Scholarlink

A swarm robotic system, consisting of numerous distributed autonomous robots, has been increasingly envisioned for applications in coordinated object removal tasks, such as clearing debris and fallen trees in unknown and complex environments typical of disaster sites. In these environments, the system should be robust, flexible, and scalable, and able to function even when communication between robots is interrupted. Although some previous studies have demonstrated flexible cooperative behaviors using the centralized approach, there is still no autonomous distributed swarm robotic system that takes these considerations into account. This paper aims to develop a coordinated system of distributed autonomous swarm robots with robustness, flexibility, and scalability by applying the Transformer encoder to swarm systems. The focus of this study is on coordinated object removal tasks. Using the TurtleBot3 Burger robot model, we train a coordinated object removal behavior that operates without any communication between robots, leveraging reinforcement learning. We demonstrate that this behavior can be applied to scenarios involving varying numbers and sizes of objects, as well as different quantities of robots, without the need for retraining. We further explore the effect of noise on sensor data and motor performance. Additionally, we demonstrate the application of our system in a rescue scenario within an unknown environment, where it coordinates with another autonomous mobile robot to enhance effectiveness.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reinforcement Learning of Scalable, Flexible, and Robust Cooperative Transport Behavior Using the Transformer Encoder

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Yuichiro Sueoka,
  • Takahiro Yoshida,
  • Koichi Osuka

摘要

A swarm robotic system, consisting of numerous distributed autonomous robots, has been increasingly envisioned for applications in coordinated object removal tasks, such as clearing debris and fallen trees in unknown and complex environments typical of disaster sites. In these environments, the system should be robust, flexible, and scalable, and able to function even when communication between robots is interrupted. Although some previous studies have demonstrated flexible cooperative behaviors using the centralized approach, there is still no autonomous distributed swarm robotic system that takes these considerations into account. This paper aims to develop a coordinated system of distributed autonomous swarm robots with robustness, flexibility, and scalability by applying the Transformer encoder to swarm systems. The focus of this study is on coordinated object removal tasks. Using the TurtleBot3 Burger robot model, we train a coordinated object removal behavior that operates without any communication between robots, leveraging reinforcement learning. We demonstrate that this behavior can be applied to scenarios involving varying numbers and sizes of objects, as well as different quantities of robots, without the need for retraining. We further explore the effect of noise on sensor data and motor performance. Additionally, we demonstrate the application of our system in a rescue scenario within an unknown environment, where it coordinates with another autonomous mobile robot to enhance effectiveness.