2020
DOI: 10.1109/lra.2020.2967306
|View full text |Cite
|
Sign up to set email alerts
|

A Navigation Architecture for Ackermann Vehicles in Precision Farming

Abstract: In this letter, inspired by the needs of the European H2020 Project PANTHEON 1 , we propose a full navigation stack purposely designed for the autonomous navigation of Ackermann steering vehicles in precision farming settings. The proposed stack is composed of a local planner and a pose regulation controller, both implemented in ROS. The local planner generates, in real-time, optimal trajectories described by a sequence of successive poses. The planning problem is formulated as a real-time cost-function minimi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
19
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
5
3
1

Relationship

1
8

Authors

Journals

citations
Cited by 30 publications
(19 citation statements)
references
References 24 publications
0
19
0
Order By: Relevance
“…Both terrestrial and aerial autonomous robotic platforms are constantly collecting large quantities of geospatial data, which are centrally processed to support the execution of common orchard maintenance tasks, such as irrigation. The need for interdisciplinary data integration resulted in the publication of a pyoints python library [31], which bridges different representations of geometric point-based data, including point clouds and geo-referenced rasters, as well as the voxels required by the prototype of farming robots aimed at precision agriculture applications [32].…”
Section: Composite Voxel Model (Cvm)mentioning
confidence: 99%
“…Both terrestrial and aerial autonomous robotic platforms are constantly collecting large quantities of geospatial data, which are centrally processed to support the execution of common orchard maintenance tasks, such as irrigation. The need for interdisciplinary data integration resulted in the publication of a pyoints python library [31], which bridges different representations of geometric point-based data, including point clouds and geo-referenced rasters, as well as the voxels required by the prototype of farming robots aimed at precision agriculture applications [32].…”
Section: Composite Voxel Model (Cvm)mentioning
confidence: 99%
“…Nowadays, robotic solutions combined with computer vision can be used to automate this repetitive inspection task, increasing the reliability, maximizing the health of crops and optimizing the use of pesticides to as little as 5%-10% [6]. For that purpose, robots need to implement plenty of different tasks such as localize themselves [7] and navigate inside greenhouses [8]- [9]; acquire quality pictures to identify pests and their locations [10]; or process the obtained results to generate efficient highlevel instructions to command the robot according to an Integrated Pest Management (IPM) system [11]. However, most research works focus on individual problems neglecting its integration within a single complete solution.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, to achieve robust autonomy, the robot must be able to adapt any mission level plan in response to the presence of both unknown and dynamic elements in these unstructured environments. Currently, autonomous field robots are limited to use in more structured environments, including row crops [1] or orchards [2], where dynamic elements such as moving individuals are not a critical consideration. These approaches generally make use of reactive planners, which can adapt to unforeseen static obstacles.…”
Section: Introductionmentioning
confidence: 99%