Laser Measurement Systems (LMS) are used in autonomous agricultural vehicles for guidance and obstacle detection purposes. In the particular case of agricultural vehicles, the efficiency of LMS measurements is affected by mechanical vibrations induced by the operation of these machines on uneven terrains. The objective of this study was to evaluate the effectiveness of a mounting system capable of reducing the transmission of mechanical vibrations to an LMS sensor installed on an agricultural tractor for the purpose of improving the detection accuracy of obstacles during field operation. A stabilization system (SS) was designed for a SICK LMS 291-S14 scanner. The LMS sensor and the SS were installed on an agricultural tractor to scan four objects positioned at known locations in the field. An experimental analysis was carried out to evaluate the detection accuracy of the LMS sensor under different operating conditions: with and without SS; with and without support bars (S); and for different types of terrains (in terms of roughness) and field operating speed. The detection accuracy of the LMS sensor was assessed by determining the difference between the known location of the four objects and their corresponding estimated location from the LMS measurements. The increase of tractor speed had a negative effect on the accuracy of the LMS sensor with an increase in the positioning error of up to 75%. However, it was not possible to establish a clear relationship between terrain roughness and the accuracy of the LMS sensor. The addition of the S systems positively affected the accuracy of the LMS sensor and resulted in a 41% decrease of the average positioning error from 0.340 to 0.201 m. Finally, the used of the SS mounting system decreased the average positioning error by 57% from 0.382 to 0.161 m. These experimental results confirmed the effectiveness of the joint use of the SS and S mounting systems in improving the accuracy of the LMS sensor mounted on an agricultural tractor through a reduction in transmitted vibrations.

Laser Measurement Systems (LMS) are used in autonomous agricultural vehicles for guidance and obstacle detection purposes. In the particular case of agricultural vehicles, the efficiency of LMS measurements is affected by mechanical vibrations induced by the operation of these machines on uneven terrains. The objective of this study was to evaluate the effectiveness of a mounting system capable of reducing the transmission of mechanical vibrations to an LMS sensor installed on an agricultural tractor for the purpose of improving the detection accuracy of obstacles during field operation. A stabilization system (SS) was designed for a SICK LMS 291-S14 scanner. The LMS sensor and the SS were installed on an agricultural tractor to scan four objects positioned at known locations in the field. An experimental analysis was carried out to evaluate the detection accuracy of the LMS sensor under different operating conditions: with and without SS; with and without support bars (S); and for different types of terrains (in terms of roughness) and field operating speed. The detection accuracy of the LMS sensor was assessed by determining the difference between the known location of the four objects and their corresponding estimated location from the LMS measurements. The increase of tractor speed had a negative effect on the accuracy of the LMS sensor with an increase in the positioning error of up to 75%. However, it was not possible to establish a clear relationship between terrain roughness and the accuracy of the LMS sensor. The addition of the S systems positively affected the accuracy of the LMS sensor and resulted in a 41% decrease of the average positioning error from 0.340 to 0.201 m. Finally, the used of the SS mounting system decreased the average positioning error by 57% from 0.382 to 0.161 m. These experimental results confirmed the effectiveness of the joint use of the SS and S mounting systems in improving the accuracy of the LMS sensor mounted on an agricultural tractor through a reduction in transmitted vibrations.

Laser Measurement Systems (LMS) are used in autonomous agricultural vehicles for guidance and obstacle detection purposes. In the particular case of agricultural vehicles, the efficiency of LMS measurements is affected by mechanical vibrations induced by the operation of these machines on uneven terrains. The objective of this study was to evaluate the effectiveness of a mounting system capable of reducing the transmission of mechanical vibrations to an LMS sensor installed on an agricultural tractor for the purpose of improving the detection accuracy of obstacles during field operation. A stabilization system (SS) was designed for a SICK LMS 291-S14 scanner. The LMS sensor and the SS were installed on an agricultural tractor to scan four objects positioned at known locations in the field. An experimental analysis was carried out to evaluate the detection accuracy of the LMS sensor under different operating conditions: with and without SS; with and without support bars (S); and for different types of terrains (in terms of roughness) and field operating speed. The detection accuracy of the LMS sensor was assessed by determining the difference between the known location of the four objects and their corresponding estimated location from the LMS measurements. The increase of tractor speed had a negative effect on the accuracy of the LMS sensor with an increase in the positioning error of up to 75%. However, it was not possible to establish a clear relationship between terrain roughness and the accuracy of the LMS sensor. The addition of the S systems positively affected the accuracy of the LMS sensor and resulted in a 41% decrease of the average positioning error from 0.340 to 0.201 m. Finally, the used of the SS mounting system decreased the average positioning error by 57% from 0.382 to 0.161 m. These experimental results confirmed the effectiveness of the joint use of the SS and S mounting systems in improving the accuracy of the LMS sensor mounted on an agricultural tractor through a reduction in transmitted vibrations.

