Complete coverage path planning in an agricultural environment

Driscoll, Theresa Marie

doi:10.31274/etd-180810-2966

Cited by 8 publications

(15 citation statements)

References 22 publications

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“…The mission planner is part of the i-LEED software, and allows designing of missions for the car-like robots depicted in Figure 2, with the aim to build feasible trajectories for a particular paddock while also considering the mode of coverage (partial, complete, or targeted spots). However, although trajectory planning of car-like vehicles has been widely addressed in the literature, especially in agriculture, most of the proposed approaches consider only the kinematic of the robot and the shape of the field, for example to minimize the time spent and increase the coverage efficiency (see Driscoll (2011) and Oksanen (2007)), as well as avoid obstacles (see Jimenez, Shirinzadeh, Nicholson, and Alici, (2007) and Zhou, Jensen, Sorensen, Busato, and Bochtis, (2014)). The consideration of dynamic parameters, as the speed of the vehicle, its maximum yaw rate, and the maximally allowed transverse acceleration to limit the lateral sliding of the vehicle during turns is however essential for the definition of fully feasible trajectories, especially during the half-turn maneuvers (e.g., a trajectory feasible for the robot at low speed cannot be feasible at higher speed).…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile Robot Trajectory Planning Under Kinematic and Dynamic Constraints for Partial and Full Field Coverage

Cariou¹,

Gobor

Seiferth

et al. 2017

Journal of Field Robotics

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The constantly rising food demand of a steadily increasing world population requires improvement in efficiency, competitiveness, and productivity of current meat and dairy production systems. Thus, robotics‐based approaches have an important role to play, especially in dairy cattle farming, because the intensive grazing systems depend on numerous time‐consuming and tedious operations required to be carried out to assure an optimal cattle feeding as well as utilization of forage resources. These operations range from data acquisition considering the amount and quality of available forage within the paddocks, to carrying out maintenance operations in order to safeguard high yield with required quality and availability of the forage throughout the whole grazing season. This issue is addressed within the ICT‐AGRI project i‐LEED, in which one of the main tasks is to build accurate and feasible trajectories for a scouting and a maintenance robot to fully or partially cover the paddocks, as well as to reach only targeted spots previously located. This paper presents an original and fully operational method for trajectory planning by designing segments of clothoids while taking into account additional dynamic constraints, such as the steering rate capacity of the robot, its speed, and the maximally allowed transverse acceleration. In case of specific points to reach within a paddock, this approach is completed by morphological operations to first define regions and next rank them w.r.t. the minimal length of travel by solving the traveling salesman problem. Based on the kinematic and dynamic properties of the scouting and maintenance robots devoted to the i‐LEED project, the performances of the proposed planning approaches are presented.

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Section: Literature Reviewmentioning

confidence: 99%

Mobile Robot Trajectory Planning Under Kinematic and Dynamic Constraints for Partial and Full Field Coverage

Cariou¹,

Gobor

Seiferth

et al. 2017

Journal of Field Robotics

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“…Inspired from the literature about "coverage path planning" [14,[15][16][17], it would be interesting to consider if capacitated vehicle routing problem models could bring added value in the remediation work, in the case of soil excavation by heavy equipment.…”

Section: Improvements and Future Developmentmentioning

confidence: 99%

“…In the case of ex-situ 2 remediation, literature does not mention the use of navigation and positioning technologies for the excavation work done by heavy machineries. As positioning technologies are routinely employed in civil engineering and agriculture [14,[15][16][17][18] for the guidance of heavy equipment for precise and efficient work it seems the shortcomings in the case of ex-situ remediation lays in the capacity to generate adequate remediation plans and in the lack of adapted GIS tools, models, methods and practice [19]. In response, this work develops models in order to be able to produce a plan containing "clean-up parcels" and derived navigation data.…”

Section: Introductionmentioning

confidence: 99%

Development and testing of geo-processing models for the automatic generation of remediation plan and navigation data to use in industrial disaster remediation

Lucas

Lénárt

Solymosi

2016

Open geospatial data, softw. stand.

