89 research outputs found

    Technology, Science and Culture

    Get PDF
    From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

    Advanced Knowledge Application in Practice

    Get PDF
    The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

    Mobile Robots Navigation

    Get PDF
    Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

    Terrain Aware Traverse Planning for Mars Rovers

    Get PDF
    NASA is proposing a Mars Sample Return mission, to be completed within one Martian year, that will require enhanced autonomy to perform its duties faster, safer, and more efficiently. With its main purpose being to retrieve samples possibly tens of kilometers away, it will need to drive beyond line-of-sight to get to its target more quickly than any rovers before. This research proposes a new methodology to support a sample return mission and is divided into three compo-nents: map preparation (map of traversability, i.e., ability of a terrain to sustain the traversal of a vehicle), path planning (pre-planning and replanning), and terrain analysis. The first component aims at creating a better knowledge of terrain traversability to support planning, by predicting rover slip and drive speed along the traverse using orbital data. By overlapping slope, rock abundance and terrain types at the same location, the expected drive velocity is obtained. By combining slope and thermal data, additional information about the experienced slip is derived, indicating whether it will be low (less than 30%) or medium to high (more than 30%). The second component involves planning the traverse for one Martian day (or sol) at a time, based on the map of expected drive speed. This research proposes to plan, offline, several paths traversable in one sol. Once online, the rover chooses the fastest option (the path cost being calculated using the distance divided by the expected velocity). During its drive, the rover monitors the terrain via analysis of its experienced wheel slip and actual speed. This information is then passed along the different pre-planned paths over a given distance (e.g., 25 m) and the map of traversability is locally updated given this new knowledge. When an update occurs, the rover calculates the new time of arrival of the various paths and replans its route if necessary. When tested in a simulation study on maps of the Columbia Hills, Mars, the rover successfully updates the map given new information drawn from a modified map used as ground truth for simulation purposes and replans its traverse when needed. The third component describes a method to assess the soil in-situ in case of dangerous terrain detected during the map update, or if the monitoring is not enough to confirm the traversability predicted by the map. The rover would deploy a shear vane instrument to compute intrinsic terrain parameters, information then propagated ahead of the rover to update the map and replan if necessary. Experiments in a laboratory setting as well as in the field showed promising results, the mounted shear vane giving values close to the expected terrain parameters of the tested soils

    Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

    Full text link
    [EN] The term "Agri-Food 4.0" is an analogy to the term Industry 4.0; coming from the concept "agriculture 4.0". Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCARISE-2015.Lezoche, M.; Hernández, JE.; Alemany Díaz, MDM.; Panetto, H.; Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry. 117:1-15. https://doi.org/10.1016/j.compind.2020.103187S115117Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Ait-Mouheb, N., Bahri, A., Thayer, B. B., Benyahia, B., Bourrié, G., Cherki, B., … Harmand, J. (2018). The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle? Regional Environmental Change, 18(3), 693-705. doi:10.1007/s10113-018-1292-zAli, J., & Kumar, S. (2011). Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain. International Journal of Information Management, 31(2), 149-159. doi:10.1016/j.ijinfomgt.2010.07.008Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 2018 15th Learning and Technology Conference (L&T). doi:10.1109/lt.2018.8368490Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., … Rana, T. K. (2017). Smart farming using IOT. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). doi:10.1109/iemcon.2017.8117219Annosi, M. C., Brunetta, F., Monti, A., & Nati, F. (2019). Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs. Computers in Industry, 109, 59-71. doi:10.1016/j.compind.2019.04.003Baio, F. H. R. (2011). Evaluation of an auto-guidance system operating on a sugar cane harvester. Precision Agriculture, 13(1), 141-147. doi:10.1007/s11119-011-9241-6Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Nicolaas Bezuidenhout, C., Bodhanya, S., & Brenchley, L. (2012). An analysis of collaboration in a sugarcane production and processing supply chain. British Food Journal, 114(6), 880-895. doi:10.1108/00070701211234390Bhatt, M. R., & Buch, S. (2015). Prediction of formability for sheet metal component using artificial intelligent technique. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2015.7095356Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39-61. doi:10.1108/scm-03-2018-0142Boehlje, M. (1999). Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them? American Journal of Agricultural Economics, 81(5), 1028-1041. doi:10.2307/1244080Bonney, L., Clark, R., Collins, R., & Fearne, A. (2007). From serendipity to sustainable competitive advantage: insights from Houston’s Farm and their journey of co‐innovation. Supply Chain Management: An International Journal, 12(6), 395-399. doi:10.1108/13598540710826326Boshkoska, B. M., Liu, S., Zhao, G., Fernandez, A., Gamboa, S., del Pino, M., … Chen, H. (2019). A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains. Computers in Industry, 110, 64-80. doi:10.1016/j.compind.2019.04.012Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55(9), 26-33. doi:10.1109/mcom.2017.1600528Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 205395171664817. doi:10.1177/2053951716648174Brown, K. (2013). Global environmental change I. Progress in Human Geography, 38(1), 107-117. doi:10.1177/0309132513498837Chilcanan, D., Navas, P., & Escobar, S. M. (2017). Expert system for remote process automation in multiplatform servers, through human machine conversation. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). doi:10.23919/cisti.2017.7975913Choi, J., In, Y., Park, C., Seok, S., Seo, H., & Kim, H. (2016). Secure IoT framework and 2D architecture for End-To-End security. The Journal of Supercomputing, 74(8), 3521-3535. doi:10.1007/s11227-016-1684-0Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65-80. doi:10.1016/j.biosystemseng.2013.09.006Del Borghi, A., Gallo, M., Strazza, C., & Del Borghi, M. (2014). An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121-130. doi:10.1016/j.jclepro.2014.04.083Devarakonda, R., Shrestha, B., Palanisamy, G., Hook, L., Killeffer, T., Krassovski, M., … Lazer, K. (2014). OME: Tool for generating and managing metadata to handle BigData. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004476Nascimento, A. F. do, Mendonça, E. de S., Leite, L. F. C., Scholberg, J., & Neves, J. C. L. (2012). Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics. Scientia Agricola, 69(6), 393-401. doi:10.1590/s0103-90162012000600008Dolan, C., & Humphrey, J. (2000). Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry. Journal of Development Studies, 37(2), 147-176. doi:10.1080/713600072Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194-202. doi:10.1016/j.compag.2014.12.023Dworak, V., Selbeck, J., Dammer, K.-H., Hoffmann, M., Zarezadeh, A., & Bobda, C. (2013). Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems. Sensors, 13(2), 1523-1538. doi:10.3390/s130201523Eisele, M., Kiese, R., Krämer, A., & Leibundgut, C. (2001). Application of a catchment water quality model for assessment and prediction of nitrogen budgets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 547-551. doi:10.1016/s1464-1909(01)00048-xElsayed, K. M. F., Ismail, T., & S. Ouf, N. (2018). A Review on the Relevant Applications of Machine Learning in Agriculture. IJIREEICE, 6(8), 1-17. doi:10.17148/ijireeice.2018.681Esteso, A., Alemany, M. M. E., & Ortiz, A. (2017). Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias. Dirección y Organización, 41-46. doi:10.37610/dyo.v0i0.509Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706GERHARDS, R., GUTJAHR, C., WEIS, M., KELLER, M., SÖKEFELD, M., MÖHRING, J., & PIEPHO, H. P. (2011). Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research, 52(1), 6-15. doi:10.1111/j.1365-3180.2011.00893.xGovindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi:10.1016/j.ijpe.2013.12.028Gumaste, S. S., & Kadam, A. J. (2016). Future weather prediction using genetic algorithm and FFT for smart farming. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). doi:10.1109/iccubea.2016.7860028Hashem, H., & Ranc, D. (2016). A review of modeling toolbox for BigData. 2016 International Conference on Military Communications and Information Systems (ICMCIS). doi:10.1109/icmcis.2016.7496565Hefnawy, A., Elhariri, T., Cherifi, C., Robert, J., Bouras, A., Kubler, S., & Framling, K. (2017). Combined use of lifecycle management and IoT in smart cities. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). doi:10.1109/skima.2017.8294112Hosseini, S. H., Tang, C. Y., & Jiang, J. N. (2014). Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data. IEEE Transactions on Sustainable Energy, 5(1), 343-350. doi:10.1109/tste.2013.2284490Hu, Y., Zhang, L., Li, J., & Mehrotra, S. (2016). ICME 2016 Image Recognition Grand Challenge. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). doi:10.1109/icmew.2016.7574663A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, & J. O. Paz. (2006). Artificial Neural Network Model as a Data Analysis Tool in Precision Farming. Transactions of the ASABE, 49(6), 2027-2037. doi:10.13031/2013.22264Jeon, S., Kim, B., & Huh, J. (2017). Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition. Energy for Sustainable Development, 40, 41-49. doi:10.1016/j.esd.2017.06.001Joly, P.