GPS-guided mobile robot platform featuring modular design elements for agricultural applications : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Engineering in Mechatronics at Massey University Turitea Campus, Palmerston North, New Zealand

The agricultural industry has not seen significant innovation in development of low-cost automated farming solutions, with current systems costing several thousands of dollars to implement. Currently these automated solutions are primarily implemented around crop planting and harvesting, and the large implementation cost of these systems makes them unfeasible for small-scale operations. Within many agricultural industries, workers expend a considerable amount of time undertaking simple tasks that are labour intensive. Many of these tasks could instead be completed using a self-driving robotic platform outfitted with the appropriate devices required for the tasks. This thesis covers the research work aiming to produce a solution that could turn an existing farming vehicle into a multipurpose low-cost agricultural platform, to act as the platform for an autonomous vehicle capable of performing pre-programmed tasks within an agricultural environment. A quad bike was selected as the vehicle platform for this research in which the control modules would control the speed and direction of this farm bike. Four modules were developed to control the vehicle components that would normally be operated by a human operator. These modules are comprised of mechanical actuators coupled with a microcontroller control system and includes some specific designs to maintain the user's ability to manually control the pre-existing systems. A gear-changing module controls the vehicles manual gearbox, providing a method to detect and control the vehicles current gear. A speed control module was developed to control the vehicles throttle and braking system and detects the vehicles speed. A steering module controls the vehicles steering system, allowing for accurate control of the vehicles direction. Finally, a vehicle controller module provides a central command interface that ties the previous three modules together and controls the vehicles electrical components and engine. Development of a low-cost differential GPS (DGPS) system was also undertaken to reduce the implementation cost of the system. Due to inconclusive results in relation to the positional accuracy of this system is was decided that a standard GPS system would be used for the vehicle prototype with further development on the DGPS system would be undertaken in future development of the research. The successful development of a farm automated vehicle platform was achieved through this research. With further improvement on software, intelligent control and the development of a low-cost differential global positioning satellite (GPS) system, a fully autonomous farm platform that can be outfitted with different tools or devices for the required farm tasks is feasible and practical

Cost-effective visual odometry system for vehicle motion control in agricultural environments

In precision agriculture, innovative cost-effective technologies and new improved solutions, aimed at making operations and processes more reliable, robust and economically viable, are still needed. In this context, robotics and automation play a crucial role, with particular reference to unmanned vehicles for crop monitoring and site-specific operations. However, unstructured and irregular working environments, such as agricultural scenarios, require specific solutions regarding positioning and motion control of autonomous vehicles. In this paper, a reliable and cost-effective monocular visual odometry system, properly calibrated for the localisation and navigation of tracked vehicles on agricultural terrains, is presented. The main contribution of this work is the design and implementation of an enhanced image processing algorithm, based on the cross-correlation approach. It was specifically developed to use a simplified hardware and a low complexity mechanical system, without compromising performance. By providing sub-pixel results, the presented algorithm allows to exploit low-resolution images, thus obtaining high accuracy in motion estimation with short computing time. The results, in terms of odometry accuracy and processing time, achieved during the in-field experimentation campaign on several terrains proved the effectiveness of the proposed method and its fitness for automatic control solutions in precision agriculture applications

An Effective Multi-Cue Positioning System for Agricultural Robotics

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters, 201

Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Hortibot: Feasibility study of a plant nursing robot performing weeding operations – part IV

Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture. A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth. The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings

Purchase, circulation and fuel taxation

During the last decade, the UK and many other developed nations have reformed existing forms of road transport taxation to address a number of transport policy goals. This has involved modifying the design of purchase, circulation and fuel taxation to promote: • More fuel efficient vehicles • Alternative fuel vehicles • Cleaner fuels (lower emissions and/or low carbon) • Modal shift and traffic volume • Congestion reduction This chapter particularly explores the use of environmental taxation to promote Transport Demand Management (TDM) and identifies key principles of the design of such environmental taxes. It notes the importance of positioning a tax measure in relation to user decisions, its targeting and the threshold levels needed to provide a useful policy impact. Taxation measures considered include: • Initial vehicle purchase • ‘Circulation’ Tax on the ownership of vehicles • Tax on the use of vehicles It is concluded that purchase, circulation and fuel taxation can promote a variety of transport and environmental policy goals. It is important to distinguish between taxation measures to influence vehicle characteristics (technology, the type of fuel used and fuel economy) as opposed to the level vehicle use (TDM). Well designed purchase and circulation taxes can stimulate cleaner car technologies and fuels, but they are not an appropriate TDM measure. Road fuel duties are an effective general TDM measure but cannot be targeted on particular areas, times or for particular urban transport policy purposes. Road user charges can be targeted on such factors, and consequently, led by the established example of Singapore, followed by Norway and London, they are attracting much attention. The road transport taxation landscape is possibly set to change with many countries now seeing road user charges as potentially replacing the entire regime of transport taxation on purchase, ownership and fuel. But, rather than replacing fuel duties, evidence is mounting that to manage transport demand, road user charges need to be in addition to and not replace fuel and vehicle taxation. This may be a politically inconvenient truth, and the real challenge will be managing the transition towards an effective new transport taxation regime

Development of a tabletop guidance system for educational robots

The guidance of a vehicle in an outdoor setting is typically implemented using a Real Time Kinematic Global Positioning System (RTK-GPS) potentially enhanced by auxiliary sensors such as electronic compasses, rotation encoders, gyroscopes, and vision systems. Since GPS does not function in an indoor setting where educational competitions are often held, an alternative guidance system was developed. This article describes a guidance method that contains a laser-based localization system, which uses a robot-borne single laser transmitter spinning in a horizontal plane at an angular velocity up to 81 radians per second. Sensor arrays positioned in the corners of a flat rectangular table with dimensions of 1.22 m × 1.83 m detected the laser beam passages. The relative time differences among the detections of the laser passages gave an indication of the angles of the sensors with respect to the laser beam transmitter on the robot. These angles were translated into Cartesian coordinates. The guidance of the robot was implemented using a uni-directional wireless serial connection and position feedback from the localization system. Three experiments were conducted to test the system: 1) the accuracy of the static localization system was determined while the robot stood still. In this test the average error among valid measurements was smaller than 0.3 %. However, a maximum of 3.7 % of the measurements were invalid due to several causes. 2) The accuracy of the guidance system was assessed while the robot followed a straight line. The average deviation from this straight line was 3.6 mm while the robot followed a path with a length of approximately 0.9 m. 3) The overall performance of the guidance system was studied while the robot followed a complex path consisting of 33 sub-paths. The conclusion was that the system worked reasonably accurate, unless the robot came in close proximity