Autonomous Farming Research Articles

Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks

The application of agricultural machinery in precision agriculture has experienced an increase in investment and research due to the use of robotics applications in the machinery design and task executions. Precision autonomous farming is the operation, guidance, and control of autonomous machines to carry out agricultural tasks. It motivates agricultural robotics. It is expected that, in the near future, autonomous vehicles will be at the heart of all precision agriculture applications [1]. The goal of agricultural robotics is more than just the application of robotics technologies to agriculture. Currently, most of the automatic agricultural vehicles used for weed detection, agrochemical dispersal, terrain leveling, irrigation, etc. are manned. An autonomous performance of such vehicles will allow for the continuous supervision of the field, since information regarding the environment can be autonomously acquired, and the vehicle can then perform its task accordingly.

Nonlinear sliding mode control of an unmanned agricultural tractor in the presence of sliding and control saturation

The paper considers the problem of automatic path tracking by autonomous farming vehicles subject to wheel slips, which are characteristic for agricultural applications. Two guidance laws are proposed to solve this problem, and both explicitly take into account the constraints on the steering angle and ensure tracking an arbitrarily curved path. The first law is implemented by the pure sliding-mode controller, whereas the second one combines the sliding mode approach with a smooth nonlinear control law and requests control chattering at the reduced amplitude as compared with the first law. Mathematically rigorous proofs of global convergence and robust stability of the proposed guidance laws are presented. In doing so, the slipping effects are treated as bounded uncertainties. Simulation results and real world experiments confirm the applicability and performance of the proposed guidance approach.

Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies, which has widespread usage in industry, is also regarded as an ideal solution for automated agriculture because it fulfils the accuracy, reliability and availability requirements of industrial and agricultural applications. Agriculture applications use position, velocity and heading information for automated vehicle guidance and control to enhance the yield and quality of the crop, and in order to vary the application of fertilizer and herbicides according to soil heterogeneity at sub-field level. A loosely coupled GPS/INS integration algorithm known as "AhrsKf" is introduced for automated agriculture vehicle guidance and control utilizing MEMS inertial sensors and GPS. The AhrsKf can produce high-frequency attitude solutions for the vehicle's guidance and control system, by using inputs from a single survey grade L1/L2 antenna, eliminating the need for the previous two antenna solutions. Given its agricultural application, the AhrsKf has been implemented with some specific design features to improve the accuracy of the attitude solution including, temperature compensation of the inertial sensors, and the aid of plough lines of farm lands. To evaluate the AhrsKf solution, two benchmarking tests have been conducted by using a three-antenna GPS system and NovAtel's SPAN-CPT. The results have demonstrated that the AhrsKf solution is stable and can correctly track the movement of the farming vehicle.

Editorial: Special issue on agricultural robotics

It can be argued that one of the earliest forms of robotics is agricultural automation, now undergoing a great revival in its activities. Its importance in the productivity of the agricultural crops is well established, but recent advancements in agricultural robotics seek not only to improve farming capacity with minimal human interference, but also to improve the efficiency of the tasks and the quality of the resulting crops through greater precision of the handling of the agricultural processes. This has seen the development in agricultural robotics centred around two main key phrases: autonomous agriculture and precision agriculture. The former is aimed at developing a capability to operate large farming facilities withminimumhumanmanpower, such as by the development of autonomous tractors or coordinated teams of tractors and sensor networks to monitor a large farming area. The latter aims at developing precise data-gathering as well as crop or produce handling, such as selective spraying on plants as opposed to the traditionalmethod of non-selectively spraying the entire field. Obviously, many applications also fall within both key phrases. This special issue of Springer Journal of Intelligent Service Robotics on Agricultural Robotics seeks to present the latest developments in this exciting field of robotics research. The agricultural environment today serves not only as a client to which existing fundamental techniques of robotics are applied, but also as the source of challenges to the invention

A semi-autonomous tractor in an intelligent master–slave vehicle system

This paper presents an approach to develop an intelligent master---slave system between agricultural vehicles, which will enable a semi-autonomous agricultural vehicle (slave) to follow a leading tractor (master) with a given lateral and longitudinal offset. In our study not only the follow-up motions but also the site-specific control of the apparatus such as rear and front power lift was considered. In the first part of this paper, the recent research works in the area autonomous farming were discussed and the restrictions of these research works were illustrated. In the second part an approach, to construct a master---slave system between two agricultural vehicles was demonstrated. In the next part, the mathematic modeling of this master---slave system and the simulation results about the control algorithm were demonstrated. Afterwards, the result of a real field test was presented and the safety considerations about such an intelligent vehicle system were made.

Autonomous farming: modelling and control of agricultural machinery in a unified framework

There are significant challenges faced by the farming industry, including a reduced labour workforce and a corporate style of farming. Such factors demand an increase in farming efficiency and productivity. This paper describes future autonomous farming producing globally competitive farming communities. The autonomous farm is described as a complex system-of-systems, bringing together robotics for autonomous farming, and precision agriculture. We initially describe a system-of-systems architecture, the heart of which is two data sets providing links between the various subsystems. These data sets include a precision farming data set, containing spatially precise navigation data, and a precision agriculture data set, consisting of spatially oriented agronomy data. Secondly, research undertaken in autonomous farm machinery is highlighted, where we present a foundation for the autonomous and robust trajectory-tracking control of articulated farm vehicles in the presence of uncertain conditions.

Estimating Hay Bale Position with Stereo Vision Technique using an Omnidirectional Camera

In path planning of autonomous agriculture vehicles, detecting and identifying obstacles, and taking appropriate collision avoidance measures are critical for safe operation. The goal of this research was to obtain the hay bale distance and position to be used in real-time obstacle avoidance detection for autonomous robot tractors using stereo vision system. The vision system was an omnidirectional camera that have a wide field view and useful for specific applications such as meadow, open field, etc. The estimated hay bales distance from the camera showed that the calibration parameters should be further improved to enable autonomous navigation of a robot tractor in the meadow.

DIFFERENTIAL FLATNESS-BASED FORMATION FOLLOWING OF A SIMULATED AUTONOMOUS SMALL GRAIN HARVESTING SYSTEM

Researchers around the world have focused on autonomous agriculture with systems encompassing greenhouse,orchard, field, and other applications. Research has shown the potential and ability of the technology to allow a vehicle orselection of vehicles to follow a specified task. In this study, one aspect of the problem, that of operating a tractor-cartcombination in conjunction with a small-grain combine harvester, was investigated. The tractor-cart combination andcombine harvester application was selected because of the high fatigue and long duration aspects of the problem. Differentialflatness-based formation following was tested in software and robotic simulation. The software simulation was based on anactual field track from a combine yield monitor and demonstrated the potential of the system. The robotic simulation usedtwo iRobot Magellan Pro robots in an indoor environment and demonstrated that the methodology could be implemented inreal time.

Establishing Automated Vehicle Navigation as a Reality for Production Agriculture

The navigation of agricultural machines by automated methods has been the dream of numerous researchers for over 80 years. However, automatic and autonomous navigation has been an elusive goal due to the sophisticated structure required to achieve a socially acceptable result. Many of the technological barriers of automation have been recently eliminated by the advancements in sensors, computers, and vehicle systems. Still a number of advancements are required in the enhancement of safety, simplicity of operation, and affordable cost. So what does it take to make autonomous agriculture a reality?Researchers have been motivated by noble concepts supporting automation including a declining and aging work force and a need to achieve production goals with fewer workers. But most of us have avoided a truly close connection to the farmer and his needs. In reaction to the concept of automation, some agriculturists have argued that tractor driving is the most enjoyable task a farmer has to do in his job and that researchers are wasting time if they believe he will give it up. Industry is focused on many of the farmer’s near-term problems and will only offer automation technology if their customer demands it. These conditions make the implementation of navigation technologies very difficult.A natural progression is needed to advance agriculture into the automation and information age using navigation technologies to enhance productivity, the quality of life, or other factors of societal benefit. Researchers must work closely with the farmer and industry to provide significant examples of how this technology can be utilized today and, additionally, build a natural progression to an age of automation that is accepted by the producer.Drawing on examples from recent experiences in vehicle navigation, this lecture will review the status of automation technology and propose a course of action to implement navigation into production agriculture.

Research in autonomous agriculture vehicles in Japan

Much research on automation in agriculture has been presented in recent years at the annual meetings of the Japanese Society of Agricultural Machinery (JSAM). This research has been performed in universities and government institutes, and by agricultural machinery manufacturers. Because of funding limitations, research in universities has concentrated on methodologies, such as navigation, sensing, and application of control theory. Development of a one dimensional image sensor, and application of neural networks and genetic algorithms, has taken place at Hokkaido University; vision guidance and fuzzy logic application at the University of Tokyo; an automatic follow-up vehicle has been developed at Kyoto University; and an automatic transport vehicle at Ehime University. At research institutes and manufacturers, with their greater financial freedom, more practical systems have been developed. A tilling robot and a driver-less air blast sprayer is being developed in the Bio-oriented Technology Research Advancement Institute (BRAIN); and an autonomous rice planter, a tillage robot and autonomous forage tractor in the research institute of the Ministry of Agriculture, Forestry, and Fishery (MAFF). Kubota Co. Ltd has developed autonomous rice planting and husbandry vehicles. In Asian countries an autonomous speed sprayer is under study in Korea and an autonomous power sprayer in Taiwan, but little research is performed elsewhere in Asia.