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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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.
AbstractList	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.
Author	Carpin, Stefano Karydis, Konstantinos Kan, Xinyue Thayer, Thomas C.
Author_xml	– sequence: 1 givenname: Xinyue orcidid: 0000-0002-9608-4290 surname: Kan fullname: Kan, Xinyue email: xkan001@ucr.edu organization: Department of Electrical, and Computer Engineering, University of California, Riverside, USA – sequence: 2 givenname: Thomas C. orcidid: 0000-0001-6930-0755 surname: Thayer fullname: Thayer, Thomas C. email: tthayer@ucmerced.edu organization: Department of Computer Science and Engineering, University of California, Merced, Merced, CA, USA – sequence: 3 givenname: Stefano orcidid: 0000-0003-3837-7463 surname: Carpin fullname: Carpin, Stefano email: scarpin@ucmerced.edu organization: Department of Computer Science and Engineering, University of California, Merced, Merced, CA, USA – sequence: 4 givenname: Konstantinos orcidid: 0000-0002-1144-8260 surname: Karydis fullname: Karydis, Konstantinos email: kkarydis@ece.ucr.edu organization: Department of Electrical, and Computer Engineering, University of California, Riverside, USA
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SubjectTerms	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
Title	Task Planning on Stochastic Aisle Graphs for Precision Agriculture
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