Path Planning Algorithms for Agricultural Machines

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Path Planning Algorithms for Agricultural Machines. Manuscript ATOE 07 009. Vol. IX. July, 2007

Environment simulator for studying automatic crop farming

Agricultural machines capable of utilizing variable rate application technology are tackling spatial variability in agricultural fields.  Agricultural field robots are the next step in technology, robots which are capable of utilizing sensor and actuating technologies without human contact and operate only areas of interest.  However, agricultural field robots are still under research.  Robots are just one part of the next generation of crop farming having more advanced tools to do the work which currently requires humans.  The next generation of crop farming, in the vision of the authors, is based on automation, which incorporates stationary and moving sensors systems, robots, model based decision making, automated operation planning which adapts to spatial variability according to the measurements as well as to weather conditions.  This article presents a top-down approach of automated crop farming using simulation, trying to cover all the component parts on a fully automated farm.  In the article, the developed simulation platform is presented as well as sample simulation results.  The environment simulator is based on crop growth models, weed growth models, soil models, spatial variation generation and weather statistics.  Models for the environment were found in literature and were tailored and tuned to fit the simulation purposes, to form a collection of models.  The collection of models was evaluated by using sensitivity analysis.  Furthermore, a full scale scenario was simulated over one season, incorporating 9000 spatial cells in five fields of a farm.   Keywords: robots, crop growth models, soil water models, decision making, operation plannin

How AI Systems Challenge the Conditions of Moral Agency?

The article explores the effects increasing automation has on our conceptions of human agency. We conceptualize the central features of human agency as ableness, intentionality, and rationality and define responsibility as a central feature of moral agency. We discuss suggestions in favor of holding AI systems moral agents for their functions but join those who refute this view. We consider the possibility of assigning moral agency to automated AI systems in settings of machine-human cooperation but come to the conclusion that AI systems are not genuine participants in joint action and cannot be held morally responsible. Philosophical issues notwithstanding, the functions of AI systems change human agency as they affect our goal setting and pursuing by influencing our conceptions of the attainable. Recommendation algorithms on news sites, social media platforms, and in search engines modify our possibilities to receive accurate and comprehensive information, hence influencing our decision making. Sophisticated AI systems replace human workforce even in such demanding fields as medical surgery, language translation, visual arts, and composing music. Being second to a machine in an increasing number of fields of expertise will affect how human beings regard their own abilities. We need a deeper understanding of how technological progress takes place and how it is intertwined with economic and political realities. Moral responsibility remains a human characteristic. It is our duty to develop AI to serve morally good ends and purposes. Protecting and strengthening the conditions of human agency in any AI environment is part of this task.Peer reviewe

Electrochemical sensors annd physicochemical analysis as tools for studying human exposure to indoor air contaminants

Sisäilmayhdistys raportti 37, Toim. Mervi Ahola, Anna Merikari Julkaisija SIY Sisäilmatieto Oy, ISSN 1237-1866; ISBN 978-952-5236-49-1 Painopaikka Grano Oy, VaasaTutkimuskammioihin asennettiin 8 anturia: hiilidioksidi, happi, vetysyanidi, rikkivety, VOC anturi (tVOC ja CO2 ekvivalentti VOC), palavat kaasut, lämpötila, kosteus. Mittaustulokset tallentuivat Arduino-mikrokontrollerin kautta minuutin väliajoin PC-tietokoneelle AgilentVEE 9.3 ohjelmalla, joka toimi myös grafiikkanäyttönä. Vakavasti sisäilmahaittaisista rakennuksista peräisin olleet materiaalit kerryttivät kosteaan kammioilmaan atmosfäärin, jonka mittausvasteet poikkesivat verrokeista. Vetysyanidin lisäys kammioilmaan (> 5 ppm) pysäytti joidenkin materiaalien hiilidioksidi- ja tVOC emissiot, mutta emissiot palautuivat, mikä viittaa päästöjä emittoivien mikrobien syaniditoleranssiin. Kapillaarielektroforeettinen analyysi antoi viitteitä siitä, että sisäilmaongelmaisista tiloista kerätyt tiivistevedet sisälsivät jonitonta tensidiä ja / tai kationista alkyyliammonium kloridia, joita julkisten tilojen siivousaineet yleisesti sisältävät.Tutkimuskammioihin asennettiin 8 anturia: hiilidioksidi, happi, vetysyanidi, rikkivety, VOC anturi (TVOC ja CO2 ekvivalentti VOC), palavat kaasut, lämpötila, kosteus. Mittaustulokset tallentuivat Arduino-mikrokontrollerin kautta minuutin väliajoin PC-tietokoneelle Agilent VEE 9.3 ohjelmalla, joka toimi myös grafiikkanäyttönä. Vakavasti sisäilmahaittaisista rakennuksista peräisin olleet materiaalit kerryttivät kosteaan kammioilmaan atmosfäärin, jonka mittausvasteet poikkesivat verrokeista. Vetysyanidin lisäys kammioilmaan (>5 ppm) pysäytti joidenkin materiaalien hiilidioksidi- ja TVOC päästöt, mutta päästöt palautuivat, mikä viittaa päästöjä emittoivien mikrobien syaniditoleranssiin ja kykyyn tuottaa energiaa alternatiivisella oksidaasilla ympäristöissä, joissa muut mikrobit eivät menesty. Kapillaarielektroforeettinen analyysi antoi viitteitä siitä, että sisäilmaongelmaisista tiloista kerätyt tiivistevedet sisälsivät jonitonta tensidiä ja/tai kationista alkyyliammonium kloridia, joita julkisten tiojen siivousaineet yleisesti sisältävät.Peer reviewe

