A review on multirobot systems in agriculture

Ju, Chanyoung; Kim, Jeongin; Seol, Jaehwi; Son, Hyoung Il

doi:10.1016/j.compag.2022.107336

Cited by 57 publications

(14 citation statements)

References 147 publications

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“…Arguably, quadcopter models are the most used in orchard monitoring but hexacopters, even if larger and more expensive, are becoming increasingly popular due to propeller redundancy which leads to better stabilization in nominal functioning and increased reliability under hardware loss. A synthetic description of the kinematic and dynamic models of multicopters is given by ( Ju et al., 2022 ).…”

Section: Uavs and Cameras Used For Image Acquisition In Orchard Monit...mentioning

confidence: 99%

“…The automatic picking of fruits is an exception. In the future, the use of complex multirobot systems that combine the actions of UAVs, ground robots, and manipulators ( Sulistijono et al, 2020 ; Ju et al., 2022 ) can lead to an increased degree of automation in modern orchards. However, research papers related to the application of artificial intelligence and the use of drones (UAVs) in the monitoring of orchards are relatively few compared to the monitoring of flat, field crops.…”

Section: Introductionmentioning

confidence: 99%

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Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Popescu,

Ichim,

Stoican

2023

Front. Plant Sci.

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Orchard monitoring is a vital direction of scientific research and practical application for increasing fruit production in ecological conditions. Recently, due to the development of technology and the decrease in equipment cost, the use of unmanned aerial vehicles and artificial intelligence algorithms for image acquisition and processing has achieved tremendous progress in orchards monitoring. This paper highlights the new research trends in orchard monitoring, emphasizing neural networks, unmanned aerial vehicles (UAVs), and various concrete applications. For this purpose, papers on complex topics obtained by combining keywords from the field addressed were selected and analyzed. In particular, the review considered papers on the interval 2017-2022 on the use of neural networks (as an important exponent of artificial intelligence in image processing and understanding) and UAVs in orchard monitoring and production evaluation applications. Due to their complexity, the characteristics of UAV trajectories and flights in the orchard area were highlighted. The structure and implementations of the latest neural network systems used in such applications, the databases, the software, and the obtained performances are systematically analyzed. To recommend some suggestions for researchers and end users, the use of the new concepts and their implementations were surveyed in concrete applications, such as a) identification and segmentation of orchards, trees, and crowns; b) detection of tree diseases, harmful insects, and pests; c) evaluation of fruit production, and d) evaluation of development conditions. To show the necessity of this review, in the end, a comparison is made with review articles with a related theme.

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Section: Uavs and Cameras Used For Image Acquisition In Orchard Monit...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Popescu,

Ichim,

Stoican

2023

Front. Plant Sci.

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“…The multi-machine cooperative technology has garnered extensive recognition from both domestic and international researchers. Its primary components encompass positioning and communication technology, path planning technology, and control technology [5]. The positioning and communication sector is responsible for acquiring and exchanging critical data such as the position and heading of both the subject vehicle and others in the vicinity.…”

Section: Introductionmentioning

confidence: 99%

A Versatile Control Method for Multi-Agricultural Machine Cooperative Steering Applicable to Two Steering Modes

Zhu,

Zhang,

Kong

et al. 2024

WEVJ

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This article aims to address the unnecessary stopping and low efficiency issues present in existing multi-machine cooperative steering control methods. To tackle this challenge, a novel cooperative control approach for multiple agricultural machines is proposed, considering two typical steering modes of farm machinery. This approach encompasses a multi-machine cooperative control framework suitable for both steering modes. Based on the established lateral and longitudinal kinematics models of the farm machines, the method includes a path-tracking controller designed using the pure pursuit and Stanley algorithms, a formation-keeping controller based on PID control, and a T-turn cooperative-steering controller based on a problem-solving approach. To assess the method’s viability, a collaborative simulation platform utilizing CarSim and Simulink was constructed, which conducted simulations for both U-turn and T-turn cooperative steering controls. The simulation results indicate that the proposed control framework and methodology can effectively ensure no collision risk during the U-turn and T-turn cooperative steering processes for three farm machines, eliminating stopping in T-turn, enhancing safety, and improving fuel economy. Compared with traditional sequential control methods, the proposed approach reduced operation time by 17.47 s and increased efficiency by 15.29% in the same scenarios.

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“…Multi-Robot Systems (MRS) have been used in several applications and industries. Agriculture [2], monitoring [3], search and rescue [4], target detection [5], product pick-up and delivery in distribution centers [6], and health care [7] are some pioneering MRS applications. Further, Multi-Robot Task Allocation (MRTA) is one of the most interesting topics of MRS.…”

Section: Introductionmentioning

confidence: 99%

A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

Chakraa,

Leclercq,

Guérin

et al. 2023

IEEE Access

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Recently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case of major events, such as chemical spills, oil rig explosions, or nuclear accidents, collecting accurate and rapidlyevolving data becomes a challenging task. So, coordinating a fleet of autonomous mobile robots is a very promising way to deal with unpredicted events and also prevent malicious actions. This paper addresses the problem of assigning optimally a set of tasks to a set of mobile robots equipped with different sensors to minimize a global objective function. The robots perform sensing tasks in order to monitor the area and to facilitate firefighters and inspectors work if a disaster occurs by providing the necessary measures. For this purpose, a centralized Genetic Algorithm (GA) is proposed to determine the task each robot will perform and the order of execution. The proposed approach is tested through a simulation scenario of a grid map environment that represents an industrial area of the city of Le Havre, France. Moreover, a comparative study is conducted with the Hybrid Filtered Beam Search (HFBS) approach and the Mixed-Integer Linear Programming (MILP) solver Cplex. The results demonstrate that the GA approach offers a favorable balance between optimality and execution time.

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A review on multirobot systems in agriculture

Cited by 57 publications

References 147 publications

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

A Versatile Control Method for Multi-Agricultural Machine Cooperative Steering Applicable to Two Steering Modes

A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

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