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Journal articles on the topic 'Cooperative exploration'

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Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Zhang, Fumin, and Naomi Ehrich Leonard. "Cooperative Filters and Control for Cooperative Exploration." IEEE Transactions on Automatic Control 55, no. 3 (March 2010): 650–63. http://dx.doi.org/10.1109/tac.2009.2039240.

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2

Chao-xia, Shi, Hong Bing-rong, and Wang Yan-qing. "Cooperative Exploration by Multi-Robots without Global Localization." International Journal of Advanced Robotic Systems 4, no. 3 (September 1, 2007): 36. http://dx.doi.org/10.5772/5682.

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Efficient exploration of unknown environments is a fundamental problem in mobile robotics. We propose a novel topological map whose nodes are represented with the range finder's free beams together with the visual scale-invariant features. The topological map enables teams of robots to efficiently explore environments from different, unknown locations without knowing their initial poses, relative poses and global poses in a certain world reference frame. The experiments of map merging and coordinated exploration demonstrate the proposed map is not only easy for merging, but also convenient for robust and efficient explorations in unknown environments.

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3

Blamont, Jacques. "International space exploration: Cooperative or competitive?" Space Policy 21, no. 2 (May 2005): 89–92. http://dx.doi.org/10.1016/j.spacepol.2005.03.003.

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4

Rathnam, Ravi K., and Andreas Birk. "Cooperative 3D Exploration under Communication Constraints." IFAC Proceedings Volumes 45, no. 5 (2012): 89–93. http://dx.doi.org/10.3182/20120410-3-pt-4028.00016.

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5

Fitriani, Fitriani. "Penguatan kapasitas kelembagaan gapoktan melalui pembentukan koperasi pertanian." Masyarakat, Kebudayaan dan Politik 28, no. 2 (April 1, 2015): 63. http://dx.doi.org/10.20473/mkp.v28i22015.63-69.

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Gapoktan or Farmers Group constructed with a focus on improving agricultural production. Gapoktan generally face problems such as limited access to capital, technology and markets. A lack of communication and coordination between the farmer and board member Gapoktan also cause ineffectiveness in Gapoktan performance. This study aims to identify the fusion process and consolidation of institutions Gapoktan become new economic institutions as agricultural cooperatives, also to discover the prerequisite conditions and the necessary support in the new cooperative institutions manifest in District Adiluwih. Methods RRA (rapid rural appraisal) and FGDs conducted to produce concrete solutions for Gapoktan institutional problem resolution. Literacy exploration is an important part in analyzing the problems solving. The finding concluded that the fusion of Gapoktan to cooperative institution took place gradually. The new cooperative called LKMA Sharia Adi Makmur Cooperation. Internal consolidation in Gapoktan board continued with the preparation of cooperative establishment. Furthermore, a new cooperative members meeting, registration at the Notary, and the filing of a legal entity were the process of the cooperative establishment. Prerequisite condition for cooperative operation is the good financial and managerial performances. Supporting conditions is necessary for cooperative perform. Conducive business circumstance on cooperative core business is needed. Strengthening cooperative networking with strategic partners is main factor to increase the capacity of the cooperative effort.

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6

Shumeta, Zekarias, and Marijke D’Haese. "Do coffee cooperatives benefit farmers? An exploration of heterogeneous impact of coffee cooperative membership in Southwest Ethiopia." International Food and Agribusiness Management Review 19, no. 4 (December 1, 2016): 37–52. http://dx.doi.org/10.22434/ifamr2015.0110.

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Smallholder farmers’ participation in agricultural cooperatives is often promoted as a promising strategy for overcoming market imperfections and to increase farmers’ productivity and income. In recognition of this potential, in recent years, Ethiopia has shown renewed interest in promoting cooperatives. However, there is lack of empirical evidence of the impact that cooperatives have on farmers’ performance in Ethiopia. Using a matching technique, we evaluate the impact of coffee cooperatives on the performance of their member households in terms of income and coffee production. We use data from coffee farmers in south-west Ethiopia. The overall results suggest that members of cooperatives are not faring much better than non-members. The treatment effects we measured were not statistically significant from zero. Yet, the aggregate figures mask differences between cooperatives and amongst individual cooperative members. Average treatment effects on members differ between cooperatives, in general older members, those who have benefitted from more education and those with larger coffee plantations seem to benefit more from membership. Our analysis sheds light on the heterogeneity in the impact that membership of a cooperative can have: this differs by cooperative and by members within cooperatives, a finding that has important policy implications.

