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Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop)

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Operations and Engineering".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13183

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Special Issue Editors

Dr. David Portugal

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Guest Editor

Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra-Polo II, 3030-290 Coimbra, Portugal
Interests: multi-robot systems; mobile robotics; localization; mapping; graph theory; sensor fusion; artificial perception

Dr. João Filipe Ferreira

E-Mail Website
Guest Editor

Computational Neuroscience and Cognitive Robotics Research Group (CNCR), Department of Computer Science, School of Science and Technology, Nottingham Trent University, Nottingham NG1 4FQ, UK
Interests: artificial perception and cognition in robotics

Dr. Francisco J. Yandún

E-Mail Website
Guest Editor

Field Robotics Center, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213-3890, USA
Interests: agricultural machinery; image processing; Bayesian estimation; sensor fusion; machine learning

Special Issue Information

Dear Colleagues,

The increasing lack of manpower and the progressive abandonment of rural areas and practices such as pastoralism have driven forestry to become increasingly mechanized in order to increase productivity. However, the development of autonomous robotic solutions for precision forestry is still at a very early stage. Harsh conditions have thus far made it impossible to provide effective and realistic means to assist human teams in forestry operations using robots.

This Special Issue will showcase this exciting and important area of application, reflecting the current state of the art by addressing the challenges faced and presenting recent achievements in research on forestry robotics. It will bring together some of the contributions presented at the IEEE ICRA 2022 Workshop on Innovation in Forestry Robotics: Research and Industry Adoption (IFRRIA, https://labs.ri.cmu.edu/ifrria-icra-2022/).

Potential topics include, but are not limited to:

Artificial sensing and perception for forestry robotics;
Multisensory systems and fusion (LIDAR, depth cameras, multispectral imaging, etc.);
Neural networks and machine learning approaches (semantic mapping, object detection, dynamic tracking, etc.);
Localization and mapping;
Inventory, monitoring and wildfire fighting applications;
Outdoor (semi-)autonomous navigation, traversability and locomotion of UGVs and/or UAVs for robots in the wilderness;
Multi-robot and swarm robotic systems: coordination, formation control, cooperative perception, teamwork and communication aspects, and forest exploration;
Planning and decision-making architectures for forestry robotics under high uncertainty;
Design of autonomous ground machines and aerial systems for forestry;
Self-adaptation and learning in forestry environments;
Reasoning, planning and decision-making for forestry operations;
Human-robot interaction and safe co-operation for forestry;
Datasets, benchmarking, evaluation methods and innovate methodologies in forestry missions;
Ethical, legal, societal, economic and safety aspects of autonomous forestry machinery operation and development.

Dr. David Portugal
Dr. João Filipe Ferreira
Dr. Francisco J. Yandún
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

field robotics
precision forestry
forest machinery
artificial perception
multi-robot systems

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Published Papers (5 papers)

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Research

22 pages, 16972 KiB

Open AccessArticle

Forest Vegetation Detection Using Deep Learning Object Detection Models

by Paulo A. S. Mendes, António Paulo Coimbra and Aníbal T. de Almeida

Forests 2023, 14(9), 1787; https://doi.org/10.3390/f14091787 - 1 Sep 2023

Cited by 4 | Viewed by 1852

Abstract

Forest fires have become increasingly prevalent and devastating in many regions worldwide, posing significant threats to biodiversity, ecosystems, human settlements, and the economy. The United States (USA) and Portugal are two countries that have experienced recurrent forest fires, raising concerns about the role of forest fuel and vegetation accumulation as contributing factors. One preventive measure which can be adopted to minimize the impact of the forest fires is to cut the amount of forest fuel available to burn, using autonomous Unmanned Ground Vehicles (UGV) that make use of Artificial intelligence (AI) to detect and classify the forest vegetation to keep and the forest fire fuel to be cut. In this paper, an innovative study of forest vegetation detection and classification using ground vehicles’ RGB images is presented to support autonomous forest cleaning operations to prevent fires, using an Unmanned Ground Vehicle (UGV). The presented work compares two recent high-performance Deep Learning methodologies, YOLOv5 and YOLOR, to detect and classify forest vegetation in five classes: grass, live vegetation, cut vegetation, dead vegetation, and tree trunks. For the training of the two models, we used a dataset acquired in a nearby forest. A key challenge for autonomous forest vegetation cleaning is the reliable discrimination of obstacles (e.g., tree trunks or stones) that must be avoided, and objects that need to be identified (e.g., dead/dry vegetation) to enable the intended action of the robot. With the obtained results, it is concluded that YOLOv5 presents an overall better performance. Namely, the object detection architecture is faster to train, faster in inference speed (achieved in real time), has a small trained weight file, and attains higher precision, therefore making it highly suitable for forest vegetation detection. Full article

