Journal of Field Robotics最新文献

{"title":"Design, Development, Integration and Field Evaluation of a Dual Robotic Arm Mango Harvesting Robot","authors":"Chenghai Yin,&nbsp;Jinyang Huang,&nbsp;Yuyang Xia,&nbsp;Hongcheng Zheng,&nbsp;Wei Fu,&nbsp;Bin Zhang","doi":"10.1002/rob.22580","DOIUrl":"https://doi.org/10.1002/rob.22580","url":null,"abstract":"<div>\u0000 \u0000 <p>To solve the problems of high labor intensity and high cost when picking mango manually, a mango picking robot system with dual robotic arms was developed to realize automatic mango picking. Firstly, the YOLOMS network was used to realize the 3D localization of picking points for single mangoes and mango clusters in unstructured environments. Secondly, a new “shearing and grasping integrated” end-effector for non-destructive harvesting of mangoes was designed. Then, a task division method for the workspace of the dual robotic arm harvesting robot was proposed to minimize the likelihood of collisions between dual arms. Additionally, a depth-first picking strategy was introduced to reduce fruit damage and enhance the success rates of picking mangoes from layered canopies. Finally, a mango harvesting robotic system with dual arms was developed and integrated. The performance of the system was evaluated by field mango picking experiments. The results showed that the average recognition rate and planning success rate of the harvesting robot were 83.94% and 98.45%, respectively. In addition, the average harvesting success rate of the robot was 73.92%, and the average single-fruit harvesting time was 8.93 s. Compared with the robot with single arm, the harvesting time was reduced by 48.38%, which indicated that the harvesting efficiency of the dual robotic arm harvesting robot was significantly improved. The average collision-free harvesting rate with the addition of the depth-first harvesting strategy was 91.68%, which verified the rationality and effectiveness of the dual robotic arm collaborative mango harvesting robotic system. The results provide technical support for automated mango harvesting.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3705-3725"},"PeriodicalIF":5.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129220","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 Novel Review on Quadruped Robots Design Variants, Gait Modulation, and Motion Planning Schemes","authors":"S. A. Azeez,&nbsp;Ravi Kumar Mandava,&nbsp;Nenavath Srinivas Naik","doi":"10.1002/rob.22575","DOIUrl":"https://doi.org/10.1002/rob.22575","url":null,"abstract":"<div>\u0000 \u0000 <p>Quadruped robots are gaining more importance among researchers due to their adaptability to complex terrains, though their stability handling is complex. Quadruped robots are one of the best kinds of legged robotic systems with regard to their framework and movement flexibility. For making quadrupeds fully autonomous, it is important to concentrate on their modeling, modulation, and maneuverability. This article sheds light on quadruped robots, starting from the early-stage developments of TITAN series quadrupeds to current progress based on variations of design, gait analysis, control, and motion planning, so as to provide directions for future researchers to develop efficient quadrupeds. Furthermore, we have made a comparative analysis on various aspects, including various mobile robots, material specifications, leg structure and topology, crucial modeling parameters, sensors and actuation systems, as well as traditional and advanced algorithms. This work explores gait analysis, followed by numerous researchers concentrating on various gait stability challenges; in addition, it presents the studies on control strategies of quadruped robots based on a PID controller, neural network controller, fuzzy logic controller, delayed feedback controller, hybrid controller, and other peculiar controllers. Furthermore, the representative explorations on motion planning algorithms of quadruped robots have been done. To conclude, this paper provides an overview of the various variants of quadrupeds, focusing on important aspects, followed by an analysis of pivot features and providing solutions to current challenges and issues so as to help future researchers in the field identify key areas of research.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3615-3693"},"PeriodicalIF":5.2,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129181","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":"Design and Analysis of New Obstacle Avoidance Scheme With Dimensionality Reduction for Motion Planning of Redundant Robot Manipulators","authors":"Dongsheng Guo,&nbsp;Qu Li,&nbsp;Naimeng Cang,&nbsp;Yilin Yu,&nbsp;Weidong Zhang,&nbsp;Weibing Li","doi":"10.1002/rob.22594","DOIUrl":"https://doi.org/10.1002/rob.22594","url":null,"abstract":"<div>\u0000 \u0000 <p>Obstacle avoidance (OA) is an important issue in the motion planning of redundant robot manipulators. Various effective OA schemes have been reported, but they may suffer from a large amount of calculation for the situation of multiple obstacles and/or complex-shaped obstacles. In this paper, to address the aforementioned limitation, a new OA scheme with dimensionality reduction is proposed and studied for redundant robot manipulators. Specifically, by combining robot kinematics and geometry, a typical inequality criterion for OA is designed, which can reduce the calculation for an obstacle point from the general three dimensions to one dimension. Such an inequality criterion is further aided by (1) the dynamic selection for the situation of a large number of obstacle points, and (2) the feature extraction for the situation of complex-shaped obstacles. With the OA environment optimized and the obstacles' dimension limited, the computational efficiency of generating the inequality criterion for specific scenarios can thus be improved. By incorporating the inequality criterion and the joint physical constraint, the new dimensionality-reduction OA (DROA) scheme is developed for redundant robot manipulators. Such a DROA scheme is depicted as a quadratic program that is solved by the reinforcement learning method. Simulation and experiment results under the PA10 robot manipulator verify the efficacy and applicability of the proposed DROA scheme.