Sathyan Anoop, Cohen Kelly, Ma Ou
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH, United States.
Front Robot AI. 2020 Dec 23;7:601243. doi: 10.3389/frobt.2020.601243. eCollection 2020.
This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.
本文介绍了一种基于遗传模糊的新范式，用于开发一组可扩展的去中心化同质机器人以执行协作任务。在这项工作中，团队中机器人的数量可以改变，而无需任何额外的训练。这项工作中考虑的动态问题涉及多个静止机器人，它们的目标是将一个通过电缆与每个机器人物理连接的公共执行器带到机器人工作空间内的任意目标位置。机器人之间不相互通信。这意味着每个机器人对团队中其他机器人的行动没有明确的了解。在任何时刻，机器人仅拥有与公共执行器和目标相关的信息。遗传模糊系统（GFS）框架用于训练机器人的控制器以实现共同目标。所有机器人共享相同的GFS模型。通过这种方式，我们利用机器人的同质性来减少训练参数。这也提供了无需任何额外训练即可扩展到任何团队规模的能力。本文通过在涉及不同数量机器人团队的大量案例上测试系统，展示了这种方法的有效性。尽管机器人是静止的，但本文提出的GFS框架对机器人的放置没有任何限制。本文描述了可扩展的GFS框架及其在涉及各种团队规模和机器人位置的广泛案例中的适用性。我们还展示了移动目标情况下的结果。
