Journal of Field Robotics最新文献

{"title":"Autonomous navigation method based on RGB-D camera for a crop phenotyping robot","authors":"Meng Yang,&nbsp;Chenglong Huang,&nbsp;Zhengda Li,&nbsp;Yang Shao,&nbsp;Jinzhan Yuan,&nbsp;Wanneng Yang,&nbsp;Peng Song","doi":"10.1002/rob.22379","DOIUrl":"10.1002/rob.22379","url":null,"abstract":"<p>Phenotyping robots have the potential to obtain crop phenotypic traits on a large scale with high throughput. Autonomous navigation technology for phenotyping robots can significantly improve the efficiency of phenotypic traits collection. This study developed an autonomous navigation method utilizing an RGB-D camera, specifically designed for phenotyping robots in field environments. The PP-LiteSeg semantic segmentation model was employed due to its real-time and accurate segmentation capabilities, enabling the distinction of crop areas in images captured by the RGB-D camera. Navigation feature points were extracted from these segmented areas, with their three-dimensional coordinates determined from pixel and depth information, facilitating the computation of angle deviation (<i>α</i>) and lateral deviation (<i>d</i>). Fuzzy controllers were designed with <i>α</i> and <i>d</i> as inputs for real-time deviation correction during the walking of phenotyping robot. Additionally, the method includes end-of-row recognition and row spacing calculation, based on both visible and depth data, enabling automatic turning and row transition. The experimental results showed that the adopted PP-LiteSeg semantic segmentation model had a testing accuracy of 95.379% and a mean intersection over union of 90.615%. The robot's navigation demonstrated an average walking deviation of 1.33 cm, with a maximum of 3.82 cm. Additionally, the average error in row spacing measurement was 2.71 cm, while the success rate of row transition at the end of the row was 100%. These findings indicate that the proposed method provides effective support for the autonomous operation of phenotyping robots.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2663-2675"},"PeriodicalIF":4.2,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and movement mechanism analysis of a multiple degree of freedom bionic crocodile robot based on the characteristic of “death roll”","authors":"Chujun Liu,&nbsp;Jingwei Wang,&nbsp;Zhongyang Liu,&nbsp;Zejia Zhao,&nbsp;Guoqing Zhang","doi":"10.1002/rob.22380","DOIUrl":"10.1002/rob.22380","url":null,"abstract":"<p>This paper introduces a multi-degree of freedom bionic crocodile robot designed to tackle the challenge of cleaning pollutants and debris from the surfaces of narrow, shallow rivers. The robot mimics the “death roll” motion of crocodiles which is a technique used for object disintegration. First, the design incorporated a swinging tail mechanism using a multi-section oscillating guide-bar mechanism. By analyzing three-, four-, and five-section tail structures, the four-section tail was identified as the most effective structure, offering optimal strength and swing amplitude. Each section of the tail can reach maximum swing angles of 8.05°, 20.95°, 35.09°, and 43.84°, respectively, under a single motor's drive. Next, the robotic legs were designed with a double parallelogram mechanism, facilitating both crawling and retracting movements. In addition, the mouth employed a double-rocker mechanism for efficient closure and locking, achieving an average torque of 5.69 N m with a motor torque of 3.92 N m. Moreover, the robotic body was designed with upper and lower segment structures and waterproofing function was also considered. Besides, the kinematic mechanism and mechanical properties of the bionic crocodile structure were analyzed from the perspectives of modeling and field tests. The results demonstrated an exceptional kinematic performance of the bionic crocodile robot, effectively replicating the authentic movement characteristics of a crocodile.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2650-2662"},"PeriodicalIF":4.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MPC-based cooperative multiagent search for multiple targets using a Bayesian framework","authors":"Hu Xiao,&nbsp;Rongxin Cui,&nbsp;Demin Xu,&nbsp;Yanran Li","doi":"10.1002/rob.22382","DOIUrl":"10.1002/rob.22382","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2630-2649"},"PeriodicalIF":4.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cable-driven underwater robotic system for delicate manipulation of marine biology samples","authors":"Mahmoud Zarebidoki,&nbsp;Jaspreet Singh Dhupia,&nbsp;Minas Liarokapis,&nbsp;Weiliang Xu","doi":"10.1002/rob.22381","DOIUrl":"https://doi.org/10.1002/rob.22381","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2615-2629"},"PeriodicalIF":4.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UP-GAN: Channel-spatial attention-based progressive generative adversarial network for underwater image enhancement","authors":"Ning Wang,&nbsp;Yanzheng Chen,&nbsp;Yi Wei,&nbsp;Tingkai Chen,&nbsp;Hamid Reza Karimi","doi":"10.1002/rob.22378","DOIUrl":"10.1002/rob.22378","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2597-2614"},"PeriodicalIF":4.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mountain search and recovery: An unmanned aerial vehicle deployment case study and analysis","authors":"Nathan L. Schomer,&nbsp;Julie A. Adams","doi":"10.1002/rob.22376","DOIUrl":"10.1002/rob.22376","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2583-2596"},"PeriodicalIF":4.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A selective harvesting robot for cherry tomatoes: Design, development, field evaluation analysis","authors":"Jiacheng Rong,&nbsp;Lin Hu,&nbsp;Hui Zhou,&nbsp;Guanglin Dai,&nbsp;Ting Yuan,&nbsp;Pengbo Wang","doi":"10.1002/rob.22377","DOIUrl":"10.1002/rob.22377","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2564-2582"},"PeriodicalIF":4.2,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pose-graph underwater simultaneous localization and mapping for autonomous monitoring and 3D reconstruction by means of optical and acoustic sensors","authors":"Alessandro Bucci,&nbsp;Alessandro Ridolfi,&nbsp;Benedetto Allotta","doi":"10.1002/rob.22375","DOIUrl":"10.1002/rob.22375","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2543-2563"},"PeriodicalIF":4.2,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization-based motion planning for autonomous agricultural vehicles turning in constrained headlands","authors":"Chen Peng,&nbsp;Peng Wei,&nbsp;Zhenghao Fei,&nbsp;Yuankai Zhu,&nbsp;Stavros G. Vougioukas","doi":"10.1002/rob.22374","DOIUrl":"https://doi.org/10.1002/rob.22374","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1984-2008"},"PeriodicalIF":4.2,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional neural networks to classify human stress that occurs during in-field sugarcane harvesting: A case study","authors":"Rajesh U. Modi,&nbsp;Sukhbir Singh,&nbsp;Akhilesh K. Singh,&nbsp;Vallokkunnel A. Blessy","doi":"10.1002/rob.22373","DOIUrl":"10.1002/rob.22373","url":null,"abstract":"<p>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.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2530-2542"},"PeriodicalIF":4.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}