鱼群涌现机制下集群机器人运动强化的迁移控制

采用鱼群模型驱动多智能体可以涌现出优良的运动特性,但是,由于机器人与真实鱼类相比具有较大的差异性,使得鱼群模型难以应用于真实机器人系统.为此,提出一种结合深度学习与强化学习的迁移控制方法,首先,使用鱼群运动数据训练深度网络(deep neural network, DNN)模型,以此作为机器人成对交互的基础;然后,连接强化学习的深度确定性策略梯度方法(deep deterministic policy gradient, DDPG)来修正DNN模型的输出,设计集群最大视觉尺寸方法挑选关键邻居,从而将DNN+DDPG模型拓展到多智能体的运动控制.集群机器人运动实验表明:所提出方法能使机器人仅利用单个邻居信息就能形成可靠、稳定的集群运动,与单纯DNN直接迁移控制相比,所提出DNN+DDPG控制框架既可以保存原有鱼群运动的灵活性,又能增强机器人系统的安全性与可控性,使得该方法在集群机器人运动控制领域具有较大的应用潜力.

Transfer control of swarm robotics motion reinforcement employing fish schooling emergency mechanism

The multi-agent system driven by the fish schooling model can emerge excellent characteristics of motion. However, due to the individual differences between robots and real fish, it is difficult for a fish schooling model to be directly applied to the actual robotics system. Hence, a transfer control method combined with deep learning and deep reinforcement learning is proposed. Firstly, a deep neural network(DNN) model is trained by the data of fish schooling, which is the basement for the interactive control of robots. Then, a deep reinforcement learning method, named deep deterministic policy gradient(DDPG), is connected to the output of the DNN model. Finally, based on the above DNN$+$DDPG model, a key neighbor selection method of the maximum group visual size is designed to expand the DNN$+$DDPG model to multi-agent motion control. Collective motion experiments show that the proposed method can formulate reliable and stable collective motion of the robots via individual information. Compared with the pure DNN transfer control, the proposed DNN$+$DDPG control frame not only preserves the flexibility of the collective motion of fish schooling, but also enhances the safety and controllability of the robotics system. Thus, there exists strong potential application of the proposed method for the swarm robotics motion control.