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7

Wu, Wencen, and Fumin Zhang. "Robust Cooperative Exploration With a Switching Strategy." IEEE Transactions on Robotics 28, no. 4 (August 2012): 828–39. http://dx.doi.org/10.1109/tro.2012.2190182.

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8

Hu, Jinwen, Jun Xu, and Lihua Xie. "Cooperative Search and Exploration in Robotic Networks." Unmanned Systems 01, no. 01 (June 20, 2013): 121–42. http://dx.doi.org/10.1142/s2301385013500064.

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Great potentials of robotic networks have been found in numerous applications such as environmental monitoring, battlefield surveillance, target search and rescue, oil and gas exploration, etc. A networked multi-robot system allows cooperative actions among robots and can achieve much beyond the summed capabilities of each individual robot. However, it also poses new research and technical challenges including novel methods for multi-agent data fusion, topology control and cooperative path planning, etc. In this paper, we review recent developments in cooperative control of robotic networks with focus on search and exploration. We shall first present a general formulation of the search and exploration problem, and then divide the overall search strategy into different modules based on their functions. Methods and algorithms are illustrated and compared following the classification of the modules. Moreover, a 3D simulator developed in our laboratory is introduced and its application is demonstrated by experiments. Finally, challenges and future research in this area are provided.

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9

Sapaty, Peter S. "Cooperative exploration of distributed worlds in WAVE." Artificial Life and Robotics 4, no. 2 (June 2000): 109–18. http://dx.doi.org/10.1007/bf02480865.

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10

Huang, Xiaoci. "Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment." Symmetry 12, no. 4 (April 4, 2020): 549. http://dx.doi.org/10.3390/sym12040549.

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This paper studies the cooperation method of multi-cooperative Unmanned Surface Vehicles (USVs) for chemical pollution source monitoring in a dynamic water environment. Multiple USVs formed a mobile sensor network in a symmetrical or asymmetrical formation. Based on ‘Infotaxis’ algorithms for multi-USV, an improved shared probability is proposed for solving the problems of low success rate and low efficiency resulting from the cognitive differences of multi-USV in cooperative exploration. By introducing the confidence factor, the cognitive differences between USVs are coordinated. The success rate and the efficiency of exploration are improved. To further optimize the exploration strategy, the particle swarm optimization (PSO) algorithm is introduced into the ‘Infotaxis’ algorithm to plan the USVs’ exploration path. This method is called the ‘PSO-Infotaxis’ algorithm. The effectiveness of the proposed method is verified by simulation and laboratory experiments. A comparison of the test results shows that the ‘PSO-Infotaxis’ algorithm is superior with respect to exploring efficiency. It can reduce the uncertainty of the estimation for source location faster and has lower exploration time, which is most important for the exploration of a large range of water areas.

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11

Li, Chao, and Shi Yin Qin. "Task Cooperation and Behavior Coordination of Multi-Robot Environment Exploration Based on Artificial Potential Field." Applied Mechanics and Materials 433-435 (October 2013): 587–94. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.587.

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This paper presents a new approach to multi-robot exploration of unstructured environment in which both multi-robot task cooperation and behavior coordination strategies are implemented. The task cooperation is achieved by assigning exploring goals to robots dynamically in a cooperative way, and the behavior coordination of robots are carried out with modified artificial potential field to assure a balanced distribution along the frontier areas as soon as possible. In this way, unnecessary detours are avoided so as to make the exploration process more efficient. A series of simulation results demonstrate that the task cooperation and behavior coordination among multiple robots are well implemented to distribute various robots over the unknown complex environment properly and accomplish the exploration mission effectively.

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12

Jayasekara, Peshala G., and Takashi Kubota. "1A2-H03 Cooperative SLAM Scheme for Planetary Exploration Rovers(Localization and Mapping (1))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _1A2—H03_1—_1A2—H03_4. http://dx.doi.org/10.1299/jsmermd.2013._1a2-h03_1.