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Background: This paper introduces research done on the automatic preparation of remediation plans and navigation data for the precise guidance of heavy machinery in clean-up work after an industrial disaster. The input test data consists of a pollution extent shapefile derived from the processing of hyperspectral aerial survey data from the Kolontár red mud disaster. Methods: Five algorithms were developed, the respective scripts were written in Python, and then tested. The first model aims at drawing a parcel clean-up plan. It tests four different parcel orientations (0, 90, 45 and 135°) and keeps the plan where clean-up parcels are less numerous. The second model uses the orientation of each contamination polygon feature to orientate the features of the clean-up plan accordingly. The third model tested if it is worth rotating the parcel features by 90°for some contamination feature. The fourth model drifts the clean-up parcel of a work plan following a grid pattern; here also with the belief to reduce the final number of parcel features. The last model aims at drawing a navigation line in the middle of each clean-up parcel. Results: The best optimization results were achieved with the second model; the drift and 90°rotation models do not offer significant advantage. By comparison of the results between different orientations we demonstrated that the number of clean-up parcels generated varies in a range of 4 to 38 % from plan to plan. Conclusions: Such a significant variation with the resulting feature numbers shows that the optimal orientation identification can result in saving work, time and money in remediation.

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“…Hameed et al [6] developed a novel side-to-side 3D coverage path planning method that ensures zero skips/overlaps regardless of the topographical nature of the field terrain, and saves a significant percentage of uncovered area if an appropriate driving angle is chosen. Driscoll [3] derived an algorithm for solving the optimal complete coverage problem on a field boundary with n sides. Jin and Tang [8] reported on a path planning algorithm based on a developed geometric model for generating an optimized full-coverage pattern for a given 2D field by using Boustrophedon paths.…”

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confidence: 99%

Optimum harvesting area of convex and concave polygon field for path planning of robot combine harvester

Rahman

Ishii

Noguchi

2019

Intel Serv Robotics

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This paper presents an optimum harvesting area of a convex and concave polygon for the path planning of a robot combine harvester. A convenient optimum harvesting area for a convex and concave polygon is proposed. The notion is that path planning specifically for a robot combine harvester is required to choose the crop field optimum harvesting area; otherwise, crop losses may occur during harvesting of the field. For a safe turning margin of the robot combine harvester, the surrounding crop near the boundary zone is cut twice or thrice by manual operation. However, this surrounding cutting crop is not exactly straight, and sometimes it is curved or meanders. In addition, path planning with a conventional AB point method in order to take a corner position from the global positioning system by visual observation is a time-consuming operation. A curved or meandering crop is not cut and left in the field during harvesting, and the harvesting area is not optimum. Therefore, a suitable N-polygon algorithm and split of convex hull and cross-point method for determining the optimum harvesting area for path planning are proposed, which reduce the crop losses in the field. The results show that this developed algorithm estimates the optimum harvesting area for a convex or concave polygon field and its corner vertices, takes all crop portions, and reduces crop losses. It is also illustrated that the working path calculated based on the corner vertices minimizes the total operational processing time. Keywords Convex polygon • Concave polygon • Optimum harvesting area • Path planning • Robot combine harvester 1 Introduction Development of an agricultural robot involves making the operator's work easier in the agricultural industry. The concept of a robot is required owing to the decreasing agricultural B Kazunobu Ishii

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Complete coverage path planning in an agricultural environment

Cited by 8 publications

References 22 publications

Mobile Robot Trajectory Planning Under Kinematic and Dynamic Constraints for Partial and Full Field Coverage

Mobile Robot Trajectory Planning Under Kinematic and Dynamic Constraints for Partial and Full Field Coverage

Development and testing of geo-processing models for the automatic generation of remediation plan and navigation data to use in industrial disaster remediation

Optimum harvesting area of convex and concave polygon field for path planning of robot combine harvester

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