Task Planning on Stochastic Aisle Graphs for Precision Agriculture

TL;DR

This paper introduces a novel stochastic graph model and a task planning algorithm for precision agriculture, enabling efficient task execution under uncertain costs and resource constraints, demonstrated through simulations and real vineyard data.

Contribution

It proposes a new Stochastic-Vertex-Cost Aisle Graph model and the NBA-P algorithm for real-time, optimal task planning in uncertain agricultural environments.

Findings

NBA-P outperforms other methods in resource efficiency

Effective handling of stochastic task costs demonstrated

Applicable to single and multi-robot scenarios

Abstract

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.