GIS Ostrava 2021 – Advances in Localization and Navigation March 17–19, 2021 TOWARDS DEVELOPMENT AND VERIFICATION OF ADVANCED OPTIMAL FARM MACHINERY ROUTE ALGORITHM (original) (raw)
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Towards Development and Verifications of Advanced Optimal Farm Machinery Route Algorithm
2021
Efforts related to minimising the environmental burden caused by agricultural activities are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using on-line optimization of route planning for soil-sensitive field operations. The research presented in this paper aims at optimizing farm machinery routes to minimize the environmental burden. As such, it further advances existing CTF solutions by its complexity including (1) efficient field divisions, (2) U-turns in headlands, (3) obstacles in a farm machinery route and (4) terrain specifics. The developed algorithm is expressed as UML activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories. The deve...
2021
Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were...
Journal of Intelligent & Robotic Systems, 2013
Field operations should be done in a manner that minimizes time and travels over the field surface. Automated and intelligent path planning can help to find the best coverage path so that costs of various field operations can be minimized. The algorithms for generating an optimized field coverage pattern for a given 2D field has been investigated and reported. However, a great proportion of farms have rolling terrains, which have a considerable influence on the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations and provide more precise navigation. Supplementary to that, energy consumption models were invoked taking into account terrain inclinations in order to provide the optimal driving direction for traversing the parallel field-work tracks and the optimal sequence for handling these tracks under the criterion of minimizing direct energy requirements. The reduced energy requirements and consequently the reduced emissions of atmospheric pollutants, e.g. CO 2 and NO, are of major concern due to their
Optimized routing on agricultural fields by minimizing maneuvering and servicing time
Precision Agriculture, 2013
Agricultural machines spend a significant part of their time on non-productive operations such as maneuvering near the boundaries of the field and loading or offloading of inputs or outputs (here referred to as servicing). This paper integrates existing methods for route optimization so as to minimize the time spent on turns and machine servicing on fields cultivated in straight rows. The following variables are optimized: (1) the orientation (angle) of the tracks, (2) the order of tracks, and (3) the types of turns between tracks. The angle of the tracks relative to field boundaries influences the number and lengths of the machine tracks, the number of turns and the positions where the machine can be serviced. Track order and the type of turns are selected to achieve overall efficiency. The algorithm was tested by computing routes for a set of fields of different sizes and assuming different operations. On small fields that do not require servicing, optimizing the turns between tracks resulted in a reduction of up to 50 % in turning time compared to the prevailing practice of navigation between adjacent tracks. A comparison of two sprayers in terms of servicing efficiency suggested that the algorithm can help selecting machinery for given field geometries. In some cases requiring machine servicing, the track orientation giving the shortest turning time did not produce the least servicing time. This illustrates that machine servicing should be taken into consideration for global optimization of machine traffic.
2017
Copyright © 2017 for this paper by its author Autonomous systems are a promising alternative for effectively executing agricultural field management strategies. Unmanned Ground Vehicles perform farming activities on custom agricultural fields, using real-time navigation. The aim of this study is to provide a software tool for optimizing accuracy and efficiency in precision farming activities, hence leading to improved farming output, while dynamically addressing operational and tactical level uncertainties. This paper contributes to the operations research field by allowing the application of simulated results direct to the guidance of a physical vehicle. Unlike existing sophisticated tools, the developed navigation mechanism is user-friendly and highly customizable at outdoor navigation.
Energy Savings from Optimised In-Field Route Planning for Agricultural Machinery
Sustainability
Various types of sensors technologies, such as machine vision and global positioning system (GPS) have been implemented in navigation of agricultural vehicles. Automated navigation systems have proved the potential for the execution of optimised route plans for field area coverage. This paper presents an assessment of the reduction of the energy requirements derived from the implementation of optimised field area coverage planning. The assessment regards the analysis of the energy requirements and the comparison between the non-optimised and optimised plans for field area coverage in the whole sequence of operations required in two different cropping systems: Miscanthus and Switchgrass production. An algorithmic approach for the simulation of the executed field operations by following both non-optimised and optimised fieldwork patterns was developed. As a result, the corresponding time requirements were estimated as the basis of the subsequent energy cost analysis. Based on the results, the optimised routes reduce the fuel energy consumption up to 8%, the embodied energy consumption up to 7%, and the total energy consumption from 3% up to 8%.
Minimising the non-working distance travelled by machines operating in a headland field pattern
Biosystems Engineering, 2008
When treating an area of field using agricultural equipment, the field is usually traversed by a series of parallel tracks using a pattern established by the experience of the operator. At the end of each track the process is constrained by the ability of the operator to distinguish the next track to be followed. The introduction of commercially available autosteering or navigation-aid systems for agricultural machines has made it possible to upload arbitrary field pattern sequences into programmable navigational computers and for the machines to follow them with precision. This new technology also offers a new perspective for improving machine field efficiency, since not all field traversal sequences are similar in terms of total non-working distance travelled. This paper presents an algorithmic approach towards computing traversal sequences for parallel field tracks, which improve the field efficiency of machines by minimising the total non-working distance travelled. Field coverage is expressed as the traversal of a weighted graph and the problem of finding an optimum traversing sequence is shown to be equivalent to finding the shortest route in the graph. The optimisation is formulated and solved as a binary integer programming problem. Experimental results show that by using optimum sequences, the total non-working distance can, depending on operation, be reduced by up to 50%.
An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs
Drones, 2020
This research paper focuses on providing an algorithm by which (Unmanned Aerial Vehicles) UAVs can be used to provide optimal routes for agricultural applications such as, fertilizers and pesticide spray, in crop fields. To utilize a minimum amount of inputs and complete the task without a revisit, one needs to employ optimized routes and optimal points of delivering the inputs required in precision agriculture (PA). First, stressed regions are identified using VegNet (Vegetative Network) software. Then, methods are applied for obtaining optimal routes and points for the spraying of inputs with an autonomous UAV for PA. This paper reports a unique and innovative technique to calculate the optimum location of spray points required for a particular stressed region. In this technique, the stressed regions are divided into many circular divisions with its center being a spray point of the stressed region. These circular divisions would ensure a more effective dispersion of the spray. Then an optimal path is found out which connects all the stressed regions and their spray points. The paper also describes the use of methods and algorithms including travelling salesman problem (TSP)-based route planning and a Voronoi diagram which allows applying precision agriculture techniques.
Navigational Path-Planning For All-Terrain Autonomous Agricultural Robot
ArXiv, 2021
The shortage of workforce and increasing cost of maintenance has forced many farm industrialists to shift towards automated and mechanized approach. The key component for autonomous systems is the path planning techniques used. Coverage path planning (CPP) algorithm is used for navigating over farmlands to perform various agricultural operations such as seeding, ploughing, or spraying pesticides and fertilizers. This report paper compares novel algorithms for autonomous navigation of farmlands. For reduction of navigational constraints, a high-resolution grid map representation is taken into consideration specific to Indian environments. The free space is covered by distinguishing the grid cells as covered, unexplored, partially explored and presence of obstacle. The performance of the compared algorithms is evaluated with metrics such as time efficiency, space efficiency, accuracy, and robustness to changes in the environment. Robotic Operating System (ROS), Dassault Systemes Exper...