Task Planning on Stochastic Aisle Graphs for Precision Agriculture
Task Planning on Stochastic Aisle Graphs for Precision Agriculture
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DOI：
10.1109/lra.2021.3062337
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Karydis, Konstantinos
中科院分区：
文献类型：
--
作者：
Kan, Xinyue;Thayer, Thomas C.;Karydis, Konstantinos
这项工作解决任务规划下的不确定性，精确农业应用，任务成本是不确定的，完成任务的收益是成比例的资源消耗（如用水量在精确灌溉）。我们的目标是完成所有任务，同时优先考虑那些更紧迫的任务，并受到不同的预算阈值和任务随机成本的影响。为了描述与农业相关的环境，包括随机成本完成任务，一个新的随机顶点成本通道图（SAG）。然后，提出了一种任务分配算法，称为下一个最佳行动规划（NBA-P）。NBA-P利用由SAG启用的底层结构，并且通过在运行时同时确定要执行的最佳任务和退出（即返回到基站）的最佳时间来处理任务规划问题。所提出的方法进行了测试与模拟数据和真实世界的实验数据集收集在商业葡萄园，在单和多机器人的情况下。在所有情况下，NBA-P在每个访问顶点的返回、因中止任务（即超过预算阈值）而浪费的资源以及总访问顶点方面优于其他评估方法。
This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single- and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.