(This article belongs to the Special Issue Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop))

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20 pages, 46243 KiB

Open AccessArticle

Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention

by Maria Eduarda Andrada, David Russell, Tito Arevalo-Ramirez, Winnie Kuang, George Kantor and Francisco Yandun

Forests 2023, 14(8), 1601; https://doi.org/10.3390/f14081601 - 8 Aug 2023

Cited by 6 | Viewed by 2028

Abstract

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. Full article

(This article belongs to the Special Issue Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop))

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23 pages, 4114 KiB

Open AccessArticle

Design and Implementation of a Control System for an Autonomous Reforestation Machine Using Finite State Machines

by Morgan Rossander and Håkan Lideskog

Forests 2023, 14(7), 1340; https://doi.org/10.3390/f14071340 - 29 Jun 2023

Cited by 4 | Viewed by 2433

Abstract

Reforestation is performed after the final felling as an important and often law-mandated step to ensure that wood production is sustainable. In Sweden alone, over 400 millions seedlings are planted annually. This work is physically demanding and the quality is uneven. Therefore, automatic production systems are under research and development. A necessary effort in this endeavor is presented in this paper: the development and evaluation of a mission supervisor utilized to control the mission and behavior of a full-scale autonomous forest regeneration machine tested in realistic environments. The mission supervisor is implemented in the Robot Operating System framework using a finite state machine package called SMACH. A terrain machine built as a research platform with an added full-scale forwarder crane is used as a base machine. First, we describe the scenario in which planting is conducted, whereupon we develop the composite tasks required as states. A simplified simulator then enables an intermediate step before field experiments. The system is implemented and operated in real time on a full-scale machine. Results show that the developed SMACH mission supervisor can be used as a sound basis for an autonomous forest regeneration machine and the chosen communication solution between different systems works well. The simulations show good agreement with the experiments. The results also show that crane movements take 70% of the machine time, emphasizing the importance of limiting crane movement, improving the actuator movement speed and integrating the composite solutions. Further development with a holistic approach is required before the concept can reach the prototype level. Full article

(This article belongs to the Special Issue Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop))

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21 pages, 3306 KiB

Open AccessArticle

End-to-End Learning for Visual Navigation of Forest Environments

by Chaoyue Niu, Klaus-Peter Zauner and Danesh Tarapore

Forests 2023, 14(2), 268; https://doi.org/10.3390/f14020268 - 31 Jan 2023

Cited by 1 | Viewed by 2874

Abstract

Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day. Full article

(This article belongs to the Special Issue Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop))

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22 pages, 2157 KiB

Open AccessArticle

Comparative Evaluation of Mobile Platforms for Non-Structured Environments and Performance Requirements Identification for Forest Clearing Applications

by João Luís Lourenço, Luís Conde Bento, António Paulo Coimbra and Aníbal T. De Almeida

Forests 2022, 13(11), 1889; https://doi.org/10.3390/f13111889 - 10 Nov 2022

Cited by 1 | Viewed by 1987

Abstract

The effort to automate is present across all industries. It has an economic purpose but potential impacts go far beyond economics. Research has been carried out and a lot of investment has been made in automation in a variety of industries, as well as in agriculture and forestry, which resulted in efficient solutions for diverse applications. In fact, more solutions have emerged in the field of agriculture than in any other. This can be explained in economic terms, but also in light of the complex navigation required because of unstructured environments such as forests. This paper provides a comprehensive review of existing mobile platforms and presents a comparative study for an application in forest clearing. We evaluate the size, automation levels, traction, energy source, locomotion systems, sensors/actuators availability and tools that such an application must have to succeed in its function. Hence, it will be possible to evaluate the feasibility of retrofitting an existing platform into an electric unmanned ground vehicle for forest clearing or if it is easier to start development from scratch. The evaluation results reveal that an electric unmanned ground vehicle for forest clearing is currently unavailable in the market and that a new platform is needed. The performance requirements for such a platform are identified and proposed in the paper. Full article

(This article belongs to the Special Issue Forestry Robotics and Digital Forest Operations (Special Issue in Collaboration with ICRA 2022 IFRRIA Workshop))

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