Journal of Field Robotics最新文献

This paper presents a multiagent cooperative search algorithm for identifying an unknown number of targets. The objective is to determine a collection of observation points and corresponding safe paths for agents, which involves balancing the detection time and the number of targets searched. A Bayesian framework is used to update the local probability density function of the targets when the agents obtain information. We utilize model predictive control and establish utility functions based on the detection probability and decrease in information entropy. A target detection algorithm is implemented to verify the target based on minimum-risk Bayesian decision-making. Then, we improve the search algorithm with the target detection algorithm. Several simulations demonstrate that compared with other existing approaches, the proposed approach can reduce the time needed to detect targets and the number of targets searched. We establish an experimental platform with three unmanned aerial vehicles. The simulation and experimental results verify the satisfactory performance of our algorithm.

Underwater robotic systems have the potential to assist and complement humans in dangerous or remote environments, such as in the monitoring, sampling, or manipulation of sensitive underwater species. Here we present the design, modeling, and development of an underwater manipulator (UM) with a lightweight cable-driven structure that allows for delicate deep-sea reef sampling. The compact and lightweight design of the UM and gripper decreases the coupling effect between the UM and the underwater vehicle (UV) significantly. The UM and gripper are equipped with force sensors, enabling them for soft and sensitive object manipulation and grasping. The accurate force exertion capabilities of the UM ensure efficient operation in the process of localization and approaching reef samples, such as the corals and sponges. The active force control of the tendon-driven gripper ensures gentle/delicate grasping, handling, and transporting of the marine samples without damaging their tissues. A complete simulation of the UM is provided for deriving the required specifications of actuators and sensors to be compatible with the UVs with a speed range of 1–4 Knots. The system's performance for accurate trajectory tracking and delicate grasping of two different types of underwater species (a sponge skeleton and a Neptune's necklace seaweed) is verified using a model-free robust-adaptive position/force controller.

Focusing on severe color deviation, low brightness, and mixed noise caused by inherent scattering and light attenuation effects within underwater environments, an underwater-attention progressive generative adversarial network (UP-GAN) is innovated for underwater image enhancement (UIE). Salient contributions are as follows: (1) By elaborately devising an underwater background light estimation module via an underwater imaging model, the degradation mechanism can be sufficiently integrated to fuse prior information, which in turn saves computational burden on subsequent enhancement; (2) to suppress mixed noise and enhance foreground, simultaneously, an underwater dual-attention module is created to fertilize skip connection from channel and spatial aspects, thereby getting rid of noise amplification within the UIE; and (3) by systematically combining with spatial consistency, exposure control, color constancy, color relative dispersion losses, the entire UP-GAN framework is skillfully optimized by taking into account multidegradation factors. Comprehensive experiments conducted on the UIEB data set demonstrate the effectiveness and superiority of the proposed UP-GAN in terms of both subjective and objective aspects.

Mountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low-cost, consumer-grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability-prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert-informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state-of-the-art robotics and MSAR.

With the aging population and increasing labor costs, traditional manual harvesting methods have become less economically efficient. Consequently, research into fully automated harvesting using selective harvesting robots for cherry tomatoes has become a hot topic. However, most of the current research is focused on individual harvesting of large tomatoes, and there is less research on the development of complete systems for harvesting cherry tomatoes in clusters. The purpose of this study is to develop a harvesting robot system capable of picking tomato clusters by cutting their fruit-bearing pedicels and to evaluate the robot prototype in real greenhouse environments. First, to enhance the grasping stability, a novel end-effector was designed. This end-effector utilizes a cam mechanism to achieve asynchronous actions of cutting and grasping with only one power source. Subsequently, a visual perception system was developed to locate the cutting points of the pedicels. This system is divided into two parts: rough positioning of the fruits in the far-range view and accurate positioning of the cutting points of the pedicels in the close-range view. Furthermore, it possesses the capability to adaptively infer the approaching pose of the end-effector based on point cloud features extracted from fruit-bearing pedicels and stems. Finally, a prototype of the tomato-harvesting robot was assembled for field trials. The test results demonstrate that in tomato clusters with unobstructed pedicels, the localization success rates for the cutting points were 88.5% and 83.7% in the two greenhouses, respectively, while the harvesting success rates reached 57.7% and 55.4%, respectively. The average cycle time to harvest a tomato cluster was 24 s. The experimental results prove the potential for commercial application of the developed tomato-harvesting robot and through the analysis of failure cases, discuss directions for future work.