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3694-3704"},"PeriodicalIF":5.2,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129182","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":"Slip-Compensation-Based Path Tracking Control for Tracked Robots Using VBEKF and Backstepping Control","authors":"Xin Zhao,&nbsp;Yuming Chen,&nbsp;En Lu,&nbsp;Wu Tao,&nbsp;Hui Wang,&nbsp;Qian Zhang,&nbsp;Rongbing Fu","doi":"10.1002/rob.22593","DOIUrl":"https://doi.org/10.1002/rob.22593","url":null,"abstract":"<div>\u0000 \u0000 <p>Tracked robots are widely used in agriculture, military, mining, and other fields. During the traveling process of tracked robots, the complex interaction between the tracks and the ground causes slip, which leads to problems such as low path tracking accuracy and poor control stability. To solve this thorny problem, quantitative control compensation parameters are generally obtained through experiments, or extended Kalman filter (EKF) is designed based on Gaussian noise to estimate slip parameters during motion. However, the sensor measurement noise of tracked robots usually exhibits a non-Gaussian distribution in uneven terrain and variable soil conditions. Under such circumstances, these methods exhibit larger errors and demonstrate inadequate adaptability. Therefore, this paper proposes a variational Bayesian EKF (VBEKF) algorithm for slip parameters estimation, and designs a slip compensation path tracking controller to improve the accuracy and adaptability of the control system of tracked robots under complex operating conditions. The main contributions of this paper are as follows: (1) The non-Gaussian noise was re-modeled using the Student's t-distribution, and combined with variational Bayesian, the VBEKF algorithm was designed. This algorithm can more accurately estimate the slip parameters between the tracks and soil under complex and varying operating conditions, demonstrating enhanced adaptability. (2) Based on the backstepping control principle, a path tracking controller with slip parameter compensation was designed for tracked robots. This controller dynamically adjusts its output control based on the estimated slip parameters to eliminate the impact of slip between the tracks and soil on path tracking accuracy. Finally, the effectiveness of the method was demonstrated through simulations and experiments. This study can improve the adaptability and stability of tracked robots under complex and variable operating conditions, ensuring accurate and rapid task completion, and has broad application prospects.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3600-3614"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129156","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":"Air-to-Ground Target Detection and Tracking Based on Dual-Stream Fusion of Unmanned Aerial Vehicle","authors":"Chuanyun Wang,&nbsp;Jianqi Yang,&nbsp;Dongdong Sun,&nbsp;Qian Gao,&nbsp;Qiong Liu,&nbsp;Tian Wang,&nbsp;Anqi Hu,&nbsp;Linlin Wang","doi":"10.1002/rob.22592","DOIUrl":"https://doi.org/10.1002/rob.22592","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Both visible and infrared images are important sources of intelligence information on the battlefield, and air-to-ground reconnaissance by UAV is an important means to obtain intelligence. However, there are great challenges in ground target detection and tracking, especially in complex battlefield environments. Aiming at the problem of insufficient accuracy of target detection by a single type of sensor in the battlefield environment at this stage, a target detection method by fusion of visible and infrared images is proposed in this paper, which is called ReconnaissanceFusion-YOLO (RF-YOLO), and with the help of infrared imagery, it can effectively improve the accuracy of target detection in the case of insufficient light. The performance of target detection in the battlefield is significantly improved by introducing two key innovative modules: dual feature fusion (DFF) module and feature fusion corrector (FFC) module. The DFF module enhances multi-channel feature fusion through a novel concatenation and channel-wise attention mechanism, while the FFC module performs feature correction between parallel streams using spatial and channel-wise weights, addressing noise and uncertainty in different modalities. These modules are integrated on top of a dual-stream YOLO architecture, allowing for effective fusion of visible and infrared information. RF-YOLO was trained and evaluated using the FLIR data set, containing 5142 pairs of strictly aligned visible and infrared images. Results demonstrate that RF-YOLO significantly outperforms benchmark networks in terms of robustness requirements. Specifically, the large model of RF-YOLO achieves an mAP of 0.831, which is a significant improvement compared to the YOLOv5l inf