An Adaptive Row Crops Path Generator with Deep Learning Synergy

Cerrato, Simone; Aghi, Diego; Mazzia, Vittorio; Salvetti, Francesco; Chiaberge, Marcello

doi:10.1109/acirs52449.2021.9519316

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…7 shows some examples of full-coverage path planning. The planning is performed by selecting the points in an A-B-B-A fashion and using the planner proposed by [5]. With geo-referenced maps, the planned path can be converted from the image reference system to a Global Navigation Satellite System (GNSS) reference frame to be used in real-world navigation.…”

Section: Qualitative Resultsmentioning

confidence: 99%

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Salvetti¹,

Angarano²,

Martini³

et al. 2022

Preprint

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The development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays a predominant role in this evolution by deploying autonomous agents able to navigate in fields while executing different tasks without the need for human intervention, such as monitoring, spraying and harvesting. In this context, global path planning is the first necessary step for every robotic mission and ensures that the navigation is performed efficiently and with complete field coverage. In this paper, we propose a learning-based approach to tackle waypoint generation for planning a navigation path for row-based crops, starting from a top-view map of the region-of-interest. We present a novel methodology for waypoint clustering based on a contrastive loss, able to project the points to a separable latent space. The proposed deep neural network can simultaneously predict the waypoint position and cluster assignment with two specialized heads in a single forward pass. The extensive experimentation on simulated and real-world images demonstrates that the proposed approach effectively solves the waypoint generation problem for both straight and curved row-based crops, overcoming the limitations of previous state-of-the-art methodologies.

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Section: Qualitative Resultsmentioning

confidence: 99%

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Salvetti¹,

Angarano²,

Martini³

et al. 2022

Preprint

Self Cite

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“…Firstly, the system takes as input a georeferenced occupancy grid map of the considered crop to compute a global path made of geographic coordinates; in particular, it exploits the DeepWay network, [1], to estimate the start/end waypoints of each row, then a custom global path planner, [38], computes the desired path maintaining a safe distance from crops. Secondly, according to the season period and the amount of crop vegetation, it is possible to choose the kind of navigation to perform: only GNSS-based or GNSS and AI-assisted.…”

Section: System Overviewmentioning

confidence: 99%

“…The global path is generated from the ordered list W ord in two steps. The intra-row paths are obtained with [38], which exploits a gradient-based planner between the starting and the ending waypoints of each row. On the other hand, the interrow paths are generated with a circular pattern in order to keep a safe margin from the end of the rows to avoid collision during the turns.…”

Section: B Global Path Planningmentioning

confidence: 99%

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

Cerrato¹,

Mazzia²,

Salvetti³

et al. 2021

Preprint

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Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental requirement to employ service robotics in most fields of application. Among all, researchers in the precision agriculture domain strive to devise robust and cost-effective autonomous platforms in order to provide genuinely large-scale competitive solutions. In this article, we present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. Firstly, we build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field. Moreover, our solution leverages on latest advancement of deep learning optimization techniques and synthetic generation of data to provide an affordable solution that efficiently tackles the well-known Global Navigation Satellite System unreliability and degradation due to vegetation growing inside rows. Extensive experimentation and simulations against computer-generated environments and real-world crops demonstrated the robustness and intrinsic generalizability of our methodology that opens the possibility of highly affordable and fully autonomous machines.

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“…This is done through training deep learning models with extensive examples of images of different environmental scenery to improve future predictions. Many recent studies have utilized deep learning models as part of their autonomous navigation system in agriculture (Adhikari et al, 2020;Aghi et al, 2020;Bah et al, 2020;Cerrato et al, 2021;de Silva et al, 2021;Doha et al, 2021). For example, Adhikari et al (2020) used a limited dataset and deep learning to develop a neural network that is robust against shadows, crop growth stages, and row spacing, however, it failed to generalize well to areas where crops had occluded the path.…”

mentioning

confidence: 99%

The integration of GPS and visual navigation for autonomous navigation of an Ackerman steering mobile robot in cotton fields

Mwitta,

Rains

2024

Front. Robot. AI

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Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields.

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An Adaptive Row Crops Path Generator with Deep Learning Synergy

Cited by 6 publications

References 16 publications

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

The integration of GPS and visual navigation for autonomous navigation of an Ackerman steering mobile robot in cotton fields

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