Evolutionary debunking arguments against theism, reconsidered

Evolutionary debunking arguments (EDAs) against religious beliefs move from the claim that religious beliefs are caused by off-track processes to the conclusion that said religious beliefs are unjustified and/or false. Prima facie, EDAs commit the genetic fallacy, unduly conflating the context of discovery and the context of justification. In this paper, we first consider whether EDAs necessarily commit the genetic fallacy, and if not, whether modified EDAs (e.g., those that posit falsehood-tracking or perniciously deceptive belief-forming mechanisms) provide successful arguments against theism. Then, we critically evaluate more recent attempts to argue that a more promiscuous evolutionary scepticism renders religious belief unjustified because, unlike commonsense and scientific beliefs, religious beliefs have no way out of such scepticism

Evolutionary debunking arguments against theism, reconsidered

Evolutionary debunking arguments (EDAs) against religious beliefs move from the claim that religious beliefs are caused by off-track processes to the conclusion that said religious beliefs are unjustified and/or false. Prima facie, EDAs commit the genetic fallacy, unduly conflating the context of discovery and the context of justification. In this paper, we first consider whether EDAs necessarily commit the genetic fallacy, and if not, whether modified EDAs (e.g., those that posit falsehood-tracking or perniciously deceptive belief-forming mechanisms) provide successful arguments against theism. Then, we critically evaluate more recent attempts to argue that a more promiscuous evolutionary scepticism renders religious belief unjustified because, unlike commonsense and scientific beliefs, religious beliefs have no way out of such scepticism

Nonlinear Model Predictive Trajectory Control in Tractor-Trailer System for Parallel Guidance in Agricultural Field Operations

Abstract: So called automatic guidance systems are becoming more common in agricultural tractors, so that a driver does not need to steer the vehicle. The systems are mostly relying on GPS with correction. However, these systems usually steer only the tractor itself, despite the fact that the implement is the one that has to be run side by side with the previous swath. With towed implements, or trailers, it is not easy to keep the position of implement on track if the angle of the steering wheels of a tractor is the only resource under control. In this paper, a system with a standard tractor with front steering wheels and an active joint in the drawbar of the trailer are both controlled by the automatic guidance system. Besides, the positioning is not only based on GPS, but also with a local sensor that detects an edge of the previous swath; and this sensor is installed on the trailer. To control this system with two inputs and two outputs with nonlinear kinematics, a multivariable controller is needed for trajectory control. In the paper, an approach to the trajectory control in case of the tractor-trailer system with nonlinear model predictive control (NMPC) is studied. The test results show that the performance is better than with linear model predictive control that was tested in earlier study. Tests were done in driving speeds 8, 10 and 12 km/h. In a curved path, the tractor following error was typically less than 12 cm and in the implement less than 8 cm. The constant control cycle is achieved by alternating the prediction horizon length. By that way, the best possible solution is always gained at the limits of computation time

ISOBUS compatible implements in the project AGRIX

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