GAOS: Spatial optimisation of crop and nature within agricultural fields

This paper proposes and demonstrates a spatial optimiser that allocates areas of inefficient machine manoeuvring to field margins thus improving the use of available space and supporting map-based Controlled Traffic Farming. A prototype web service (GAOS) allows farmers to optimise tracks within their fields and explore planning alternatives prior to downloading the plans to their RTK GPS-guided steering system. GAOS retrieves accurate data on field geometry from a geo-database. Via a web interface, the farmer sets options regarding operation properties, potential locations for field margins and headlands, etc. Next, an optimisation script that employs an open source geospatial library (osgeo.ogr) is called. The objective function considers costs involved with un-cropped areas, turning at headlands and subsidies received for field margins. Optimisation results are stored in a database and are available for (1) viewing via the web interface, (2) downloading to the GPS-guided steering system and (3) communication to third parties

Towards the development and verification of a 3D-based advanced optimized farm machinery trajectory algorithm

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 visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostenice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories

Intelligent Behavior of Autonomous Vehicles in Outdoor Environment

The objective of this PhD-project has been to develop and enhance the operational behaviour of autonomous or automated conventional machines under out-door conditions. This has included developing high-level planning measures for the maximisation of machine productivity as an important element in the continued efforts of planning and controlling resource inputs in both arable and high value crops farming. The methods developed generate the optimized coverage path for any field regardless of its complexity on 2D or 3D terrains without any human intervention and in a manner that minimizes operational time, skipped and overlapped areas, and fuel consumption. By applying the developed approaches, a reduction of more than 20% in consumed fossil fuel together with a corresponding reduction in the emissions of CO2 and other greenhouses is achievable.In this work, a software package for the autonomous navigation of field robotics over 2D and 3D field terrains and the optimization of field operations and machinery systems have been developed. A web-based version of the developed software package is currently under progress

Field trajectories proposals as a tool for increasing work efficiency and sustainable land management

ArticleTogether with the requirement for higher productivity the average performance and the weight of agricultural machines are increasing. Agricultural land is increasingly exposed to pressures caused by agricultural machinery. The heavy agricultural machinery passes across a field are frequently associated with technogenic soil compaction. Soil compaction is one of the main problems of modern agriculture. From the previous measuring of the traffic intensity it was found 86.13 % of the total field area was run - o ver with a machine at least once a year, when using conventional tillage and 63.75 % of the total field area was run - over when using direct seeding technology, with dependence on the working width of the machines. Field passes are inevitable in present agri culture. As a result of the increase of total machines weight, it is necessary to optimize the traffic lines trajectories and limit the entries of the machines in the field. At present, the choice of traffic lines direction is based primarily on the experi ence of drivers or the practice of farmers. There are a number of influences that affect the machine work efficiency. Monitoring of the tractor, on an irregular 8 fields showed the following results. Eight - meter working width tiller or seeder brought short ening of total length of turns at headlands with the change in trajectory azimuth. For purposes of measuring the monitored tractors were equipped with monitoring units ITineris. An overview of the chosen directions of the trajectories and the lengths of wo rking and non - working passes was obtained. Based on the shape of the plot, the trajectory of the lines was also modelled. Suitable traffic lines directions in terms of the ratio of work and non - work passes were searched. Based on records of real trajectori es, the ratio of working and non - working path ranged between 6.3 and 15.2%. It was obvious from the results that the shortening of non - working passes and turns in comparison with the originally chosen trajectory directions was achieved by optimization. Thi s was especially valid for complex shapes of fields. Trajectory optimization leads to a reduction of total le n g th of path in all cases. The reduction in total length of path ranged from 69.7 m to 1 , 004.8 m. Changing the length of the working path ranged fr om 10.9 m to 264.9 m with the change in azimuth. The extension was observed in three cases. The highest part on the change of the overall length of the path presented nonworking rides

Time-Optimal Path Planning for Automated Grain Carts

In this paper, we address a motion planning problem for an autonomous agricultural vehicle modeled as tractor-trailer system. We first present a numerical approach and a primitive-based approach for computing the time-optimal path based on given static initial and goal configurations. In the former approach, we define a value function for the entire state space. The value function is consistent with the time to reach goal configuration, and it is finally used to compute the optimal trajectory. In the latter approach, based on the regular and singular primitives, we present an algorithm to construct such primitives and derive the final path. Subsequently, we extend the results and present a dynamic motion planning strategy to accommodate the case of mobile target configuration. Finally, simulation results are provided to validate the feasibility and effectiveness of these techniques

Optimal route planning of agricultural field operations using ant colony optimization

Farming operations efficiency is a crucial factor that determines the overall operational cost in agricultural production systems.  Improved efficiency can be achieved by implementing advanced planning methods for the execution of field operations dealing, especially with the routing and area coverage optimisation aspects. Recently, a new type of field area coverage patterns, the B-patterns, has been introduced.  B-patterns are the result of a combinatorial optimisation process that minimizes operational criterions such as, the operational time, non-working travelled distance, fuel consumption etc.  In this paper an algorithmic approach for the generation of B-patterns based on ant colony optimisation is presented.  Ant colony optimization metaheuristic was chosen for the solution of the graph optimisation problem inherent in the generation of B-patterns.  Experimental results on two selected fields were presented for the demonstration of the effectiveness of the proposed approach. Based on the results, it was shown that it is feasible to use ant colony optimization for the generation of optimal routes for field area coverage while tests made on the resulting routes indicated that they can be followed by any farm machine equipped with auto-steering and navigation systems

Design of a wildlife avoidance planning system for autonomous harvesting operations

Harvesting and mowing operations are among the main potential stressors affecting wildlife within agricultural landscapes, leading to large animal losses. A number of studies have been conducted on harvesting practices to address the problem of wildlife mortality, providing a number of management actions or field area coverage strategies. Nevertheless, these are general rules limited to simple-shaped fields, and which are not applicable to more complex operational situations. The objectives of the present study were to design a system capable of deriving a wildlife avoidance driving pattern for any field shape complexity and field boundary conditions (in terms of escape and non-escape areas) and applicable to different animal behaviours. The assumed animal escape reactions are the result of the parameterization of a series of developed behavioural functions. This parameterization will be able to adapt any knowledge that is or might become available as a result of dedicated future experiments on animal behaviour for different species or different animal ages

Boustrophedon coverage path planning for UAV aerial surveys in wind

© 2017 IEEE. In the quickly developing world of precision agriculture UAV remote sensing, there is a need for a greater understanding of winds effect on fixed wing aerial surveying, as this is missing from current literature. This paper presents a method to define and calculate flight times in a Boustrophedon aerial survey coverage path in wind, for a given convex polygon, at a given sweep angle. It is shown that there exists no easy way to define a sweep angle relative to the wind that minimises flight time. This method is validated by comparing the numerical simulated path and times with a number of surveys run in the high fidelity X-Plane simulator