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13

Al Khawaldah, Mohammad, Mohammad Al-Khedher, Ibrahim Al-Adwan, and Ayman Al Rawashdeh. "An Autonomous Exploration Strategy for Cooperative Mobile Robots." Journal of Software Engineering and Applications 07, no. 03 (2014): 142–49. http://dx.doi.org/10.4236/jsea.2014.73016.

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14

Ziparo, Vittorio Amos, Alexander Kleiner, Alessandro Farinelli, Luca Marchetti, and Daniele Nardi. "COOPERATIVE EXPLORATION FOR USAR ROBOTS WITH INDIRECT COMMUNICATION." IFAC Proceedings Volumes 40, no. 15 (2007): 554–59. http://dx.doi.org/10.3182/20070903-3-fr-2921.00094.

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15

Jiang, Zheng-Qing, and Dong-Hun Lee. "Exploration of Chinese-Korean Cooperative Multimedia Design Education." International Journal of Contents 5, no. 4 (December 28, 2009): 69–74. http://dx.doi.org/10.5392/ijoc.2009.5.4.069.

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16

Kim, Jonghoek. "Cooperative Exploration and Networking While Preserving Collision Avoidance." IEEE Transactions on Cybernetics 47, no. 12 (December 2017): 4038–48. http://dx.doi.org/10.1109/tcyb.2016.2594500.

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17

Lu, Wenjun, Yanqing Wang, and Yongquan Li. "A new method of multi-robot cooperative exploration." International Journal of Modelling, Identification and Control 41, no. 1/2 (2022): 155. http://dx.doi.org/10.1504/ijmic.2022.10052106.

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18

Wang, Yanqing, Yongquan Li, and Wenjun Lu. "A new method of multi-robot cooperative exploration." International Journal of Modelling, Identification and Control 41, no. 1/2 (2022): 155. http://dx.doi.org/10.1504/ijmic.2022.127104.

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19

Susanto,, Ratno, and Achmad Afandi. "Peningkatan Senam Lantai Meroda Dengan Model Cooperative Learning Tipe Circuit Learning." Jurnal Porkes 4, no. 2 (December 31, 2021): 204–9. http://dx.doi.org/10.29408/porkes.v4i2.5047.

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This exploration aims to improve wheelchair gymnastics through cooperative learning type circuit learning for Madrasah Tsanawiyah Nurul Huda Dau due to the lack of interest of students who participate in wheelchair gymnastics and require many learning models to attract students' interest in exercising. The learning process that requires time for achievement to achieve the KKM value of exploration needs to be carried out and developed to achieve the learning outcomes obtained with their abilities. This exploration is Classroom Action Research (CAR). Do-it-yourself exploration with 25 students. The exploration uses observation, cycle I, cycle II, action implementation, and reflection. Exploration instruments that are used during exploration are the rubric for assessing the wheeling motion (KI 3: Psychomotor) and sheets (KI 4: Cognitive). Exploratory data analysis techniques measure plus achieve learning outcomes and use quantitative data analysis. The results of exploration in learning 10 students completed 15 students did not complete learning. Students are given a circuit learning type of cooperative learning media as a means of moving learning outcomes, usually, from 25 students, 80% of learning outcomes are obtained. The conclusion is that exploration in cycle II shows an escalation of the learning effect of 25 students as much as 74%.

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20

Yu, Shu, and Takaya Yuizono. "Opening the ‘Black Box’ of Cooperative Learning in Face-to-Face versus Computer-Supported Learning in the Time of COVID-19." Education Sciences 11, no. 3 (March 4, 2021): 102. http://dx.doi.org/10.3390/educsci11030102.