<div>In the years to come, a growing global population will require more crop production than ever before. As technological advances improve across all industries, autonomous agricultural vehicles (AAVs) can be part of the solution to the rising demand for food. By improving and transforming conventional farming methods, AAVs have the potential to transform the way farming operations are completed. AAVs are a class of robotic machines that have the ability to complete agricultural tasks without requiring direct and constant control of a human operator. By removing the need for an operator, these agricultural robotic machines allow for new vehicle designs and new opportunities for different vehicle configurations and sizes. </div><div><br></div><div>A simulation model was developed that calculates the energy requirements of AAVs operating on row crops. This deterministic model was used to quantify the energy needs and energy expenditures of agricultural vehicles, and to investigate the effects of using AAVs in lieu of conventional human-operated agricultural machinery.</div><div><br></div><div>The energy model was demonstrated using a pre-defined scenario of a typical row-crop farming operation in the Midwest U.S. The purpose of the case study was to compare a conventional crop production operation with operations that have implemented autonomous machines. Four general vehicle configurations were chosen based on the traction machine size: large tractors (e.g.,~greater than 60 kW), small tractors (e.g.,~less than 60 kW), utility vehicles (e.g.,~John Deere Gator), and single row machines. The complete crop production operation was based on a farm size of 607 ha (1,500 acre) with half the land devoted to corn production. The four main operations were fertilizer application, pesticide spraying, no-till planting, and harvesting.</div><div><br></div><div>First, the energy model was used to compare a whole farm operation with three different machine configurations: using all conventional large machines, using all autonomous large machines, and using all autonomous smaller machines (55 kW tractor). The results show that from an energy standpoint, the most significant savings comes from the decreased amount of agrochemical application associated with AAVs. The total energy consumption of the large tractor AAV configuration is 36% less than the conventional operation (11,081 MJ/ha vs. 7,090 MJ/ha).</div><div><br></div><div>In order to have a better perspective on the effects of using AAVs, further analysis was conducted on an individual operation basis: fertilizing, pesticide spraying, no-till planting, and harvesting. Because AAVs can work 24-hours per day, the fertilizing operation for the single large tractor AAV could be completed in 1.6 working days, as opposed 2.4 working days for the conventional machine. It only required two small tractor AAVs to meet or exceed the performance of the conventional machine, yet for the same amount of money, four to five small tractor AAVs could be purchased.</div><div><br></div><div>The greatest benefit to utilizing AAVs is the intelligent application of pesticide, which can allow for 65--95% reduction in chemical use. The spraying operation highlighted the advantages of large machines (conventional and autonomous), namely speed of operation and width. It takes two small tractor AAVs, seven utility AAVs, or 12 single-row AAVs to match their performance. </div><div><br></div><div>For the no-till planting operation, two small tractor AAVs, seven utility AAVs, or 39 single-row AAVs are required to match the performance of conventional machinery. However, for the same cost as the conventional machine, six small tractor AAVs, 16 utility AAVs, or 55 single-row AAVs could be purchased. The benefit of using higher numbers of AAVs is seen in the amount of time required to complete the planting task, where the swarms of AAVs could finish planting in nearly 1/3 of the time.</div><div><br></div><div>Harvesting was previously analyzed during the whole farm scenario. In general, the energy consumption and costs are relatively the same between the conventional machine and the large AAV. The advantage of the autonomous harvesting is that is can operate continuously throughout the night. Continuous operation is possible for this scenario because corn can be harvested at night. However, soybeans cannot because the onset of dew at dusk does not allow for proper processing of the crop. </div><div><br></div><div>Along with the energy model, crop production efficiency metrics were studied that provided an objective method of analyzing the advantages and disadvantages associated with replacing and/or augmenting conventional farming vehicles with AAVs. Energy-per-unit-area shows the amount of energy that is consumed over the entire field, regardless of the task time required. Because labor energy consumption is insignificant compared to the other three inputs, energy-per-unit-area is also independent of the number of machines simultaneously in use. Working days and machinery capital cost are other metrics that proved beneficial when comparing AAVs to conventional machines.