マルチエージェントタスク割り当てのためのタスク要件とエージェント能力の学習
Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation
チーム構成とタスク性能のデータからエージェント能力とタスク要件を線形制約として学習し、既存の最適化ベースのタスク割り当てに組み込むフレームワークを提案。ROS/Gazeboで探索・マニピュレーションタスクにて検証。
著者: Bo Fu, William Smith, Denise Rizzo, Matthew Castanier, Maani Ghaffari, Kira Barton
分類: cs.RO, cs.MA
原文アブストラクト
This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team configurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team configurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multi-agent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%.
