Toronto Metropolitan University
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Vehicle path planning for complete field coverage using genetic algorithms

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journal contribution
posted on 2021-05-21, 17:26 authored by A. E. F. Ryerson, Q. Zhang
In farming operations, one of the fundamental issues facing farmer is the cost of running the farm. If the equipment the farmer is using can be made more efficient, the cost of farming will be reduced. One way of making agricultural equipment more efficient is to develop automated or autonomous functions for the equipment. One of the fundamental tasks for autonomous equipment is to plan the path for the equipment to travel. This paper reports the research on the feasibility of creating an automated method of path planning for autonomous agricultural equipment. Genetic algorithms were chosen to plan the paths with a primary goal of creating an optimal path guiding the equipment to completely cover a field while avoiding all known obstacles. Two example fields were designed for evaluating the feasibility of this concept on simple problems. While simulation results verified the feasibility of this conceptual path planning method, they also indicated that further development would be required before the algorithm could actually be implemented on agricultural equipment for real-world field applications.
Keywords: Automonous equipment, genetic algorithms, off-road vehicle, path planning




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