-B. (2005). Resilient farming systems in a complex world — new issues for the governance of science and innovation. Australian Journal of Experimental Agriculture, 45(6), 617. doi:10.1071/ea03252Joshi, R., Banwet, D. K., & Shankar, R. (2009). Indian cold chain: modeling the inhibitors. British Food Journal, 111(11), 1260-1283. doi:10.1108/00070700911001077Kamata, T., Roshanianfard, A., & Noguchi, N. (2018). Heavy-weight Crop Harvesting Robot - Controlling Algorithm. IFAC-PapersOnLine, 51(17), 244-249. doi:10.1016/j.ifacol.2018.08.165Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. doi:10.1016/j.ijpe.2019.05.022Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183-200. doi:10.1108/02635570710723804Khan, S. F., & Ismail, M. Y. (2018). An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review. Journal of Computer Science, 14(2), 132-143. doi:10.3844/jcssp.2018.132.143Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., … Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69(6), 532-542. doi:10.2489/jswc.69.6.532Ko, T., Lee, J., & Ryu, D. (2018). Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10(11), 4274. doi:10.3390/su10114274KÖK, M. S. (2009). Application of Food Safety Management Systems (ISO 22000/HACCP) in the Turkish Poultry Industry: A Comparison Based on Enterprise Size. Journal of Food Protection, 72(10), 2221-2225. doi:10.4315/0362-028x-72.10.2221Kvíz, Z., Kroulik, M., & Chyba, J. (2014). Machinery guidance systems analysis concerning pass-to-pass accuracy as a tool for efficient plant production in fields and for soil damage reduction. Plant, Soil and Environment, 60(No. 1), 36-42. doi:10.17221/622/2012-pseLamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129-138. doi:10.1016/j.cie.2015.10.018Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273-292. doi:10.1080/13658810903296630Li, F.-R., Gao, C.-Y., Zhao, H.-L., & Li, X.-Y. (2002). Soil conservation effectiveness and energy efficiency of alternative rotations and continuous wheat cropping in the Loess Plateau of northwest China. Agriculture, Ecosystems & Environment, 91(1-3), 101-111. doi:10.1016/s0167-8809(01)00265-1Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. doi:10.3390/s18082674Meichen, L., Jun, C., Xiang, Z., Lu, W., & Yongpeng, T. (2018). Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine, 51(17), 861-865. doi:10.1016/j.ifacol.2018.08.086Louwagie, G., Northey, G., Finn, J. A., & Purvis, G. (2012). Development of indicators for assessment of the environmental impact of livestock farming in Ireland using the Agri-environmental Footprint Index. Ecological Indicators, 18, 149-162. doi:10.1016/j.ecolind.2011.11.003Luque, A., Peralta, M. E., de las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205. doi:10.1016/j.promfg.2017.09.195Malhotra, S., Doja, M. ., Alam, B., & Alam, M. (2017). Bigdata analysis and comparison of bigdata analytic approches. 2017 International Conference on Computing, Communication and Automation (ICCCA). doi:10.1109/ccaa.2017.8229821Mayer, J., Gunst, L., Mäder, P., Samson, M.-F., Carcea, M., Narducci, V., … Dubois, D. (2015). «Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland». European Journal of Agronomy, 65, 27-39. doi:10.1016/j.eja.2015.01.002McGuire, S., & Sperling, L. (2013). Making seed systems more resilient to stress. Global Environmental Change, 23(3), 644-653. doi:10.1016/j.gloenvcha.2013.02.001Mekala, M. S., & Viswanathan, P. (2017). A Survey: Smart agriculture IoT with cloud computing. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). doi:10.1109/icmdcs.2017.8211551Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian Journal of Science and Technology, 9(38). doi:10.17485/ijst/2016/v9i38/95032Mocnej, J., Seah, W. K. G., Pekar, A., & Zolotova, I. (2018). Decentralised IoT Architecture for Efficient Resources Utilisation. IFAC-PapersOnLine, 51(6), 168-173. doi:10.1016/j.ifacol.2018.07.148Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017). Intelligent drip irrigation and fertigation using wireless sensor networks. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). doi:10.1109/tiar.2017.8273682Montecinos, J., Ouhimmou, M., Chauhan, S., & Paquet, M. (2018). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932-939. doi:10.1016/j.jclepro.2018.03.127Yandun Narváez, F., Gregorio, E., Escolà, A., Rosell-Polo, J. R., Torres-Torriti, M., & Auat Cheein, F. (2018). Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability. Journal of Terramechanics, 76, 1-13. doi:10.1016/j.jterra.2017.10.005Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264. doi:10.1016/j.cor.2017.07.004Nilsson, E., Hochrainer-Stigler, S., Mochizuki, J., & Uvo, C. B. (2016). Hydro-climatic variability and agricultural production on the shores of Lake Chad. Environmental Development, 20, 15-30. doi:10.1016/j.envdev.2016.09.001Nolan, P., Paley, D. A., & Kroeger, K. (2017). Multi-UAS path planning for non-uniform data collection in precision agriculture. 2017 IEEE Aerospace Conference. doi:10.1109/aero.2017.7943794Oberholster, C., Adendorff, C., & Jonker, K. (2015). Financing Agricultural Production from a Value Chain Perspective. Outlook on Agriculture, 44(1), 49-60. doi:10.5367/oa.2015.0197Opara, L. U., & Mazaud, F. (2001). Food Traceability from Field to Plate. Outlook on Agriculture, 30(4), 239-247. doi:10.5367/000000001101293724Ott, K.-H., Aranı́bar, N., Singh, B., & Stockton, G. W. (2003). Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry, 62(6), 971-985. doi:10.1016/s0031-9422(02)00717-3Panetto, H. (2007). Towards a classification framework for interoperability of enterprise applications. International Journal of Computer Integrated Manufacturing, 20(8), 727-740. doi:10.1080/09511920600996419Paulraj, G. J. L., Francis, S. A. J., Peter, J. D., & Jebadurai, I. J. (2018). Resource-aware virtual machine migration in IoT cloud. Future Generation Computer Systems, 85, 173-183. doi:10.1016/j.future.2018.03.024Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). eAGROBOT — A robot for early crop disease detection using image processing. 2015 2nd International Conference on Electronics and Communication Systems (ICECS). doi:10.1109/ecs.2015.7124873Pinho, P., Dias, T., Cruz, C., Sim Tang, Y., Sutton, M. A., Martins-Loução, M.-A., … Branquinho, C. (2011). Using lichen functional diversity to assess the effects of atmospheric ammonia in Mediterranean woodlands. Journal of Applied Ecology, 48(5), 1107-1116. doi:10.1111/j.1365-2664.2011.02033.xPrathibha, S. R., Hongal, A., & Jyothi, M. P. (2017). IOT Based Monitoring System in Smart Agriculture. 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). doi:10.1109/icraect.2017.52Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47-59. doi:10.1016/j.agsy.2018.01.022Ribarics, P. (2016). Big Data and its impact on agriculture. Ecocycles, 2(1), 33-34. doi:10.19040/ecocycles.v2i1.54Rosell, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141. doi:10.1016/j.compag.2011.09.007Roshanianfard, A., Kamata, T., & Noguchi, N. (2018). Performance evaluation of harvesting robot for heavy-weight crops. IFAC-PapersOnLine, 51(17), 332-338. doi:10.1016/j.ifacol.2018.08.200Routroy, S., & Behera, A. (2017). Agriculture supply chain. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. doi:10.1108/jadee-06-2016-0039Ruiz-Garcia, L., Steinberger, G., & Rothmund, M. (2010). A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. Food Control, 21(2), 112-121. doi:10.1016/j.foodcont.2008.12.003Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big

    Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

    Full text link
    Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.Comment: Accepted by 2019 China Symposium on Cognitive Computing and Hybrid Intelligence(CCHI'19

    Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems

    Get PDF
    La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados. Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo. Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida. Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has conducted the research community to face historical complex problems by devising UGV-UAV cooperation paradigms. However, it is usually not a trivial task to determine whether or not a UGV-UAV cooperation is suitable for a particular problem. For this reason, in this thesis, we investigate a particular UGV-UAV cooperation paradigm over two problems in the literature, and we propose an autonomous controller to test it on simulated scenarios. Driven by the planetary exploration, we formulate a particular cooperative exploration problem consisting of reaching a set of target points in a large-scale exploration area. This problem defines the UGV as a moving charging station to carry the UAV through different locations from where the UAV can reach the target points. Consequently, we propose the cooperaTive ExploRation Routing Algorithm (TERRA) to solve it. This algorithm stands out for splitting up the exploration problem into five sub-problems, in which each sub-problem is solved in a particular stage of the algorithm. In the same way, driven by the explosion of parcels delivery in e-commerce companies, we formulate a generalization of the well-known last-mile delivery problem. This generalization defines the same UGV’s and UAV’s rol as the exploration problem. That is, the UGV acts as a moving charging station which carries the parcels along several UAVs to deliver them. In this way, we follow the split strategy depicted by TERRA to propose the COoperative Unmanned deliveRIEs planning algoRithm (COURIER). This algorithm replicates the first four TERRA’s stages, but it builds a new fifth stage to produce a task plan solving the problem. In order to evaluate the UGV-UAV cooperation paradigm on simulated scenarios, we propose the Autonomous coopeRatIve Execution System (ARIES). This controller follows a hierarchical decentralized leader-follower approach to integrate any cooperation paradigm in a distributed manner. Both algorithms have been characterized to identify the relevant aspects of the cooperation paradigm in the related problems. Also, both of them demonstrate a great performance of the cooperation paradigm in such problems, and as well as the autonomous controller, reveal a great potential for future real applications

    Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

    Get PDF
    Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE
    corecore