Results with autonomous vehicles operating in specialty crops

Bergerman, Marcel; Singh, Sanjiv; Hamner, Bradley

doi:10.1109/icra.2012.6225150

Cited by 38 publications

(17 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each system has its peculiarities, which may be related to the characteristics of the region, such as the relief, climatic conditions, technological level and cultural habits of fruit growers. Tall Spindle and V-trellis training systems were developed to increase yield, fruit quality and precocity of production at high planting densities (Bergerman et al, 2012). There are also other management systems derived from a central leader used in apple orchards with high plant densities, such as Solaxe, Tall Spindle (D'Abrosca et al, 2017) and Vertical Axis.…”

Section: Introductionmentioning

confidence: 99%

Effect of different training systems to catch greater light interception in apple cultivar Maxi Gala in temperate climate

et al. 2019

View full text Add to dashboard Cite

Apple is one of the most consumed fruits in the world. In Brazil, apple is the deciduous fruit of major importance. Its production is focused in the southern region of the country. To increase apple tree yield and to optimize fruit quality, it is important to choose a suitable training system to obtain greater light interception and photosynthetic radiation. Therefore, the objective of this study was to evaluate vegetative and productive aspects of the cultivar Maxi Gala grafted on seven-year-old dwarf rootstock M.9, under three different training systems: Tall Spindle, Solaxe and Vertical Axis. The experimental orchard was implemented in August, 2010 in Vacaria city, Rio Grande do Sul province, Brazil. The experiment was conducted and evaluated in (2014-2017) seasons. The results showed that Tall Spindle, Solaxe and Vertical Axis driving systems had no effect on internode length, plant height, fruit diameter and quality parameters such as pulp firmness and soluble solids. Therefore, the Tall Spindle driving system was more efficient for accumulative production, being this the indicated combination for regions with the edaphoclimatic characteristics like those of the orchard under study.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of different training systems to catch greater light interception in apple cultivar Maxi Gala in temperate climate

et al. 2019

View full text Add to dashboard Cite

show abstract

“…One autonomous robot that is being developed for use in orchards is the Autonomous Prime Mover from Carnegie Mellon University (Bergerman 2012). This vehicle is able to autonomously drive through the rows with trees in the orchards.…”

Section: Earth-based Systemsmentioning

confidence: 99%

Field Robots

From¹,

Gravdahl²,

Pettersen³

2014

Advances in Industrial Control

View full text Add to dashboard Cite

Field robotics is a large and diverse research area which deals with mobile robots of different types that operate in unstructured and often dynamic environments. We have already studied underwater robots and space robots in the previous chapters, so we will focus on land-based robots. Most robots that operate on land use wheels for locomotion, so in this chapter we will mainly focus on wheeled robots. Also legged robots are being developed, and are found especially in research and education. Legged robots are particularly efficient in rough and uneven terrain but have yet to reach the same level of utility as their wheeled counterparts. Snake robots are also being used in research and education and are expected to play an important role in field robotics in the future Liljebäck et al. (2013).Field robotics is a large and diverse research area that include topics such as mechanical design, locomotion and actuation, sensor technology, mapping and navigation, perception, and the control of the robot, possibly with some level of autonomy and cognition. Most of these topics are outside the scope of this book, and we refer to other references such as Siegwart and Nourbakhsh (2004), Dudek and Jenkin (2000), and Choset et al. (2005). In this chapter we will focus on the mechanical design and the kinematic and dynamic modeling of these robots. IntroductionField robots are mainly used for operation of plants and fields that cover large areas, such as in agriculture and environmental mapping, or for exploration tasks in remotely located areas, such as deserts and distant planets (Lazinica 2006). Common for these robots is that they tend to have a high degree of autonomy and are often only supervised or partially controlled by an operator.One promising application area for field robots is agricultural automation (Ceccarelli 2012). Because of the large geographical areas that these fields cover and the extremely repetitive tasks that need to be performed, vehicle-manipulator systems are well suited for agricultural applications. Agricultural robots are normally P.J. From et al., Vehicle-Manipulator Systems, Advances in Industrial Control,

show abstract

“…But also, efficiency and a better equipment usage can be achieved by autonomous systems. In the work presented in [2], an autonomous platform similar to our vehicle is applied in aiding specialty crops maintenance and collection. This system uses laser based sensors to detect obstacles.…”

Section: A Related Workmentioning

confidence: 99%

Vision-Based Autonomous Navigation with a Probabilistic Occupancy Map on Unstructured Scenarios

Klaser

Osório

Wolf

2014

2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol

View full text Add to dashboard Cite

Vision-based robotics perception still have a great focus of attention on building systems because of its common availability and low cost. The 3D data produced by the disparity calculation methods in stereo cameras are inaccurate and presents substantial noise. We present here our method to deal with the noisy 3D point cloud produced by stereo camera to build a navigation map and mark obstacles with a probabilistic occupancy map approach. The objective is to integrate continuously the sensor readings marking occupied and free space based on some certainty and accumulate it over time. The output is a navigability map we use to plan a trajectory path. Our main focus is applications like agricultural fields. We have modeled and tested the system fully in simulation and validated it with our real vehicle platform CaRINA I on unstructured scenarios.

show abstract

Results with autonomous vehicles operating in specialty crops

Cited by 38 publications

References 12 publications

Effect of different training systems to catch greater light interception in apple cultivar Maxi Gala in temperate climate

Effect of different training systems to catch greater light interception in apple cultivar Maxi Gala in temperate climate

Field Robots

Vision-Based Autonomous Navigation with a Probabilistic Occupancy Map on Unstructured Scenarios

Contact Info

Product

Resources

About