Routing Algorithms for Robot Assisted Precision Irrigation

Thayer, Thomas C.; Vougioukas, Stavros; Goldberg, Ken; Carpin, Stefano

doi:10.1109/icra.2018.8461242

Cited by 32 publications

(55 citation statements)

References 16 publications

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“…Aisle graphs [8], [27] model motion constraints emerging when robots navigate in structured environments like agricultural fields. Vertices denote task locations, and edges represent connections between locations.…”

Section: Related Workmentioning

confidence: 99%

“…Also, some tasks can be more urgent than others, and have to be prioritized. It is often the case [3], [6], [7] that there exists some prior information about a required task (e.g., older measurements of soil moisture [8]) that can bias robot task assignment(s). Hence, it is necessary to develop approaches that utilize limited prior information to plan tasks with uncertain costs and priority level.…”

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confidence: 99%

“…To model this, we consider the cost to complete a task to be a stochastic random variable that follows some known distribution. The cost to move between locations, however, is considered to be deterministic [8]. Specific details are given in the following.…”

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confidence: 99%

“…The paper also contributes a new Stochastic-Vertex-Cost Aisle Graph (SAG). SAG is an extension of the aisle graph [8], [27], which is often used to describe agriculture-related environments. The main novelty of SAG is that it can represent uncertain task costs.…”

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Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Kan

Thayer

Carpin

et al. 2021

IEEE Robot. Autom. Lett.

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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 runtime. 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.Index Terms-Planning, robotics and automation in agriculture and forestry, scheduling and coordination, task and motion planning. I. INTRODUCTIONA UTONOMOUS agricultural mobile robots become increasingly more capable for persistent missions like monitoring crop health [1] and sampling specimens [2] across extended spatio-temporal scales to enhance efficiency and productivity in precision agriculture [3]. An autonomous robot (or a team of them) needs to perform certain tasks in distinct locations of the environment subject to a specific budget [4] on the actions Manuscript

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Section: Related Workmentioning

confidence: 99%

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Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Kan

Thayer

Carpin

et al. 2021

IEEE Robot. Autom. Lett.

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“…Many domestic and overseas scholars adopt different methods to solve the TSP problem [1][2][3][4][5][6][7][8][9]. For TSP problems in precision agriculture: Thomas C. Thayer and other scholars in the literature [10] proposed that the machine should be adjusted effectively according to the battery power and usage time in precise irrigation. Thomas C. Thayer and other scholars also make a feasibility analysis on the application of two greedy heuristic methods in this problem.…”

Section: Introductionmentioning

confidence: 99%

Solution of TSP Problem of Measurement of Soil Attributes for Precision Agriculture

Yun¹,

Shao²,

Mai³

2019

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There are many PATCHES in the measurement of soil attributes in precision agriculture, which are greatly different from the surrounding soil attributes. In this paper, firstly, we build a TSP model of precision agriculture based on the location of PATCHES. Then, we use the improved ant colony algorithm to solve the TSP model. Numerical simulations show the effectiveness and reliability of the proposed method.

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Distributed Estimation of Scalar Fields with Implicit Coordination

Booth,

Carpin

2024

Springer Proceedings in Advanced Robotics

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Routing Algorithms for Robot Assisted Precision Irrigation

Cited by 32 publications

References 16 publications

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Solution of TSP Problem of Measurement of Soil Attributes for Precision Agriculture

Distributed Estimation of Scalar Fields with Implicit Coordination

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