基于深度学习的仿生集群运动智能控制

Intelligent control of bionic collective motion based on deep learning

集群运动的自组织控制相较于控制理论方法更具鲁棒性与灵活性,其中具有强大自组织特性的生物种群多表现为单体的等级交互,其特点是交互双方各自影响互不对称,由于信息交互繁杂导致构建等级交互模型仍充满挑战.鉴于此,使用深度学习技术分析红鼻鱼的集群运动实验数据,构建多参数输入的单体等级交互模型,有针对地设计成对交互的深度网络结构,并进行合理训练以获取交互模型,基于视觉压力挑选出关键邻居,将此模型用于该邻居的等级交互,相较于其他邻居选择方式,所提出智能控制方法与真实鱼类的集群运动具有较一致的宏观特性.仿真实验表明:所提出方法能扩展应用到更大规模的集群聚合运动控制中,使得单体仅利用局部信息即可实现大规模的集群运动;该方法具有使用简单、规模灵活、计算快速的特点,在多机器人控制、智能交通系统、饱和集群攻击以及多智能体物流等领域具有广阔的应用前景.

Compared with the method of automatic control theory, the self-organized control for collective motion is more robust and flexible. The strong self-organizing collective motion of biological species is related with individual hierarchical interactions, which is characterized by the asymmetrical influence of the pairwise interaction. Due to the complexity of interaction information, the construction of analytical models of hierarchical interactions is still full of challenges. Based on the deep learning technology, the experimental data of the collective motion of Hemigrammus rhodostomus fish is analysed to construct the individual interaction model with multi-parameter inputs. A deep network structure for pair interaction is designed, and the interaction model is obtained by means of proper training. Based on visual pressure, the individual identifies the key neighbour, which is used for hierarchical interaction built by deep neural networks. Compared with other neighbour selection methods, the macro characteristics are more consistent between the proposed intelligent control method and real fish collective mention experiment. Simulation shows that the proposed method can be extended to larger-scale groups for aggregation control with collective motion, so that the individual can take advantage of local information to achieve large-scale collective motion. The proposed control method is simple to use, flexible for different scale, and fast for calculation. Thus, it has broad application prospects in the fields of multi-robot control, intelligent transportation systems, saturated cluster attacks, and multi-agent logistics.