Wi-Fi and Satellite-Based Location Techniques for Intelligent Agricultural Machinery Controlled by a Human Operator

In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to maintaining safety, synchronization, and efficiency during the execution of a mission. An advanced localization system for both entities involved in the joint work, i.e., the autonomous tractors and the human operator, provides a basis for meeting the task requirements. In this paper, different localization techniques for a human operator and an autonomous tractor in a field environment were tested. First, we compared the localization performances of two global navigation satellite systems’ (GNSS) receivers carried by the human operator: (1) an internal GNSS receiver built into a handheld device; and (2) an external DGNSS receiver with centimeter-level accuracy. To investigate autonomous tractor localization, a real-time kinematic (RTK)-based localization system installed on autonomous tractor developed for agricultural applications was evaluated. Finally, a hybrid localization approach, which combines distance estimates obtained using a wireless scheme with the position of an autonomous tractor obtained using an RTK-GNSS system, is proposed. The hybrid solution is intended for user localization in unstructured environments in which the GNSS signal is obstructed. The hybrid localization approach has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment; and (2) the acquisition of the tractor RTK coordinates when the human operator is near the tractor. In five RSSI tests, the best result achieved was an average localization error of 4 m. In tests of real-time position correction between rows, RMS error of 2.4 cm demonstrated that the passes were straight, as was desired for the autonomous tractor. From these preliminary results, future work will address the use of autonomous tractor localization in the hybrid localization approach

A Kalman Filter Implementation for Precision Improvement in Low-Cost GPS Positioning of Tractors

Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain

Reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean työkoneen yhteistyönä

Coverage path planning is the task of finding a collision free path that passes over every point of an area or volume of interest. In agriculture, the coverage task is encountered especially in the process of crop cultivation. Several tasks are performed on the field, one after the other, during the cultivation cycle. Cooperation means that multiple agents, in this case vehicles, are working together towards a common goal. Several studies consider the problem where a single task is divided and assigned among the agents. In this thesis, however, the vehicles have different tasks that are sequentially dependent, that is, the first task must be completed before the other. The tasks are performed simultaneously on the same area. The literature review suggests that there is a lack of previous research on this topic. The objective of this thesis was to develop an algorithm to solve the cooperative coverage path planning problem for sequentially dependent tasks. A tool chain that involves Matlab, Simulink and Visual Studio was adapted for the development and testing of the solution. A development and testing architecture was designed including a compatible interface to a simulation and a real-life test environment. Two different algorithms were implemented based on the idea of computing short simultaneous paths at a time and scheduling them in real-time. The results were successfully demonstrated in a real-life test environment with two tractors equipped with a disc cultivator and a seeder. The objective was to sow the test area. The test drives show that with the algorithms that were developed in this thesis it is possible to perform two sequentially dependent agricultural coverage tasks simultaneously on the same area.Kattavassa reitinsuunnittelussa yritetään löytää polku, jonka aikana määritelty ala tai tilavuus tulee käytyä läpi niin että alueen jokainen piste on käsitelty. Maataloudessa tämä tehtävä on merkityksellinen erityisesti peltoviljelyssä. Useita peltotöitä suoritetaan yksi toisensa jälkeen samalla alueella viljelyvuoden aikana. Useissa tutkimuksissa käsitellään yhteistyönä tehtävää reitinsuunnittelua, jossa yksi tehtävä on jaettu osiin ja osat jaetaan useiden tekijöiden kuten robottien kesken. Tässä diplomityössä peltotyökoneilla on kuitenkin omat erilliset tehtävänsä, joilla on määrätty järjestys, eli niiden suorittaminen riippuu työjärjestyksestä. Työkoneet työskentelevät samanaikaisesti samalla alueella. Diplomityössä tehty kirjallisuuskatsaus viittaa siihen, että vastaavaa aihetta ei ole aiemmin tutkittu. Tämän diplomityön tavoitteena on kehittää algoritmi, jolla voidaan toteuttaa reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean peltotyökoneen yhteistyönä. Algoritmikehitystä ja testausta varten suunniteltiin yhtenäinen rajapinta, jolla algoritmia voitaisiin testata sekä simulaatiossa että todellisessa testitilanteessa. Algoritmikehityksessä käytettiin työkaluina Matlab, Simulink ja Visual Studio -ohjelmia. Työssä toteutettiin kaksi algoritmia, jotka perustuvat samaan ideaan: suunnitellaan kerrallaan kaksi lyhyttä samanaikaista polkua, jotka ajoitetaan reaaliajassa. Algoritmeja testattiin todellisessa testiympäristössä kahden työkoneen yhteistyönä, kun tavoitteena on kylvää koko testialue. Ensimmäinen työvaihe suoritettiin lautasmuokkaimella ja toinen kylvökoneella. Testiajot osoittavat, että diplomityössä kehitetyillä algoritmeilla voidaan ohjata kahden toisistaan riippuvaisen peltotyön toteutus samanaikaisesti samalla peltoalueella

A Novel Approach to Autonomous Farming Robot

Now-a-days everyone has lost interest from farming as it has become a very difficult and tedious job. Although hi-tech vehicles and equipment have overcome older version of vehicles and equipment and also they made farming quite easy. But it still requires a plenty of human effort. Today automation has been introduced in almost every form of industry and a prominent reason to reducing human effort. Our Objective is to reduce human efforts in farming as we planned to develop an autonomous guidance system for farm vehicles. Our system will be based on Global Positioning System (GPS) [1]. To develop complete autonomous system, other than GPS, systems like machine vision, laser-based sensors, inertial sensors would be needed to be employed for avoiding obstacles in the path and overcoming other challenges. However making such systems would require more time and monetary resources then available, hence developing such complete autonomous system is out of scope of the current task at hand. Our aim is to develop a Mixture of such complete autonomous system which will fulfill one of the Basic needs of a complete autonomous guidance system. DOI: 10.17762/ijritcc2321-8169.160412