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Littérature scientifique sur le sujet « Robot Networks »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Robot Networks"

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Kim, Jonghoek. "Workspace exploration and protection with multiple robots assisted by sensor networks." International Journal of Advanced Robotic Systems 15, no. 4 (2018): 172988141879217. http://dx.doi.org/10.1177/1729881418792170.

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This article introduces multi-robot strategies making multiple robots explore an unknown environment in a cooperative manner. Our exploration strategies do not require global localization of a robot or a node. Multiple robots build a Voronoi diagram as a topological map of the environment, while deploying sensor nodes which can sense and communicate. As the sensor network built by one robot meets the network built by another robot, both robots can exchange data with each other. The robots then use the merged sensor network to protect the environment. We introduce an intruder capture algorithm

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Kim, Jonghoek. "Constructing 3D Underwater Sensor Networks without Sensing Holes Utilizing Heterogeneous Underwater Robots." Applied Sciences 11, no. 9 (2021): 4293. http://dx.doi.org/10.3390/app11094293.

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This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no se

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Ravankar, Abhijeet, Ankit Ravankar, Yukinori Kobayashi, Yohei Hoshino, Chao-Chung Peng, and Michiko Watanabe. "Hitchhiking Based Symbiotic Multi-Robot Navigation in Sensor Networks." Robotics 7, no. 3 (2018): 37. http://dx.doi.org/10.3390/robotics7030037.

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Robot navigation is a complex process that involves real-time localization, obstacle avoidance, map update, control, and path planning. Thus, it is also a computationally expensive process, especially in multi-robot systems. This paper presents a cooperative multi-robot navigation scheme in which a robot can ‘hitchhike’ another robot, i.e., two robots going to the same (or close) destination navigate together in a leader–follower system assisted by visual servoing. Although such cooperative navigation has many benefits compared to traditional approaches with separate navigation, there are many

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Brecelj, Tilen, and Tadej Petrič. "Stable Heteroclinic Channel Networks for Physical Human–Humanoid Robot Collaboration." Sensors 23, no. 3 (2023): 1396. http://dx.doi.org/10.3390/s23031396.

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Human–robot collaboration is one of the most challenging fields in robotics, as robots must understand human intentions and suitably cooperate with them in the given circumstances. But although this is one of the most investigated research areas in robotics, it is still in its infancy. In this paper, human–robot collaboration is addressed by applying a phase state system, guided by stable heteroclinic channel networks, to a humanoid robot. The base mathematical model is first defined and illustrated on a simple three-state system. Further on, an eight-state system is applied to a humanoid robo

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Kim, Jonghoek. "Capturing intruders based on Voronoi diagrams assisted by information networks." International Journal of Advanced Robotic Systems 14, no. 1 (2017): 172988141668269. http://dx.doi.org/10.1177/1729881416682693.

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We consider a scenario of deploying multiple robots to capture all intruders in a cluttered workspace with many obstacles. Here, we say that a robot captures an intruder in the case where the intruder is within the maximum range of a weapon on the robot. All robots use the Voronoi diagram as the topological map of the workspace. Due to obstacles, intruders are confined to move along a passage between obstacles. Suppose the weapons on every robot are powerful enough to cover a passage in the workspace. Then, we can consider a simplified scenario such that robots and intruders are restricted to

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Chen, Dechao, Shuai Li, and Qing Wu. "A Review on Neural Dynamics for Robot Autonomy." International Journal of Robotics and Control 1, no. 1 (2018): 20. http://dx.doi.org/10.5430/ijrc.v1n1p20.

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Exploiting neural networks to solve control problems of robots is becoming commonly and effectively in academia and engineering. Due to the remarkable features like distributed storage, parallelism, easy implementation by hardware, adaptive self-learning capability, and free of off-line training, the solutions of neural networks break the bottlenecks of serial-processing strategies and methods, and serve as significant alternatives for robotic engineers and researchers. Especially, various types and branches of recurrent neural networks (RNNs) have been sequentially developed since the seminal

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Zhang, Jin Xue, and Hai Zhu Pan. "A Task of Miniature Mobile Robot Learning for Obstacle Avoidance through Neural Networks." Applied Mechanics and Materials 151 (January 2012): 498–502. http://dx.doi.org/10.4028/www.scientific.net/amm.151.498.

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This paper is concerned with Q-learning , a very popular algorithm for reinforcement learning ,for obstacle avoidance through neural networks. The principle tells that the focus always must be on both ecological nice tasks and behaviours when designing on robot. Many robot systems have used behavior-based systems since the 1980’s.In this paper, the Khepera robot is trained through the proposed algorithm of Q-learning using the neural networks for the task of obstacle avoidance. In experiments with real and simulated robots, the neural networks approach can be used to make it possible for Q-lea

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Yuschenko, A. S., and Yin Shuai. "Dialogue Control of Collaborative Robots Based on Artifi cial Neural Networks." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 11 (2021): 567–76. http://dx.doi.org/10.17587/mau.22.567-576.

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Collaborative robotics progress is based on the possibility to apply robots to the wide range activity of peoples. Now the user can control the robot without any special knowledge in robotics and safe. The price of such possibilities is complication of control system of robot which now has to aquire an opportunity of autonomous behavior under human’s control, using the necessary sensors and elements of artificial intelligence. In our research we suppose the collaborative robot as mobile robotic device possible to fulfil some work under the human’s speech demands not only in the same space with

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Tezuka, Hirohisa, Norifumi Katafuchi, Yukihiro Nakamura, et al. "Robot Platform Architecture for Information Sharing and Collaboration Among Multiple Networked Robots." Journal of Robotics and Mechatronics 18, no. 3 (2006): 325–32. http://dx.doi.org/10.20965/jrm.2006.p0325.

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We present a framework for creating an open platform where a variety of robots collaborate via communication networks to provide us with our daily life support. We describe the architecture of the network robot platform, which consists of a connection unit to connect robots to the platform, an area management gateway to manage multiple robot resources, and a robot-user interaction database to share user, robot, and service information, to provide advanced services through multiple robot collaboration. We confirmed its functions using an announcement service as an example of application on the

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Phan Bui, Khoi, Giang Nguyen Truong, and Dat Nguyen Ngoc. "GCTD3: Modeling of Bipedal Locomotion by Combination of TD3 Algorithms and Graph Convolutional Network." Applied Sciences 12, no. 6 (2022): 2948. http://dx.doi.org/10.3390/app12062948.

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In recent years, there has been a lot of research using reinforcement learning algorithms to train 2-legged robots to move, but there are still many challenges. The authors propose the GCTD3 method, which takes the idea of using Graph Convolutional Networks to represent the kinematic link features of the robot, and combines this with the Twin-Delayed Deep Deterministic Policy Gradient algorithm to train the robot to move. Graph Convolutional Networks are very effective in graph-structured problems such as the connection of the joints of the human-like robots. The GCTD3 method shows better resu

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Thèses sur le sujet "Robot Networks"

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Bishop, Russell C. "A Method for Generating Robot Control Systems." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1222394834.

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Smith, Brian Stephen. "Automatic coordination and deployment of multi-robot systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28248.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Dr. Magnus Egerstedt; Committee Co-Chair: Dr. Ayanna Howard; Committee Member: Dr. David Taylor; Committee Member: Dr. Frank Dellaert; Committee Member: Dr. Ian Akyildiz; Committee Member: Dr. Jeff Shamma.

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Zhong, Xiaolin. "Robot calibration using artificial neural networks." Thesis, Edinburgh Napier University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295387.

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Cupitt, Rebekah. "We Are the Robots : An anthropological perspective on human-robot interaction." Thesis, Socialantropologiska institutionen, Stockholms universitetet, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-49557.

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Abstract How do we cope with technology today? We are surrounded by machines, computers and technological devices from mobile phones to automated check- outs. These types of machines are no longer exotic in Sweden where today the average person is usually fluent in their use. But do we really have an understanding of how these objects work, is understanding necessary and how do we cope when our knowledge is lacking? This thesis is intended as an introduction to an anthropological way of look- ing at strategies people develop for understanding, using and interacting with technological objects,

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Rush, Jonathan Reginald. "Evolving cellular neural networks for autonomous robot control." Thesis, University of Salford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308293.

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rahman, md mahbubur. "Efficient Mission Planning for Robot Networks in Communication Constrained Environments." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3484.

