Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Fechar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

001
Publicações

2019

Parallelization of a Vine Trunk Detection Algorithm for a Real Time Robot Localization System

Autores
Azevedo, F; Shinde, P; Santos, L; Mendes, J; Santos, FN; Mendonca, H;

Publicação
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
Developing ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge due to two main reasons: harsh condition of the terrain and unstable localization accuracy obtained with Global Navigation Satellite System (GNSS). In this context, a reliable localization system requires an accurate detector for high density of natural/artificial features. In previous works, we presented a novel visual detector for Vineyards Trunks and Masts (ViTruDe) with high levels of detection accuracy. However, its implementation on the most common processing units - central processing units (CPU), using a standard programming language (C/C++), is unable to reach the processing efficiency requirements for real time operation. In this work, we explored parallelization capabilities of processing units, such as graphics processing units (GPU), in order to accelerate the processing time of ViTruDe. This work gives a general perspective on how to parallelize a generic problem in a GPU based solution, while exploring its efficiency when applied to the problem at hands. The ViTruDe detector for GPU was developed considering the constraints of a cost-effective robot to carry-out crop monitoring tasks in steep slope vineyard environments. We compared the proposed ViTruDe implementation on GPU using Compute Unified Compute Unified Device Architecture(CUDA) and CPU, and the achieved solution is over eighty times faster than its CPU counterpart. The training and test data are made public for future research work. This approach is a contribution for an accurate and reliable localization system that is GNSS-free. © 2019 IEEE.

2019

Path Planning approach with the extraction of Topological Maps from Occupancy Grid Maps in steep slope vineyards

Autores
Santos, L; Santos, FN; Magalhaes, S; Costa, P; Reis, R;

Publicação
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
Robotic platforms are being developed for precision agriculture, to execute repetitive and long term tasks. Autonomous monitoring, pruning, spraying and harvesting are some of these agricultural tasks, which requires an advanced path planning system aware of maximum robot capabilities (mobile platform and arms), terrain slopes and plant/fruits position. The state of the art path planning systems have two limitations: are not optimized for large regions and the path planning is not aware of agricultural tasks requirements. This work presents two solutions to overcome these limitations. It considers the VGR2TO (Vineyard Grid Map to Topological) approach to extract from a 2D grid map a topological map, to reduce the total amount of memory needed by the path planning algorithm and to reduce path search space. Besides, introduces an extension to the chosen algorithm, the Astar algorithm, to ensure a safe path and a maximum distance from the vine trees to enable robotic operations on the tree and its fruits. © 2019 IEEE.

2019

Vineyard segmentation from satellite imagery using machine learning

Autores
Santos, L; Santos, FN; Filipe, V; Shinde, P;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Steep slope vineyards are a complex scenario for the development of ground robots due to the harsh terrain conditions and unstable localization systems. Automate vineyard tasks (like monitoring, pruning, spraying, and harvesting) requires advanced robotic path planning approaches. These approaches usually resort to Simultaneous Localization and Mapping (SLAM) techniques to acquire environment information, which requires previous navigation of the robot through the entire vineyard. The analysis of satellite or aerial images could represent an alternative to SLAM techniques, to build the first version of occupation grid map (needed by robots). The state of the art for aerial vineyard images analysis is limited to flat vineyards with straight vine’s row. This work considers a machine learning based approach (SVM classifier with Local Binary Pattern (LBP) based descriptor) to perform the vineyard segmentation from public satellite imagery. In the experiments with a dataset of satellite images from vineyards of Douro region, the proposed method achieved accuracy over 90%. © Springer Nature Switzerland AG 2019.

2019

A Version of Libviso2 for Central Dioptric Omnidirectional Cameras with a Laser-Based Scale Calculation

Autores
Aguiar, A; dos Santos, FN; Santos, L; Sousa, A;

Publicação
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract

2019

Deep Learning Applications in Agriculture: A Short Review

Autores
Santos, L; Santos, FN; Oliveira, PM; Shinde, P;

Publicação
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract