Solving Large-scale Stochastic Orienteering Problems with Aggregation

Thayer, Thomas C.; Carpin, Stefano

doi:10.1109/iros45743.2020.9340899

“…Orienteering is NP-hard, and thus greedy heuristics are often employed [8]. Recent efforts on stochastic orienteering associate stochastic costs to graph edges and propose a time-aware policy for a robot to adjust its path to avoid exceeding a certain budget [31]. However, addressing cases that involve uncertain task cost on vertices for aisle graphs remains open.…”

Section: Related Workmentioning

confidence: 99%

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Kan

¹

,

Thayer

²

,

Carpin

³

et al. 2021

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

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

show abstract

“…Even with motion constraints introduced via aisle graphs, orienteering remains an NP-hard problem and thus greedy heuristics are often employed [8]. Recent efforts on stochastic orienteering associate stochastic costs to graph edges and propose a time-aware policy for a robot to adjust its path to avoid exceeding a certain budget [31]. However, addressing cases that involve uncertain task cost on vertices for aisle graphs remains open.…”

Section: Related Workmentioning

confidence: 99%

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Kan

¹

,

Thayer

²

,

Carpin

³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

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.

show abstract

An Improvement Heuristic Based on Variable Neighborhood Search for Dynamic Orienteering Problems with Changing Node Values and Changing Budgets

Le

¹

,

Middendorf

²

,

Shi

³

2022

SN COMPUT. SCI.

0

View full text Add to dashboard Cite

We study the Dynamic Orienteering Problem (DOP) with changing node values and changing budgets. It is a complex combinatorial optimization problem with many applications, e.g., in tour planning. To solve the DOP, an improvement heuristic based on Variable Neighborhood Search $$\mathrm {VNS}_{\mathrm {DOP}}$$ VNS DOP is proposed. In addition, three methods for handling solutions that became invalid by budget changes are presented. Heuristic $$\mathrm {VNS}_{\mathrm {DOP}}$$ VNS DOP is experimentally compared with two improvement heuristics based on state-of-the-art algorithms for the static Orienteering Problem. In addition, the influence of the three invalid solution handling methods on the algorithms’ optimization behavior is evaluated experimentally. For the experiments, benchmark instances as well as instances generated from existing road networks are used. As a quality measure for the algorithms, their performance over time is used. The results show that both types of dynamic changes, i.e., changes in the node values and changes in the budget, lead to higher volatility in the results for all compared algorithms. However, the latter type has a more negative effect on the performance. Out of the compared algorithms, the proposed heuristic $$\mathrm {VNS}_{\mathrm {DOP}}$$ VNS DOP obtains the best results in most cases on a variety of problem instances with dynamic node values and dynamic budgets, showing that it has a high performance over time in dynamic environments and is able to deal with different levels of dynamic changes. For DOPs with changing budgets, the invalid solution handling method that repairs solutions by fixing the violation of the budget constraint as fast as possible performs best for the considered algorithms.

show abstract

Solving Large-scale Stochastic Orienteering Problems with Aggregation

Cited by 5 publications

References 20 publications

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

An Improvement Heuristic Based on Variable Neighborhood Search for Dynamic Orienteering Problems with Changing Node Values and Changing Budgets

Contact Info

Product

Resources

About