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Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailabili

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Vestheim, Siri. "Pruning of RBF Networks in Robot Manipulator Learning Control." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18591.

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Radial Basis Function Neural Networks are well suited for learning the systemdynamics of a robot manipulator and implementation of these networks in thecontrol scheme for a manipulator is a good way to deal with the system uncertaintiesand modeling errors which often occur. The problem with RBF networkshowever is to nd a network with suitable size, not too computational demandingand able to give accurate approximations. In general two methods for creating anappropriate RBF network has been developed, 1) Growing and 2) Pruning.In this report two dierent pruning methods which are suitable for us

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Wei, Zhouping, University of Western Sydney, and of Mechatronic Computer and Electrical Engineering School. "Model predictive control of a robot using neural networks." THESIS_XXX_MCEE_Wei_Z.xml, 1999. http://handle.uws.edu.au:8081/1959.7/323.

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The aim of the thesis is to develop a model-based control strategy, namely, the Model Predictive Control (MPC) method, for robot position control using artificial neural networks. MPC is primarily developed for process control. Therefore its application in robot control has been less reported. In addition, conventional MPC uses linear model of the system for prediction which leads to inaccuracy for highly non-linear systems, such as robot. In this thesis a simulation model of a modified PUMA robot is constructed. This model is built using both MATLAB/SIMULINK and FORTRAN languages. In this mod

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Künzle, Philippe. "Building topological maps for robot navigation using neural networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82266.

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Robots carrying tasks in an unknown environment often need to build a map in order to be able to navigate. One approach is to create a detailed map of the environment containing the position of obstacles. But this option can use a large amount of memory, especially if the environment is large. Another approach, closer to how people build a mental map, is the topological map. A topological map contains only places that are easy to recognize (landmarks) and links them together.<br>In this thesis, we explore the issue of creating a topological map from range data. A robot in a simulated en

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Xie, Wei. "KNN Query Processing in Wireless Sensor and Robot Networks." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30673.

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In Wireless Sensor and Robot Networks (WSRNs), static sensors report event information to one of the robots. In the k nearest neighbour query processing problem in WSRNs, the robot receives event report needs to find exact k nearest robots (KNN) to react to the event, among those connected to it. We are interested in localized solutions, which avoid message flooding to the whole network. Several existing methods restrict the search within a predetermined boundary. Some network density-based estimation algorithms were proposed but they either result in large message transmission or require the

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Livres sur le sujet "Robot Networks"

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Pomerleau, Dean A. Neural network perception for mobile robot guidance. Kluwer Academic Publishers, 1993.

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Li, Shuai, Long Jin, and Mohammed Aquil Mirza. Kinematic Control of Redundant Robot Arms Using Neural Networks. John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119557005.

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Li, Shuai, and Yinyan Zhang. Neural Networks for Cooperative Control of Multiple Robot Arms. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7037-2.

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Suresh, Jagannathan, and Yeşildirek A, eds. Neural network control of robot manipulators and nonlinear systems. Taylor & Francis, 1999.

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Veloso, Manuela. RoboCup-99: Robot Soccer World Cup III. Springer-Verlag Berlin Heidelberg, 2000.

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W, Protzel Peter, Palumbo Daniel L, and Langley Research Center, eds. Automatic learning rate adjustment for self-supervising autonomous robot control. National Aeronautics and Space Administration, Langley Research Center, 1992.

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Michael, Mayer N., Savage Jesus, Saranlı Uluc̨, and SpringerLink (Online service), eds. RoboCup 2011: Robot Soccer World Cup XV. Springer Berlin Heidelberg, 2012.

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Baltes, Jacky. RoboCup 2009: Robot Soccer World Cup XIII. Springer-Verlag Berlin Heidelberg, 2010.

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Milford, Michael John. Robot navigation from nature: Simultaneous localisation, mapping, and path planning based on hippocampal models. Springer, 2008.

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Zalzala, A. M. S. Adaptive robot control using artificial neural networks: An application in the theory of cognition. University of Sheffield, Dept. of Control Engineering, 1989.

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Chapitres de livres sur le sujet "Robot Networks"

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Arteaga, Marco A., Alejandro Gutiérrez-Giles, and Javier Pliego-Jiménez. "Robot Networks." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85980-0_10.

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Yetişenler, Çağdaş, and Ahmet Özkurt. "Multiple Robot Path Planning for Robot Soccer." In Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_2.

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Pranava, Udayagiri R., Vishal Guruprasad, Preetham S. Nag, and S. Suraj. "Firefighting Robot." In Lecture Notes in Networks and Systems. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3812-9_58.

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Henderson, Thomas C. "Mobile Robot Performance Analysis." In Computational Sensor Networks. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09643-8_7.

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Pomerleau, Dean A. "Knowledge-Based Training of Artificial Neural Networks for Autonomous Robot Driving." In Robot Learning. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3184-5_2.

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Mezei, Ivan, Veljko Malbasa, and Ivan Stojmenovic. "Auction Aggregation Protocols for Wireless Robot-Robot Coordination." In Ad-Hoc, Mobile and Wireless Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04383-3_14.

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Nanda, U., A. Biswas, K. L. G. Prathyusha, et al. "Automated Plant Robot." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4218-3_11.

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Bakshi, Smita, Bandita Sahu, and Sanjay Kumar Kuanar. "Task Assignment of Cooperating Robot in Multi-Robot Environment." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8160-7_8.

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Jansen, Arjen, Patrick van der Smagt, and Frans Groen. "Nested Networks for Robot Control." In Applications of Neural Networks. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2379-3_9.

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Mester, Gyula, Istvan Matijevics, Tamas Szepe, and Janos Simon. "Wireless Sensor-Based Robot Control." In Computer Communications and Networks. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-510-1_16.

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Actes de conférences sur le sujet "Robot Networks"

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Ebrahimzadeh, Amin, Martin Maier, and Roch H. Glitho. "Coordination of Intelligent Mobile Robots for Human-Agent-Robot Teamwork in 6G." In 2024 IEEE Future Networks World Forum (FNWF). IEEE, 2024. https://doi.org/10.1109/fnwf63303.2024.11028825.

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Len, Irina, Julia Len, Natalia Amelina, and Yury Ivanskiy. "Robot navigation under uncertainty." In 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA). IEEE, 2024. http://dx.doi.org/10.1109/dcna63495.2024.10718568.

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Zhang, Yihang, Yinfeng Fang, Xixia Yu, Yong Peng, and Zhaojie Ju. "Cosine Similarity Graph Attention Networks for Autism Spectrum Disorder Diagnosis." In 2024 World Rehabilitation Robot Convention (WRRC). IEEE, 2024. http://dx.doi.org/10.1109/wrrc62201.2024.10696658.

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Gajewski, Kamil, Witold Prusak, Jaroslaw Fafara, Aleksander Skrzypiec, and Tymoteusz Turlej. "ARTIFICIALLY AIDED FUNGI RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." In 22nd International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022v/3.2/s14.33.

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This article presents the concept of using neural networks in the recognition of fungi for use in a mobile forest ecosystem inspection robot. There are many dependencies regarding the occurrence of fungi in the vicinity of specific tree species. The presence of some fungi may be the result of a developing tree disease. The possibility of quick recognition of the fungus species using an autonomous mobile robot will allow for faster detection and prevention of the disease in entire ecosystems. An attempt was made to use neural networks to improve the efficiency of recognizing a specific species

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Chuang, Ching-Wei, and Harry H. Cheng. "A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66902.

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Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed

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Chatlatanagulchai, Withit, and Peter H. Meckl. "Model-Free Trajectory Control of a Two-Link Flexible-Joint Robot: Theory and Experiment." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79582.

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We present a state-feedback control of a two-link flexible-joint robot. First, we obtain desired control laws from Lyapunov’s second method. Then, we use three-layer neural networks to learn the unknown parts of the desired control laws. In this way, the control algorithm does not require the mathematical model representing the robot. We use a smooth variable structure controller to handle uncertainties from the neural network approximation and external disturbances. To show the effectiveness and practicality of this control algorithm, we performed an experiment on one of the robots in our lab

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Hozyn, Stanislaw. "Hand Gesture Recognition For Human-Robot Cooperation In Manufacturing Applications." In 37th ECMS International Conference on Modelling and Simulation. ECMS, 2023. http://dx.doi.org/10.7148/2023-0373.