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This paper aims to identify the changes in student behaviors that resulted from the switch from face-to-face (F2F) learning to computer-supported cooperative learning (CSCL) due to the coronavirus (COVID-19) pandemic. We constructed a triple-dimensional index with “thinking ability improvement”, “horizontal knowledge construction”, and “vertical social relationship evolution” to make comparisons. According to majors, we selected 23 students who registered for entrepreneurship courses from March to June 2019 in F2F and 23 students from March to June 2020 in CSCL formats. We utilized mixed methods, including experimental, content-based, and social network methods, to conduct evaluations. The results show the following: (1) Cooperative learning is beneficial in cultivating creative thinking for both F2F and CSCL groups. (2) The level of knowledge construction was slightly higher in F2F than that in CSCL in general. The effect of F2F learning in the early stage of the course was better, and in the later stage of the class CSCL attained a higher value. (3) For social abilities, the interactions in CSCL were closer than those in the F2F group. F2F cooperative learning was more prone to “fake cooperation” and free-riding behavior, whereas CSCL led to “pan-cooperation” and lacked the in-depth exploration of knowledge. Therefore, this pandemic provides opportunities for cooperative learning with in-depth exploration. CSCL offers sustainable and more hybrid learning activities that allow for the combination of online and offline learning to be experienced according to course contents.

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21

Cai, Yifan, and Simon X. Yang. "A PSO-Based Approach with Fuzzy Obstacle Avoidance for Cooperative Multi-Robots in Unknown Environments." International Journal of Computational Intelligence and Applications 15, no. 01 (March 2016): 1650001. http://dx.doi.org/10.1142/s1469026816500012.

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Cooperative exploration in unknown environments is fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are the key issues for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish target searching and foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance module improves the smoothness of robot trajectory. In the simulation studies, several scenarios with and without the fuzzy module are investigated. The robot trajectory smoothness improvement is demonstrated through the comparative studies.

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22

Tuna, Gurkan, Kayhan Gulez, and V. Cagri Gungor. "The effects of exploration strategies and communication models on the performance of cooperative exploration." Ad Hoc Networks 11, no. 7 (September 2013): 1931–41. http://dx.doi.org/10.1016/j.adhoc.2012.07.011.

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23

Batinović, Ana, Juraj Oršulić, Tamara Petrović, and Stjepan Bogdan. "Decentralized Strategy for Cooperative Multi-Robot Exploration and Mapping." IFAC-PapersOnLine 53, no. 2 (2020): 9682–87. http://dx.doi.org/10.1016/j.ifacol.2020.12.2618.

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24

ZHU, ShengYing, Yi XIU, DongChen LIU, Ning ZHANG, and Rui XU. "Two-spacecraft cooperative optical navigation for binary-asteroid exploration." SCIENTIA SINICA Physica, Mechanica & Astronomica 52, no. 1 (May 26, 2021): 214506. http://dx.doi.org/10.1360/sspma-2020-0500.

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25

Kim, Jonghoek, Fumin Zhang, and Magnus Egerstedt. "Simultaneous Cooperative Exploration and Networking Based on Voronoi Diagrams." IFAC Proceedings Volumes 42, no. 22 (2009): 1–6. http://dx.doi.org/10.3182/20091006-3-us-4006.00001.

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26

Poncela, Alberto. "Cooperative behaviour-based complete exploration for an autonomous robot." AI Communications 22, no. 3 (2009): 187–89. http://dx.doi.org/10.3233/aic-2009-0449.

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27

Bang, Mun Seop, Young Hoon Joo, and Sang Hoon Ji. "Optimal Region Deployment for Cooperative Exploration of Swarm Robots." Journal of Korean Institute of Intelligent Systems 22, no. 6 (December 25, 2012): 687–93. http://dx.doi.org/10.5391/jkiis.2012.22.6.687.

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28

Taylor, Christopher R., Matthew T. Mulvee, Domonkos S. Perenyi, Michael R. Probert, Graeme M. Day, and Jonathan W. Steed. "Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration." Journal of the American Chemical Society 142, no. 39 (September 8, 2020): 16668–80. http://dx.doi.org/10.1021/jacs.0c06749.

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29

Mahdoui, Nesrine, Vincent Frémont, and Enrico Natalizio. "Communicating Multi-UAV System for Cooperative SLAM-based Exploration." Journal of Intelligent & Robotic Systems 98, no. 2 (November 8, 2019): 325–43. http://dx.doi.org/10.1007/s10846-019-01062-6.

