Navigation techniques of mobile robots in greenhouses

Abstract

With the continuous development of the industrialization process, the countries all over the world gradually appeared lack of agricultural labor force and aging phenomenon, which was especially prominent in developed countries. However the agricultural robot with high operating efficiency, high qualities of work will play an increasingly important role in future agricultural production. Robot navigation is not only the key to automation and also the biggest obstacle constraining their development. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: 1. Odometry; 2. Inertial Navigation; 3. Magnetic Compasses; 4. Active Beacons; 5. Global Positioning Systems; 6. Landmark Navigation; and 7. Model Matching. Therefore, the research status of agricultural robot navigation was introduced in this paper. Also, this paper discusses the problem of using navigation methods for agricultural mobile robots in greenhouses. Nowadays, many agricultural tasks are dangerous and repetitive for human beings and could be improved employing robots. The autonomous navigation in greenhouses has been solved using both deliberative and pseudo-reactive techniques.

Authors and Affiliations

Ehsan Kamran| Department of farm machinery, Faculty of Agriculture, Islamic Azad University, Ilam Branch, Ilam, Iran, Asadullah Mirasi| Department of farm machinery, Faculty of Biosystems Engineering, Islamic Azad University, Lordegan Branch, Shahrecord, Iran, Mousarreza samadi| Ph.D. Candidate in post-harvest, Department of Agricultural Machinery Engineering, University of Tabriz, Iran

Keywords

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  • EP ID EP13232
  • DOI -
  • Views 334
  • Downloads 11

How To Cite

Ehsan Kamran, Asadullah Mirasi, Mousarreza samadi (2014). Navigation techniques of mobile robots in greenhouses. International journal of Advanced Biological and Biomedical Research, 2(5), 1438-1449. https://www.europub.co.uk/articles/-A-13232