benchmark's 0.739. This represents an improvement of over 12% in detection accuracy. Additionally, RF-YOLO offers a Nano version that balances accuracy and speed. The Nano version achieves an mAP of 0.765, while maintaining a model size of only 11.5 MB, making it suitable for deployment on UAV edge computing devices with limited resources. To validate the practical applicability of our approach, this paper successfully implements target detection and tracking on a real UAV's edge computing device using the ROS system and SiameseRPN, combined with the proposed RF-YOLO. Real-world flight tests were conducted on an internal playground, demonstrating the effectiveness of our method in actual UAV applications. The system achieved a processing rate of approximately 10 fps at 640 × 640 resolution on an NVIDIA TX2 edge computing device, showcasing its real-time performance capability in practical scenarios. This study contributes to enhancing UAV-based battlefield reconnaissance capabilities by improving the accuracy and robustness of target detection and tracking in complex environments. The proposed RF-YOLO method, along with its successful implementation on a real UAV platform, provides a promising solution for adva","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3582-3599"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129159","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":"PR2: A Physics- and Photo-Realistic Humanoid Testbed With Pilot Study in Competition","authors":"Hangxin Liu,&nbsp;Qi Xie,&nbsp;Zeyu Zhang,&nbsp;Tao Yuan,&nbsp;Song Wang,&nbsp;Zaijin Wang,&nbsp;Xiaokun Leng,&nbsp;Lining Sun,&nbsp;Jingwen Zhang,&nbsp;Zhicheng He,&nbsp;Yao Su","doi":"10.1002/rob.22588","DOIUrl":"https://doi.org/10.1002/rob.22588","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents the development of a <b>P</b>hysics-<b>r</b>ealistic and <b>P</b>hoto-<b>r</b>ealistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 offers high-quality scene rendering and robot dynamic simulation, enabling (i) the creation of diverse scenes using various digital assets, (ii) the integration of advanced perception or foundation models, and (iii) the implementation of planning and control algorithms for dynamic humanoid robot behaviors based on environmental feedback. The beta version of PR2 has been deployed for the simulation track of a nationwide full-size humanoid robot competition for college students, attracting 137 teams and over 400 participants within 4 months. This competition covered traditional tasks in bipedal walking, as well as novel challenges in loco-manipulation and language-instruction-based object search, marking a first for public college robotics competitions. A retrospective analysis of the competition suggests that future events should emphasize the integration of locomotion with manipulation and perception. By making the PR2 testbed publicly available at https://github.com/pr2-humanoid/PR2-Platform, we aim to further advance education and training in humanoid robotics. Video demonstration: https://pr2-humanoid.github.io/.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3545-3560"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129160","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":"Research on Steering Path Tracking Performance of Articulated Quad-Tracked Vehicle Based on Fuzzy PID Control","authors":"Shuai Wang,&nbsp;Shibo Liu,&nbsp;Guoqiang Wang,&nbsp;Chaoyang Ma,&nbsp;Jianbo Guo,&nbsp;Huanyu Zhao","doi":"10.1002/rob.22589","DOIUrl":"https://doi.org/10.1002/rob.22589","url":null,"abstract":"<div>\u0000 \u0000 <p>The articulated quad-track vehicles exhibit excellent mobility and obstacle-crossing capabilities in outdoor environments, making them widely applicable in agriculture and military fields. Their steering control is a complex issue influenced by numerous factors. To reduce the computational complexity of the controller, achieve rapid system response, and simultaneously improve the stability and precision of the articulated quad-track vehicles during the steering control process, an optimal matching analysis is performed between the inner and outer track speed ratios and the deflection angles at the front and rear articulation points of the vehicle. By utilizing fuzzy proportional–integral–derivative control and visual navigation, a path-tracking control experimental platform for the articulated quad-track vehicle is designed. Through a combination of virtual prototype simulations and physical experiments, the distance deviation and heading angle deviation between the actual driving path of the virtual and experimental prototypes and the preset path are analyzed. The designed path–tracking control system can adjust the driving speeds of the left and right tracks and the articulation point deflection angle based on the preset driving path, enabling the vehicle to track the path. Under stable steering conditions, the distance deviation is within 0.1 m, and the heading angle deviation is within 6°, demonstrating excellent control performance.