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30

Belbachir, Assia, and Sorore Benabid. "An efficient cooperative exploration strategy for wireless sensor network." Intelligent Service Robotics 11, no. 3 (March 21, 2018): 237–46. http://dx.doi.org/10.1007/s11370-018-0249-x.

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31

司, 文静. "Exploration on Cooperative Learning of Bridge Engineering Construction Technology." Advances in Education 12, no. 11 (2022): 4955–59. http://dx.doi.org/10.12677/ae.2022.1211755.

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32

Zhao, Kun Yu, Jun Li, Qi Long Yong, Ye Hua Jiang, and Jian Hong Yi. "Cooperative Innovation, Common Development, Exploration and Practice to Cultivate High-Quality Talents - Faculty of Material Science and Engineering in KMUST." Advanced Materials Research 662 (February 2013): 981–87. http://dx.doi.org/10.4028/www.scientific.net/amr.662.981.

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Abstract: To make cooperative innovation, common development and improve the ability of independent innovation, cultivating talents is fundamental. The aim of setting up joint-training mode between universities and research institute is to enforce education resource sharing, carry out the deep cooperation about talents and scientific research projects, and establish the cooperative innovation alliance. Kunming University of Science and Technology (KMUST) with the help of subject advantages of material college, training talents jointly with Structure Material Research Institute of Beijing Iron and Steel Research Institute has met the demand of the country and produced a marked effect. Now, the cooperation mode has already transformed to teachers' scientific research cooperation, not just the single joint-training to postgraduates. Thoroughly, Beijing Iron and Steel Research Institute has become the training base for young teachers who investigate on iron and steel materials. At the same time, the cooperation and contacts between university and iron and steel enterprises has increased significantly.

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33

Li, Shanshan, Liang Zhang, and Zongpu Li. "Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2146/1/012021.

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Abstract In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.

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34

Zhou, Zigang, Zao Yi, Yongjia Yang, Hao Chen, Yanling Han, Huili Wang, Xumei Duan, and Qiang Wang. "Research and practice of cooperative learning method in photoelectric technology training of college students." Lifelong Education 8, no. 1 (February 4, 2019): 13. http://dx.doi.org/10.18282/le.v8i1.760.

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<p>Based on the constructivist learning theory, this paper adopts cooperative learning teaching method and constructs the COME science and technology education model with classroom, ideas, methods and elements as the unit according to the model of Chinese college students' physics academic competition. After more than ten years of exploration and practice by the micro-nano optical science and technology innovation team, the results show that the teaching method of combining teacher guidance and cooperative learning in the teaching of university photoelectric science and technology training not only strengthens the communication and cooperation between students, exercises students' ability to analyze and solve problems, cultivates students' cooperative consciousness, innovative spirit and scientific research quality, but also improves students' academic performance in a large area, which can definitely enhance students' knowledge, ability and quality comprehensively and harmoniously.</p>

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35

Yu, Minggang, Ming He, Ziyu Ma, Mingguang Zou, Lei Wan, and Kai Kang. "Cooperative Evolution Mechanism of Unmanned Swarm within the Framework of Public Goods Game." Mathematical Problems in Engineering 2021 (April 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5575815.

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One of the key advantages of unmanned swarm operation is its autonomous cooperation. When the communication is interrupted or the centralized control manner is lost, the cooperative operation can still be carried out orderly. This work proposed a cooperative evolution mechanism within the framework of multiplayer public goods game to solve the problem of autonomous collaboration of unmanned swarm in case of failure of centralized control. It starts with the requirement analysis of autonomous cooperation in unmanned swarm, and then, the evolutionary game model of multiplayer public goods based on aspiration-driven dynamics is established. On this basis, the average abundance function is constructed by theoretical derivation, and furthermore, the influence of cost, multiplication factor, and aspiration level on the average abundance is simulated. Finally, the evolutionary mechanism of parameter adjustment in swarm cooperation is revealed via case study, and deliberate proposals are suggested to provide a meaningful exploration in the actual control of unmanned swarm cooperation.

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36

Plakolb, Simon, and Nikita Strelkovskii. "Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior." Systems 11, no. 2 (February 14, 2023): 105. http://dx.doi.org/10.3390/systems11020105.

