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논문 기본 정보

자료유형
학술대회자료
저자정보
Na-Hyun Lee (Korea Institute of Robotics & Technology Convergence (KIRO)) Tae-Young Uhm (Korea Institute of Robotics & Technology Convergence (KIRO)) Ji-Hyun Park (Korea Institute of Robotics & Technology Convergence (KIRO)) Kyung-Seok Noh (Korea Institute of Robotics & Technology Convergence (KIRO)) Hyo-Gon Kim (Korea Institute of Robotics & Technology Convergence (KIRO))
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2023
발행연도
2023.10
수록면
1,858 - 1,861 (4page)

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초록· 키워드

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Recently, many studies have been conducted that apply deep reinforcement learning to multi-robot task allocation. However, most of them are in the form of distributing the same number of tasks to robots, making it difficult to respond to irregular tasks. In addition, the weight of the object and the payload of the robot cannot be set differently. In this paper, we proposed reinforcement learning based sequential multi robot task allocation algorithm considering weight of objects and payload of robots. This algorithm is designed for use in environments with irregularly occurring tasks, and sequentially assigns the most suitable robot for the tasks that occur. Also, this algorithm has the advantage that it can be applied even if robots have different payloads and objects have different weights. Rainbow DQN was used for this algorithm, and the learning environment was customized using openAI gym.

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Abstract
1. INTRODUCTION
2. PRELIMINARY
3. METHOD
4. SIMULATION
5. CONCLUSION AND FUTURE WORK
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