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A Path Planning Application for a Mountain Vineyard Autonomous Robot

机译:山地葡萄园自治机器人的路径规划申请

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Coverage path planning (CPP) is a fundamental agricultural field task required for autonomous navigation systems. It is also important for resource management, increasingly demanding in terms of reducing costs and environmental polluting agents as well as increasing productivity. Additional problems arise when this task involves irregular agricultural terrains where the crop follows non-uniform configurations and extends over steep rocky slopes. For mountain vineyards, finding the optimal path to cover a restricted set of terraces, some of them with dead ends and with other constraints due to terrain morphology, is a great challenge. The problem involves other variables to be taken into account such as speed, direction and orientation of the vehicle, fuel consumption and tank capacities for chemical products. This article presents a decision graph-based approach, to solve a Rural Postman Coverage like problem using A* and Dijkstra algorithms simultaneously to find the optimal sequence of terraces that defines a selected partial coverage area of the vineyard. The decision structure is supported by a graph that contains all the information of the Digital Terrain Model (DTM) of the vineyard. In this first approach, optimality considers distance, cost and time requirements. The optimal solution was represented in a graphical user OpenGL application developed to support the path planning process. Based on the results, it was possible to prove the applicability of this approach for any vineyards which extend like routes. Near optimal solutions based on other specific criteria could also be considered for future work.
机译:覆盖路径规划(CPP)是自主导航系统所需的基本农业领域任务。对资源管理的重要性也很重要,在降低成本和环境污染剂方面越来越苛刻,以及提高生产率。当这项任务涉及不规则的农业地形时出现了额外的问题,其中作物遵循非均匀的配置并延伸陡峭的岩石斜坡。对于山地葡萄园,寻找最佳路径,以覆盖一套限制的露台,其中一些与死亡结局和其他限制因地形形态而言,是一个巨大的挑战。问题涉及其他变量,如速度,方向和方向的速度,方向和方向,化学产品的燃料消耗和罐容量。本文提出了一种基于决策图的方法,可以同时使用*和dijkstra算法解决问题,如问题,以查找定义葡萄园的选定部分覆盖区域的最佳露台序列。决策结构由包含葡萄园的数字地形模型(DTM)的所有信息的图表支持。在第一种方法中,最优性考虑距离,成本和时间要求。在开发支持路径规划过程的图形用户OpenGL应用程序中表示最佳解决方案。根据结果​​,可以证明这种方法对延伸的任何葡萄园的适用性。还可以考虑基于其他具体标准的近优化解决方案,以便将来的工作。

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