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3561-3581"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129157","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":"Design and Analysis of Double-Ring Robotic Coconut Tree Climber for Enhanced Performance","authors":"Sakthiprasad Kuttankulangara Manoharan,&nbsp;Rajesh Kannan Megalingam,&nbsp;Shree Rajesh Raagul Vadivel,&nbsp;Brindha Shaju,&nbsp;Dhananjay Raghavan","doi":"10.1002/rob.22560","DOIUrl":"https://doi.org/10.1002/rob.22560","url":null,"abstract":"<div>\u0000 \u0000 <p>In the dynamic field of agricultural technology, the development of coconut tree climbers exemplifies significant progress in addressing the challenges of efficient and safe coconut harvesting. Designing an unmanned coconut tree climber robot is complex due to the unpredictable structures of the coconut tree trunk and crown. Key challenges include developing a climbing mechanism, ensuring smooth ascents and descents, managing payload stability, and designing an effective harvester for coconut bunches, all of which impact the robot's overall performance. This paper introduces a novel design featuring a double-ring structure for the climber robot, aimed at enhancing its performance. The study includes a comprehensive static analysis to determine the average range of torque values for the actuators. Dynamic and kinematic analyses are conducted to establish essential relationships that predict the robot's characteristics before testing. A four-degree-of-freedom manipulator is used as the harvester. The proposed methodology was tested on a coconut tree trunk in a lab setup and field conditions across 10 different coconut trees. Real-time data collected during these tests were validated against predictions made through simulations before experimentation. The analyses, including theoretical analysis, simulation outcomes, and experimental test setups, conclusively demonstrate that the proposed structure maintains consistent stability throughout the climbing process, even on trees with varying inclinations and trunk radii relative to height. The success rates of the double-ring setup consistently surpass those of the single-ring configuration, with success rates ranging from 80% to 100% for the single ring and 100% for the double-ring setup.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3500-3514"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129161","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":"Predictive Obstacle Avoidance Algorithm for Under-Actuated Unmanned Surface Vehicle Under Disturbances via Reinforcement Learning","authors":"Kefan Jin,&nbsp;Zhe Liu,&nbsp;Jian Wang","doi":"10.1002/rob.22554","DOIUrl":"https://doi.org/10.1002/rob.22554","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the growing complexity of diverse maritime tasks, underactuated unmanned surface vehicle (USV) has become a research hotspot. The rapid development of deep reinforcement learning (DRL) technology has brought forth a novel approach for the USV autonomous control, rendering unnecessary the dynamical modeling of the target USV. To further improve the USV collision avoidance performance against maritime disturbances, this paper presents a predictive reinforcement learning method for USV obstacle avoidance control. A prediction module is designed to generate latent features that depict environmental states. After that, the prediction feature is provided for a DRL-based policy module to produce an action distribution for the underactuated unmanned surface vehicle. The proposed method in this paper can enable the USV avoid obstacle and reach the destination solely based on its local observational information, without relying on prior global information. Simulation and physical experiments have demonstrated that, compared to general DRL methods, the proposed method exhibits stronger robustness to environmental disturbances, enabling the USV to reach the destination while avoid the obstacle.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3482-3499"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129155","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":"Advances and Trends in Terrain Classification Methods for Off-Road Perception","authors":"Tanzila Arafin,&nbsp;Anwar Hosen,&nbsp;Zoran Najdovski,&nbsp;Lei Wei,&nbsp;Mohammad Rokonuzzaman,&nbsp;Michael Johnstone","doi":"10.1002/rob.22586","DOIUrl":"https://doi.org/10.1002/rob.22586","url":null,"abstract":"<p>Off-road autonomous vehicles (OAVs) are becoming increasingly popular for navigating challenging environments in agriculture, military, and exploration applications. These vehicles face unique challenges, such as unpredictable terrain, dynamic obstacles, and varying environmental conditions. Therefore, it is essential to have an efficient terrain classification system to ensure safe and efficient operation of OAVs. This paper provides an overview of recent advances and emerging trends in off-road terrain classification methods. Through a comprehensive literature review, this study explores the use of sensor modalities and techniques that leverage both appearance and geometry of the terrain for classification tasks. The study discusses learning-based approaches, particularly deep learning, and highlights the integration of multiple sensor modalities through hybrid multimodal techniques. Finally, this study reviews the available off-road datasets and explores the use cases and applications of terrain classification across various autonomous domains. Given the rapid advancements in terrain classification, this paper organizes and surveys to provide a comprehensive overview. By offering a structured review of the current landscape, this paper significantly enhances our understanding of terrain classification in unstructured environments, while also highlighting important areas for future research, particularly in deep-learning-based advancements.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3515-3544"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129158","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}