Linear-Optimization-Based Path Planning Algorithm for an Agricultural UAV

Noriega, Alfonso; Anderson, Richard

doi:10.2514/6.2016-1003

Cited by 12 publications

(8 citation statements)

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“…The short distance between the data logger and the drone enables the usage of ultra-low power RF protocols for measurement data transmission providing only a short range [21]. Those systems could be extended by path planning techniques to automate the control of the drone that were designed for similar problems [22,23]. Idbella et al presented such a UAV based data logging approach for monitoring agro-ecological condition of vine plants [24].…”

Section: Uav Supported Data Loggingmentioning

confidence: 99%

Open Lorawan Sensor Node Architecture for Agriculture Applications

Bolte¹,

Witkowski²,

Morgenstern³

2021

Advances in Machine Learning

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In agriculture, it becomes more and more important to have detailed data, e.g. about weather and soil quality, not only in large scale classic crop farming applications but also for urban agriculture. This paper proposes a modular wireless sensor node that can be used in a centralized data acquisition scenario. A centralized approach, in this case multiple sensor nodes and a single gateway or a set of gateways, can be easily installed even without local infrastructure as mains supply. The sensor node integrates a LoRaWAN radio module that allows long-range wireless data transmission and low-power battery operation for several months at reasonable module costs. The developed wireless sensor node is an open system with focus on easy adaption to new sensors and applications. The proposed system is evaluated in terms of transmission range, battery runtime and sensor data accuracy.

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Section: Uav Supported Data Loggingmentioning

confidence: 99%

Open Lorawan Sensor Node Architecture for Agriculture Applications

Bolte¹,

Witkowski²,

Morgenstern³

2021

Advances in Machine Learning

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“…Table 1 reveals an increasing interest in UAVs in the field of agriculture in recent years, and most agricultural UAVs currently in use are single-UAV systems except for [19,21]. The main research areas are remote sensing [7,8,10,13,17,18,20,22], mapping [7,8,11,15], and monitoring [9,12,14,19,26], and it is not yet used in various areas such as sowing and harvesting. Furthermore, the research for irrigation and pest control is on the rise nowadays [16,27,28].…”

Section: Review About the Application Of Uav In Agriculturementioning

confidence: 99%

“…In sensors, RGB cameras [13][14][15]19], thermal cameras [7,8], and multi-spectral cameras [7][8][9][10][11][12]17,18,20,22] are used. Recent studies focused on agricultural UAVs through image processing, including preprocessing, onboard-processing and post-processing; thus, it is widely used in camera sensors.…”

Section: Review About the Application Of Uav In Agriculturementioning

confidence: 99%

“…However, when analyzing the existing application of UAV system for agriculture (see, for instance, [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]), most studies execute agricultural tasks using a single UAV with an autonomous control. There are few studies on the use of the multi-UAV system in performing agricultural works; thus, it is only at the advanced stage of research [19,21,23].…”

Section: Introductionmentioning

confidence: 99%

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Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

Son

2018

Electronics

148

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Abstract:The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

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“…However, current academic literature covering failure-related topics of multicopters mainly focuses on fault detection techniques and fault-tolerant control algorithms [10][11][12][13][14], which belong to a study of low-level subsystems. For the study of the high-level decision-making module, most research focuses on path planning [15][16][17] and obstacle avoidance [18,19] of an individual multicopter, or PHMbased mission allocation of a multicopter team [20,21]. However, few studies have focused on the failsafe mechanism design of an individual multicopter subject to multiple potential failures.…”

Section: Introductionmentioning

confidence: 99%

Failsafe mechanism design of multicopters based on supervisory control theory

Quan

Zhao

Lin

et al. 2020

IET cyber-systems robotics

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In order to handle undesirable failures of a multicopter which occur in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory is proposed for the semi-autonomous control mode 1 . Failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis, and other low-level subsystems. In order to design a failsafe mechanism for multicopters, safety issues of multicopters are introduced.Then, user requirements including functional requirements and safety requirements are textually described, where function requirements determine a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. In order to model the user requirements by discrete-event systems, several multicopter modes and events are defined.On this basis, the multicopter plant and control specifications are modeled by automata. Then, a supervisor is synthesized by monolithic supervisory control theory. In addition, we present three examples to demonstrate the potential blocking phenomenon due to inappropriate design of control specifications. Also, we discuss the meaning of correctness and the properties of the obtained supervisor. This makes the failsafe mechanism convincingly correct and effective. Finally, based on

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Linear-Optimization-Based Path Planning Algorithm for an Agricultural UAV

Cited by 12 publications

References 1 publication

Open Lorawan Sensor Node Architecture for Agriculture Applications

Open Lorawan Sensor Node Architecture for Agriculture Applications

Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

Failsafe mechanism design of multicopters based on supervisory control theory

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