</div><div><br></div><div>Finally, a modeling tool was developed and demonstrated that allows a user to interact with the energy model in an intuitive way. Creating the modeling tool in Microsoft Excel allows for easy distribution to a wide audience, as opposed to using a more expensive software package. The energy model workbook is composed of five spreadsheets that contain instructions, inputs, outputs, and supporting data tables. A GUI was created using Microsoft Excel VBA that lets the user interact with an event-driven program. Data sets can easily be created and modified for the purpose of evaluating different farming operations. Additionally, options within the GUI allow for parameter studies where multiple data sets can be instantly created in order to analyze the effects of changing a single variable. </div>

O objetivo deste trabalho é desenvolver um sistema de navegação global para que veículos agrícolas autônomos possam executar missões em campos de cultivo através de um sistema de planejamento de trajetórias. Missões podem ser entendidas como sendo tarefas (p.ex.: de monitoramento, coleta de amostras, etc.) através de pequenas rotas que os veículos devem seguir ao longo de seus trabalhos diários, percorrendo a menor distância possível entre os pontos de origem e destino. O planejamento de trajetória foi dividido em etapas para facilitar o entendimento de cada uma delas. O mapeamento apresentado neste trabalho foi feito em regiões de cultivo de café nos estados de São Paulo e Minas Gerais. Os pontos do mapa foram amostrados utilizando um módulo receptor de sinal GPS (Global Positioning System) ao longo dos caminhos onde é possível a passagem do veículo dentro da plantação. Uma etapa importante para o sucesso deste sistema é a etapa de pré-processamento dos dados. Nesta etapa são inseridas as relações entre os pontos do mapeamento da área. As missões foram pré-definidas de modo a testar o cálculo do caminho de custo mínimo que é realizado através do algoritmo de Dijkstra. A cada ponto da rota é fornecido o ângulo de direção com o qual o veículo deve estar em relação ao Norte geográfico. De acordo com a mudança pretendida do ângulo de direção é proposta uma suavização nesta mudança através da alteração do percurso para um arco de circunferência. Neste caso, o raio de giro é informado. A última etapa consiste em fornecer a velocidade máxima de deslocamento do veículo em função da mudança de direção e velocidade angular máxima do centro de massa do veículo. O sistema proposto neste trabalho foi capaz de determinar o caminho com a menor distância entre dois pontos do mapeamento (coordenadas geográficas) e o calcular da distância entre os pontos. Embora a fórmula utilizada para calcular a distância entre duas coordenadas geográficas considerar o formato da Terra como sendo uma esfera, isto não gerou erro significativo para a aplicação proposta. A suavização proposta possibilitou, em alguns pontos, o aumento da velocidade de deslocamento por fazer a mudança do ângulo de direção de forma menos abrupta. / The objective of this work is to develop a global navigation system for autonomous agricultural vehicles can perform missions in crop fields through a system of path planning. Missions can be understood as tasks (eg monitoring, sampling, etc.). Through small routes that vehicles must follow throughout their daily jobs, traveling the shortest possible distance between the points of origin and destination. The path planning was divided into steps to make it easy to understand each one. The mapping presented in this work was done in coffee-growing regions in the state of São Paulo and Minas Gerais. The map points have been sampled using a GPS receiver module along the path where it is possible to move the vehicle within the plantation. An important step for the success of this system is the data pre-processing step. In this step are inserted the relations between the points of the mapping. The missions are predefined in order to test if the calculation of the minimum cost path made by Dijkstra algorithm is correct. At each point of the route is given the vehicle heading angle (vehicle position towards the geographic North). According to the intended change of the heading angle is proposed a smoothing method to smooth this change by changing the route to an arc. In this case, the turning radius is reported. The last step is to provide the maximum speed of the vehicle due to the change of direction and maximum angular speed of the center of mass. The system proposed in this paper was able to determine the path with the shortest distance between two points of the mapping (geographic coordinates) and calculate the distance between these points. Although the formula used to calculate the distance between two geographical coordinates consider the shape of the Earth as a sphere, this did not generate significant errors for the proposed application. The proposed smoothing allowed, in some cases, to increase the vehicle speed by making the change of heading angle less abrupt.