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Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general.

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37

Mundia, Mukwalikuli, Chewe Nkonde, Francis Simui, and Joseph Imasiku. ""Exploration of Agriculture Cooperative Enterprising Performance in Kabwe District, Zambia"." World Journal of Research and Review 15, no. 3 (September 10, 2022): 5. http://dx.doi.org/10.31871/wjrr.15.3.5.

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38

Franchi, A., L. Freda, G. Oriolo, and M. Vendittelli. "The Sensor-based Random Graph Method for Cooperative Robot Exploration." IEEE/ASME Transactions on Mechatronics 14, no. 2 (April 2009): 163–75. http://dx.doi.org/10.1109/tmech.2009.2013617.

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39

杨, 岸. "Construction and Exploration of University-Enterprise Cooperative Maker Training Base." Advances in Education 10, no. 05 (2020): 842–47. http://dx.doi.org/10.12677/ae.2020.105137.

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40

Huibers, Linda, Wendy Thijssen, Jan Koetsenruijter, Paul Giesen, Richard Grol, and Michel Wensing. "GP cooperative and emergency department: an exploration of patient flows." Journal of Evaluation in Clinical Practice 19, no. 2 (February 5, 2012): 243–49. http://dx.doi.org/10.1111/j.1365-2753.2011.01806.x.

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41

RUBINTON, NATALIE. "Career Exploration for Middle School Youth: A University-School Cooperative." Vocational Guidance Quarterly 33, no. 3 (March 1985): 249–55. http://dx.doi.org/10.1002/j.2164-585x.1985.tb01316.x.

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42

Ropero, Fernando, Pablo Muñoz, and María D. R-Moreno. "TERRA: A path planning algorithm for cooperative UGV–UAV exploration." Engineering Applications of Artificial Intelligence 78 (February 2019): 260–72. http://dx.doi.org/10.1016/j.engappai.2018.11.008.

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43

Dai, Xuefeng, Laihao Jiang, and Yan Zhao. "Cooperative exploration based on supervisory control of multi-robot systems." Applied Intelligence 45, no. 1 (January 14, 2016): 18–29. http://dx.doi.org/10.1007/s10489-015-0741-3.

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44

Wu, Wencen, and Fumin Zhang. "Cooperative exploration of level surfaces of three dimensional scalar fields." Automatica 47, no. 9 (September 2011): 2044–51. http://dx.doi.org/10.1016/j.automatica.2011.06.001.

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45

Li, Wei, Cisong Shi, Qing Xu, and Ying Huang. "Dynamic Population Cooperative." International Journal of Swarm Intelligence Research 13, no. 1 (January 1, 2022): 1–20. http://dx.doi.org/10.4018/ijsir.313664.

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Particle swarm optimization (PSO) has attracted wide attention in the recent decade. Although PSO is an efficient and simple evolutionary algorithm and has been successfully applied to solve optimization problems in many real-world fields, premature maturation and poor local search capability remain two critical issues for PSO. Therefore, to alleviate these disadvantages, a dynamic population cooperative particle swarm optimization for global optimization problems (DPCPSO) is proposed. Firstly, to enhance local search capability, an elite neighborhood learning strategy is constructed by leveraging information from elite particles. Meanwhile, to make the particle easily jump out of the local optimum, a crossover-mutation mechanism is utilized. Finally, a dynamic population partitioning mechanism is designed to balance exploration and exploitation capabilities. 16 classic benchmark functions and 1 real-world optimization problem are used to test the proposed algorithm against with 6 typical PSO algorithms. The experimental results show that DPCPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of DPCPSO are also significantly improved. Therefore, the algorithm is highly competitive in solving global optimization problems.

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46

Jiang, Jiechuan, and Zongqing Lu. "Generative Exploration and Exploitation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4337–44. http://dx.doi.org/10.1609/aaai.v34i04.5858.

