Boustrophedon coverage path planning for UAV aerial surveys in wind

Coombes, Matthew; Chen, Wenhua; Liu, Cunjia

doi:10.1109/icuas.2017.7991469

Cited by 54 publications

(40 citation statements)

References 17 publications

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“…The time taken for this full turn maneuver

(t_{t}^{j})

consists of the time taken to fly each trochoidal turn

(t_{1}^{j}, t_{2}^{j})

, and the straight tangential path between them

(t_{3}^{j})

, shown in Figure and Equation . For further detail about the derivations of these terms, please refer to Coombes et al () and Techy and Woolsey ().

t_{t, c}^{j} = t_{1}^{j} + t_{2}^{j} + t_{3}^{j} .

…”

Section: Cost Function—flight Time In Wind (Ftiw)mentioning

confidence: 99%

“…In addition to effectively trying to limit the number of turns, it also has the effect to limit the total number of decomposed cells which serves to decrease the extra wasted transit time between them. However, the wind strength and the flight path angle to the wind will have a massive impact on the flight time of a survey (Coombes, Chen, & Liu, ; Hovenburg, de Alcantara Andrade, Rodin, Johansen, & Storvold, ; Richards, ) due to high wind velocity relative to airspeed (typically 20%–50%). Flight times can be significantly improved by flying perpendicular to the wind direction (Coombes, Fletcher, Chen, & Liu, ).…”

Section: Introductionmentioning

confidence: 99%

“…However, the wind strength and the flight path angle to the wind will have a massive impact on the flight time of a survey (Coombes, Chen, & Liu, 2017;Hovenburg, de Alcantara Andrade, Rodin, Johansen, & Storvold, 2018;Richards, 2018) due to high wind velocity relative to airspeed (typically 20%-50%). Flight times can be significantly improved by flying perpendicular to the wind direction (Coombes, Fletcher, Chen, & Liu, 2018).…”

mentioning

confidence: 99%

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Decomposition‐based mission planning for fixed‐wing UAVs surveying in wind

Coombes

Fletcher

Chen

et al. 2019

Journal of Field Robotics

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This paper presents a new method for planning fixed‐wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back‐and‐forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top‐down recursive greedy approach is used to traverse the search space to minimize the flight time of the survey. This optimization can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offers greatly reduced survey times in the wind. Other improved cost functions have been developed to take into account real‐world problems, for example, No‐Fly Zones, in addition to flight time. A number of real surveys are performed to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real‐world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multirotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of an unmanned aerial vehicle aerial survey.

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“…The time taken for this full turn maneuver

(t_{t}^{j})

consists of the time taken to fly each trochoidal turn

(t_{1}^{j}, t_{2}^{j})

, and the straight tangential path between them

(t_{3}^{j})

, shown in Figure and Equation . For further detail about the derivations of these terms, please refer to Coombes et al () and Techy and Woolsey ().

t_{t, c}^{j} = t_{1}^{j} + t_{2}^{j} + t_{3}^{j} .

…”

Section: Cost Function—flight Time In Wind (Ftiw)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Decomposition‐based mission planning for fixed‐wing UAVs surveying in wind

Coombes

Fletcher

Chen

et al. 2019

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The path consists of two different states of flight; the straight sweep paths where the images are captured, and the turn manoeuvre used to transition between sweeps. This is laid out in detail in [18], and as such, will only have a brief overview here.…”

Section: Coverage Path Planningmentioning

confidence: 99%

“…It plots a full Boustrophedon path in the same orientation across the whole concave field polygon, then calculates a time cost in wind for each sweep and uses a modified travelling salesman solver to find the fastest order and direction to traverse all the sweeps without forcing the aircraft to perform manoeuvres that it is incapable of. The previous work by this author presents a similar flight time model for calculating flight time for each portion of the flight in wind [18]. This is then extended too more complex concave fields by optimally decomposing the concave polygon into multiple simple convex polygons, which can be fully imaged using a Boustrophedon path [16].…”

Section: Introductionmentioning

confidence: 99%

Flight Testing Boustrophedon Coverage Path Planning for Fixed Wing UAVs in Wind

Coombes

Chen

Liu

2019

2019 International Conference on Robotics and Automation (ICRA)

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A method was previously developed by this author to optimise the flight path of a fixed wing UAV performing aerial surveys of complex concave agricultural fields. This relies heavily on a flight time in wind prediction model as its cost function. This paper aims to validate this model by comparing flight test results with the model prediction. There are a number of assumptions that this model relies on. The major assumption is that wind is steady and uniform over the small area and time scales involved in a survey. To show that this is reasonable, wind fields measurements will be taken from a multi rotor UAV with an ultrasonic windspeed sensor.

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Mountain search and recovery: An unmanned aerial vehicle deployment case study and analysis

Schomer,

Adams

2024

Journal of Field Robotics

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Mountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low‐cost, consumer‐grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability‐prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert‐informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state‐of‐the‐art robotics and MSAR.

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Boustrophedon coverage path planning for UAV aerial surveys in wind

Cited by 54 publications

References 17 publications

Decomposition‐based mission planning for fixed‐wing UAVs surveying in wind

Decomposition‐based mission planning for fixed‐wing UAVs surveying in wind

Flight Testing Boustrophedon Coverage Path Planning for Fixed Wing UAVs in Wind

Mountain search and recovery: An unmanned aerial vehicle deployment case study and analysis

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