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Detalhes

Detalhes

003
Publicações

2020

Forest Robot and Datasets for Biomass Collection

Autores
Reis, R; dos Santos, FN; Santos, L;

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

Abstract

2020

Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

Autores
Santos, L; Santos, F; Mendes, J; Costa, P; Lima, J; Reis, R; Shinde, P;

Publicação
Robotica

Abstract
SummarySteep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

2020

Visual Trunk Detection Using Transfer Learning and a Deep Learning-based Coprocessor

Autores
Aguiar, AS; Dos Santos, FN; Miranda De Sousa, AJM; Oliveira, PM; Santos, LC;

Publicação
IEEE Access

Abstract

2020

Path Planning for ground robots in agriculture: a short review

Autores
Santos, LC; Santos, FN; Solteiro Pires, EJS; Valente, A; Costa, P; Magalhaes, S;

Publicação
2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Abstract

2020

Vineyard trunk detection using deep learning – An experimental device benchmark

Autores
Pinto de Aguiar, ASP; Neves dos Santos, FBN; Feliz dos Santos, LCF; de Jesus Filipe, VMD; Miranda de Sousa, AJM;

Publicação
Computers and Electronics in Agriculture

Abstract