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Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to overcome sparse reward. GENE automatically generates start states to encourage the agent to explore the environment and to exploit received reward signals. GENE can adaptively tradeoff between exploration and exploitation according to the varying distributions of states experienced by the agent as the learning progresses. GENE relies on no prior knowledge about the environment and can be combined with any RL algorithm, no matter on-policy or off-policy, single-agent or multi-agent. Empirically, we demonstrate that GENE significantly outperforms existing methods in three tasks with only binary rewards, including Maze, Maze Ant, and Cooperative Navigation. Ablation studies verify the emergence of progressive exploration and automatic reversing.

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Ohtsubo, Yoshikazu, and Morihito Matsuyama. "Group Control of Mobile Robots for More Efficient Searches – Verification of Semi-Autonomous Trajectory Tracking Motions in Irregular Ground Environment –." Journal of Robotics and Mechatronics 30, no. 6 (December 20, 2018): 980–90. http://dx.doi.org/10.20965/jrm.2018.p0980.

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After the occurrence of a disaster, it is critical to perform rapid and accurate searching operations in the large disaster area. It is efficient to perform such operations using multiple mobile exploration robots. Accordingly, we focus on cooperative cruising in a disaster environment and propose the trajectory tracking control method for a semi-autonomous search robot. We apply a robot operating system (ROS) to execute the trajectory tracking control using two mobile exploration robots. In this paper, we describe the trajectory tracking control using gravity potential method and the results of a cooperative cruising experiment in an uneven terrain environment.

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Cui, Yujuan, Wenhan Dong, Duoxiu Hu, and Haibo Liu. "The Application of Improved Harmony Search Algorithm to Multi-UAV Task Assignment." Electronics 11, no. 8 (April 7, 2022): 1171. http://dx.doi.org/10.3390/electronics11081171.

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In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints.

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Zong, Zefang, Meng Zheng, Yong Li, and Depeng Jin. "MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9980–88. http://dx.doi.org/10.1609/aaai.v36i9.21236.

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Cooperative Pickup and Delivery Problem (PDP), as a variant of the typical Vehicle Routing Problems (VRP), is an important formulation in many real-world applications, such as on-demand delivery, industrial warehousing, etc. It is of great importance to efficiently provide high-quality solutions of cooperative PDP. However, it is not trivial to provide effective solutions directly due to two major challenges: 1) the structural dependency between pickup and delivery pairs require explicit modeling and representation. 2) the cooperation between different vehicles is highly related to the solution exploration and difficult to model. In this paper, we propose a novel multi-agent reinforcement learning based framework to solve the cooperative PDP (MAPDP). First, we design a paired context embedding to well measure the dependency of different nodes considering their structural limits. Second, we utilize cooperative multi-agent decoders to leverage the decision dependence among different vehicle agents based on a special communication embedding. Third, we design a novel cooperative A2C algorithm to train the integrated model. We conduct extensive experiments on a randomly generated dataset and a real-world dataset. Experiments result shown that the proposed MAPDP outperform all other baselines by at least 1.64\% in all settings, and shows significant computation speed during solution inference.

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Zhang, Guohua. "Optimization Research and Exploration of New Rural Cooperative Medical Information System." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 03 (March 9, 2021): 113. http://dx.doi.org/10.3991/ijoe.v17i03.20695.

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<span style="line-height: 114%; letter-spacing: 0.1pt; font-family: 'Times','serif'; font-size: 10pt; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-bidi-font-family: 'Times New Roman'; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The New Rural Cooperative Medical Care Information System (NRCMS) used a single relational database to store most of the data in the early days. The scheme is difficult to expand and has little concurrent ability, so it is not suitable to deal with semi-structured, Unstructured data. Then along came distributed technologies such as Hadoop, which had the advantage of using inexpensive machines to store and process data. But HADOOP was designed to distribute data equally among the nodes in the cluster for storage and processing, ignoring the differences in the storage and computing capabilities of the cluster nodes themselves, and the performance of individual nodes even affects the performance of the collation system, and it is master-slave node processing data, there are data can tamper, system terminal is too single and so on. The contribution of this paper lies in the overall structure and technical route of the new rural cooperative medical system, and the optimization method of data flow of heterogeneous nodes, which can effectively save the scale and cost of the cluster, with the help of blockchain technology to ensure the security and credibility of data, optimize the front-end cluster service architecture, support multi-channel and high-concurrent key information push mode, has a very good promotion.</span>

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