O objetivo deste trabalho é desenvolver um sistema de navegação global para que veículos agrícolas autônomos possam executar missões em campos de cultivo através de um sistema de planejamento de trajetórias. Missões podem ser entendidas como sendo tarefas (p.ex.: de monitoramento, coleta de amostras, etc.) através de pequenas rotas que os veículos devem seguir ao longo de seus trabalhos diários, percorrendo a menor distância possível entre os pontos de origem e destino. O planejamento de trajetória foi dividido em etapas para facilitar o entendimento de cada uma delas. O mapeamento apresentado neste trabalho foi feito em regiões de cultivo de café nos estados de São Paulo e Minas Gerais. Os pontos do mapa foram amostrados utilizando um módulo receptor de sinal GPS (Global Positioning System) ao longo dos caminhos onde é possível a passagem do veículo dentro da plantação. Uma etapa importante para o sucesso deste sistema é a etapa de pré-processamento dos dados. Nesta etapa são inseridas as relações entre os pontos do mapeamento da área. As missões foram pré-definidas de modo a testar o cálculo do caminho de custo mínimo que é realizado através do algoritmo de Dijkstra. A cada ponto da rota é fornecido o ângulo de direção com o qual o veículo deve estar em relação ao Norte geográfico. De acordo com a mudança pretendida do ângulo de direção é proposta uma suavização nesta mudança através da alteração do percurso para um arco de circunferência. Neste caso, o raio de giro é informado. A última etapa consiste em fornecer a velocidade máxima de deslocamento do veículo em função da mudança de direção e velocidade angular máxima do centro de massa do veículo. O sistema proposto neste trabalho foi capaz de determinar o caminho com a menor distância entre dois pontos do mapeamento (coordenadas geográficas) e o calcular da distância entre os pontos. Embora a fórmula utilizada para calcular a distância entre duas coordenadas geográficas considerar o formato da Terra como sendo uma esfera, isto não gerou erro significativo para a aplicação proposta. A suavização proposta possibilitou, em alguns pontos, o aumento da velocidade de deslocamento por fazer a mudança do ângulo de direção de forma menos abrupta. / The objective of this work is to develop a global navigation system for autonomous agricultural vehicles can perform missions in crop fields through a system of path planning. Missions can be understood as tasks (eg monitoring, sampling, etc.). Through small routes that vehicles must follow throughout their daily jobs, traveling the shortest possible distance between the points of origin and destination. The path planning was divided into steps to make it easy to understand each one. The mapping presented in this work was done in coffee-growing regions in the state of São Paulo and Minas Gerais. The map points have been sampled using a GPS receiver module along the path where it is possible to move the vehicle within the plantation. An important step for the success of this system is the data pre-processing step. In this step are inserted the relations between the points of the mapping. The missions are predefined in order to test if the calculation of the minimum cost path made by Dijkstra algorithm is correct. At each point of the route is given the vehicle heading angle (vehicle position towards the geographic North). According to the intended change of the heading angle is proposed a smoothing method to smooth this change by changing the route to an arc. In this case, the turning radius is reported. The last step is to provide the maximum speed of the vehicle due to the change of direction and maximum angular speed of the center of mass. The system proposed in this paper was able to determine the path with the shortest distance between two points of the mapping (geographic coordinates) and calculate the distance between these points. Although the formula used to calculate the distance between two geographical coordinates consider the shape of the Earth as a sphere, this did not generate significant errors for the proposed application. The proposed smoothing allowed, in some cases, to increase the vehicle speed by making the change of heading angle less abrupt.

The effects of in-vehicle automation and driving assistant systems on the mental workload and situation awareness of drivers have been the interest of many studies; some of the implications of automation in such man-machine systems have been identified. Due to the introduction of advanced automated systems in agricultural machinery, farmers are currently working with semi-autonomous vehicles. A human factors perspective on the design of these systems will ensure safe and efficient operation of such man-machine systems. In this study, a systematic approach was utilized to address human factors issues associated with operating a semi-autonomous agricultural vehicle, and to provide design recommendations. The study was carried out in three stages. First, a task analysis was used to identify tasks associated with operating an agricultural vehicle and to select appropriate experimental variables. Next, a preliminary experiment was performed to validate the test procedure and measurement techniques. Finally, the main experiment was administered. Experiments were conducted using the Tractor Driving Simulator located in the Agricultural Ergonomics Laboratory at the University of Manitoba. Thirty young experienced tractor drivers participated in this study. The experiment investigated the effects of i) vehicle steering task automation (VSTA) and ii) implement control and monitoring task automation (ICMTA) on mental workload and situation awareness of drivers. It was found that ICMTA significantly affected situation awareness (and its underlying components) of the operator. The situation awareness of drivers increased as the automation support level increased, but the highest level of automation, where the participants were out of the task loop, resulted in low situation awareness, similar to the condition with no automation support. VSTA only reduced the attentional demand of the situation, one of the three components of the situation awareness, which had negative effect on overall situation awareness. Based on the results from a subjective mental workload measure, moderate levels of mental workload were reported when the participants were involved in the implement control and monitoring task loop. The highest level of ICMTA reduced the average mental workload by 18%. Reaction time of drivers and number of errors committed by drivers both decreased as the automation level increased. / October 2015