Task Planning on Stochastic Aisle Graphs for Precision Agriculture

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 prioritiz...

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Vydáno v:IEEE robotics and automation letters Ročník 6; číslo 2; s. 3287 - 3294
Hlavní autoři: Kan, Xinyue, Thayer, Thomas C., Carpin, Stefano, Karydis, Konstantinos
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Agriculture
Algorithms
Apexes
Base stations
Budgets
Costs
Multiple robots
Planning
Robot kinematics
robotics and automation in agriculture and forestry
Robots
scheduling and coordination
Stochastic processes
Task analysis
task and motion planning
Task planning (robotics)
Vineyards
Water consumption
ISSN:2377-3766, 2377-3766
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Shrnutí: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.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3062337