integral-derivative algorithm, the FTSMC algorithm has superior performance, as indicated by a mean absolute percentage error consistently below 4%.