Modern mobile robots require precise and robust localization and navigation systems to achieve mission tasks correctly. In particular, in the underwater environment, where Global Navigation Satellite Systems cannot be exploited, the development of localization and navigation strategies becomes more challenging. Maximum A Posteriori (MAP) strategies have been analyzed and tested to increase navigation accuracy and take into account the entire history of the system state. In particular, a sensor fusion algorithm relying on a MAP technique for Simultaneous Localization and Mapping (SLAM) has been developed to fuse information coming from a monocular camera and a Doppler Velocity Log (DVL) and to consider the landmark points in the navigation framework. The proposed approach can guarantee to simultaneously locate the vehicle and map the surrounding environment with the information extracted from the images acquired by a bottom-looking optical camera. Optical sensors can provide constraints between the vehicle poses and the landmarks belonging to the observed scene. The DVL measurements have been employed to solve the unknown scale factor and to guarantee the correct vehicle localization even in the absence of visual features. Furthermore, to evaluate the mapping capabilities of the SLAM algorithm, the obtained point cloud is elaborated with a Poisson reconstruction method to obtain a smooth seabed surface. After validating the proposed solution through realistic simulations, an experimental campaign at sea was conducted in Stromboli Island (Messina), Italy, where both the navigation and the mapping performance have been evaluated.

Headland maneuvering is a crucial part of the field operations performed by autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial autoguidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries. Commercial orchards often contain narrow and irregularly shaped headlands, which may include static obstacles, rendering the task of planning a smooth and collision-free turning trajectory difficult. To address this challenge, we propose an optimization-based motion planning algorithm for headland turning under geometrical constraints imposed by headland geometry and obstacles. Our method models the headland and the AAV using convex polytopes as geometric primitives, and calculates optimal and collision-free turning trajectories in two stages. In the first stage, a coarse path is generated using either a classical pattern-based turning method or a directional graph-guided hybrid A* algorithm, depending on the complexity of the headland geometry. The second stage refines this coarse path by feeding it into a numerical optimizer, which considers the vehicle's kinematic, control, and collision-avoidance constraints to produce a feasible and smooth trajectory. We demonstrate the effectiveness of our algorithm by comparing it to the classical pattern-based method in various types of headlands. The results show that our optimization-based planner outperforms the classical planner in generating collision-free turning trajectories inside constrained headland spaces. Additionally, the trajectories generated by our planner respect the kinematic and control limits of the vehicle and, hence, are easier for a path-tracking controller to follow. In conclusion, our proposed approach successfully addresses complex motion planning problems in constrained headlands, making it a valuable contribution to the autonomous operation of AAVs, particularly in real-world orchard environments.

Assessing human stress in agriculture proves to be a complex and time-intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real-time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real-world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery-prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence-driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network-based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini-batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini-batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

This paper addresses the challenge of generating clear image frames and minimizing the loss of keyframes by a robot engaging in rapid large viewing angle motion. These issues often lead to detrimental consequences such as trajectory drifting and loss during the construction of curved motion trajectories. To tackle this, we proposed a novel visual simultaneous localization and mapping (SLAM) algorithm, TKO-SLAM, which is based on time-delay feature regression and keyframe position optimization. TKO-SLAM uses a multiscale recurrent neural network to rectify object deformation and image motion smear. This network effectively repairs the time-delay image features caused by the rapid movement of the robot, thereby enhancing visual clarity. Simultaneously, inspired by the keyframe selection strategy of the ORB-SLAM3 algorithm, we introduced a grayscale motion-based image processing method to supplement keyframes that may be omitted due to the robot's rapid large viewing angle motion. To further refine the algorithm, the time-delay feature regression image keyframes and adjacent secondary keyframes were used as dual measurement constraints to optimize camera poses and restore robot trajectories. The results of experiments on the benchmark RGB-D data set TUM and real-world scenarios show that TKO-SLAM algorithm achieves more than 10% better localization accuracy than the PKS-SLAM algorithm in the rapid large viewing angle motion scenario, and has advantages over the SOTA algorithms.

With technological advancement, the use of drones in delivery systems has become increasingly feasible. Many companies have developed rotary-wing drone (RWD) technologies for parcel delivery. At present, the limited endurance is the main disadvantage of RWD delivery. The energy consumption of RWDs must be carefully managed, and it is necessary to develop an effective energy-consumption model to support RWD flight planning. Because the interaction between the forces on the RWD and its flying environment is very complex, it is challenging to estimate accurately the RWD energy consumption. This study summarizes several energy-consumption models proposed in the literature, then we develop an RWD energy-consumption model (called the integrated model) based on analyzing the dynamic equilibrium of forces and power consumption in flight phases (including climb, descent, hover, and horizontal flight). Computational experiments involving several commercial RWDs indicate that the integrated model is more effective than several models in the literature. In the case where an RWD completed one flight segment, on average, 87.63% of the battery capacity was consumed in the horizontal flight phase. We also analyzed the effects of the total mass and horizontal airspeed on the RWD endurance and found that a larger mass corresponded to shorter endurance, and in the experimental range of the horizontal airspeed, a higher horizontal airspeed corresponded to longer endurance. Moreover, the total mass affected the RWD endurance more significantly than the horizontal airspeed.