Task Planning on Stochastic Aisle Graphs for Precision Agriculture
This work addresses the problem of efficient and robust task planning for autonomous robots in precision agriculture, particularly for scenarios with uncertain task costs and resource constraints.
This paper tackles task planning under uncertainty for precision agriculture, where task costs are stochastic and task gain is proportional to resource consumption. The proposed Next-Best-Action Planning (NBA-P) algorithm, utilizing a new Stochastic-Vertex-Cost Aisle Graph (SAG), outperforms other methods in terms of return per visited vertex, wasted resources from aborted tasks, and total visited vertices in both simulated and real-world vineyard experiments.
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.