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DESIGN AND MODELING OF A BALLOON ROBOT WITH WHEEL PADDLES FOR AGRICULTURAL USEXiaotong Huang (18524037) 09 May 2024 (has links)
<p dir="ltr">The research study of Design and Modeling of a Balloon Robot with Wheel Paddles for Agricultural Use (Huang, et al. 2023) presented the design, analysis, and simulation of an innovative agricultural robot that integrated a buoyancy system with a helium balloon and wheeled paddles for navigation, aiming to optimize crop health monitoring. The thesis research initiated with a comprehensive examination of the conceptual design, focusing on the robot's buoyancy mechanism and propulsion system. Detailed motion analysis and kinematic studies underpinned the development of a dynamic model, which was rigorously tested through MATLAB simulations. The MATLAB simulations assessed the unmanned vehicle's operational efficiency, maneuverability, and energy consumption in the environment setting of agricultural. The findings of the new design highlighted the robot's potential to surpass traditional agricultural robots in precision and adaptability, mitigating the limitations of ground and aerial alternatives. The thesis study of the balloon robot concluded with strategic recommendations for future enhancements, emphasizing scalability, payload capacity, and environmental adaptability, thus paving the way for advanced agricultural robotics.</p>
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Scouting algorithms for field robots using triangular mesh mapsLiu, Lifang 31 July 2007
Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.<p>A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.<p>The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method.
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Scouting algorithms for field robots using triangular mesh mapsLiu, Lifang 31 July 2007 (has links)
Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.<p>A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.<p>The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method.
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