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

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

A Design Framework for Off-road Equipment Automation

Design frameworks can be helpful in the development of complex systems needed to automate machines. Designing autonomous off-road machinery requires having the means for managing the complexity of multiple interacting systems. A design framework, consisting of four technical layers, is presented. These layers are (1) machine architecture, (2) machine awareness, (3) machine control, and (4) machine behavior. Examples of technology advanced in development efforts of autonomous, robotic platforms for agricultural applications are provided. Linkages were made to applications in the construction machinery sector. Similarities between agricultural and construction automation exist in each of the technical layers

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

Machining-based coverage path planning for automated structural inspection

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets