Nov 9, 2021 · At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain.

Jan 23, 2025 · To address these challenges, this paper proposes a novel graph reinforcement learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios.

Jan 15, 2024 · This study evaluated various task allocation techniques, focusing on their speed of task allocation and management of task queues. We apply the CCEP over realistic scenarios and observe essential insights about the behavior of each technique.

Feb 1, 2024 · Five types of constraints are designed and represented by graph structures with different connection forms. Heuristic graph convolution scheduling agent is created to provide flexible and interpretable task allocation strategies.

Oct 4, 2024 · We introduce a new graph neural operator-based approach for task allocation in a system of heterogeneous robots composed of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs).

We formulate a dynamic task allocation problem where tasks can appear randomly at any location and agents can assign themselves to a task at any time. Once a task is completed, agents are free to move on to a new task. The goal is to complete tasks as quickly as possible as they arrive.

Apr 19, 2024 · Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems.

Nov 15, 2024 · The results showed that Random Forest outperformed the other classifiers in task allocation prediction, achieving an accuracy of 96.7 %, followed by K-NN (94.2 %), Decision Tree (93.5 %), and AdaBoost (93 %). The study demonstrates that ML models are effective in tackling task allocation issues in DASD settings, and the outcomes are promising. 1.

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

Download scientific diagram | A graphic representation of the task allocation problem. from publication: Local Ant System for Allocating Robot Swarms to Time-constrained Tasks | We...