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Human-robot cooperation plays an increasingly important role in manufacturing applications. Together, humans and robots display an exceptional skill level that neither can achieve independently. For such cooperation, hand gesture communication using computer vision has been proven to be the most suitable due to the low cost of implementation and flexibility. Therefore, this work focuses on the hand gesture classification problem in view of human and robot collaboration. To facilitate collaboration, six of the most common gestures applicable in manufacturing applications were selected. The firs

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Rieder, Mathias, and Norbert Bartneck. "Demonstrator for a Collaborative Human-Robot Picking System." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100971.

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This paper introduces a demonstrator for a Collaborative Human-Robot Picking application consisting of a robot in front of a rack picking known objects into a bin. The major part of the system demonstrates the execution of picking orders carried out by human pickers and picking robots in a common workspace. A further part includes the training of Convolutional Neural Networks for object detection. A central concept of the system is an Emergency Call enabling the robot to request help if problems occur during object detection or grasping. The main goals of this demonstrator are to evaluate the

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Pandya, Abhijit S., and Paul G. Luebbers. "Neural networks for robot navigation." In Orlando '91, Orlando, FL, edited by Mohan M. Trivedi. SPIE, 1991. http://dx.doi.org/10.1117/12.45519.

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Josin, Charney, and White. "Robot control using neural networks." In Proceedings of 1993 IEEE International Conference on Neural Networks (ICNN '93). IEEE, 1988. http://dx.doi.org/10.1109/icnn.1988.23980.

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Rapports d'organisations sur le sujet "Robot Networks"

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Nasr, Chaiban. Neural Networks For Robot Control. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387882.

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Barbie, Alexander. ARCHES Digital Twin Framework. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_arches_core_1.0.0.

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In the Helmholtz Future Project ARCHES (Autonomous Robotic Networks to Help Modern Societies) with a consortium of partners from AWI (Alfred- Wegener-Institute Helmholtz Centre for Polar and Marine Research), DLR (German Aerospace Center), KIT (Karlsruhe Institute of Technology), and the GEOMAR (Helmholtz Centre for Ocean Research Kiel), several Digital Twins of ocean observation systems were developed by the GEOMAR and AWI. The ARCHES Digital Twin Framework is one of the results of this project. The software is based on the Robot Operating System (ROS) and is written in Python.

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Song, So Young, and Youn-Kyung Kim. Consumers� Adoption of Fashion Robot Advisers: A Joint-Network Analysis. Iowa State University. Library, 2019. http://dx.doi.org/10.31274/itaa.8278.

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Braganza, D., D. M. Dawson, I. D. Walker, and N. Nath. Neural Network Grasping Controller for Continuum Robots. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462583.

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Brooks, Rodney A. A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada207958.

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Wilson, Edward. Experiments in Neural-Network Control of a Free-Flying Space Robot. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada329618.

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Ray, Asok. Collaboration of Miniature Multi-Modal Mobile Smart Robots over a Network. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada626480.

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Scharzenberger, Cody. Design of a Canine Inspired Quadruped Robot as a Platform for Synthetic Neural Network Control. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7014.

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Dubcovsky, Jorge, and T. (Tzion) Fahima. Validation of candidate genes for a QTL responsible for water stress tolerance and their diversity in wheat. United States-Israel Binational Agricultural Research and Development Fund, 2022. http://dx.doi.org/10.32747/2022.8134149.bard.

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The main goals of this project as stated in the original proposal were to identify which of the candidate gene(s) identified in a 1.5 Mb region of wheat and rye chromosomes 1RS and 1BS were responsible for the differences in root architecture, reveal their natural variation and characterize the epistatic interactions that modulate their effect in different backgrounds. Background: Wheat is an essential crop for global food security and is well adapted to a wide variety of soils. However, the gene networks regulating different wheat root architectures remain poorly understood. Root depth and bi

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Liu, Y., ZQ Tian, C. Song, YC Zhang, and W. Shen. Comparison of robot-assisted surgery, laparoscopic-assisted surgery, and conventional open surgery for the treatment of gastric cancer： A network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.12.0121.

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