基于鱼群涌现行为启发的集群机器人硬注意力强化模型

Hard attention reinforcement model for swarm robotics inspired by fish school emergence behavior

生物集群运动模型能使集群机器人涌现秩序,但是所形成的机器人自然集群秩序难以有效地被人工控制,为此提出鱼群硬注意力模型来解析实验鱼群数据中的交互行为。该模型通过编码器网络、图注意力网络、信息聚合网络、预解码网络以及最终解码网络等结构来获取焦点单体的重要邻居;再利用深度确定性策略梯度技术设计轨道强化网络与安全强化网络,以实现集群的人工控制。多智能体仿真与集群机器人实验结果表明:所提方法能够实现集群的人工轨道、安全控制,重要邻居信息为解决集群运动的强化学习难题提供了新思路,所提控制模型在无人机群空中协作、智慧农机集群作业、物流仓储多体搬运等领域具有较大的应用潜力。

The biological swarm motion model enables the emergence of order in robot collectives,but controlling the natural swarm order formed by robots is challenging.To address this issue,this paper proposed the fish school hard attention model to analyze interaction behaviors in experimental fish school data.This model utilized structures such as an encoder network,graph attention network,information aggregation network,pre-decoding network and a final decoding network to capture crucial information about the focal individual s important neighbors.Subsequently,it employed deep deterministic policy gradient techniques to design trajectory reinforcement networks and safety reinforcement networks to achieve artificial control of the swarm.Results from multi-agent simulations and experiments with swarm robotics demonstrate that the proposed method can realize artificial trajectory and safety control of collectives.The utilization of high-attention neighborhood information for resolving reinforcement learning challenges in collective motion provides a novel approach.The proposed control model exhibits substantial potential applications in areas such as collaborative aerial operations of drone swarms,intelligent agricultural machinery operations,and multi-robot material handling in logistics and warehousing.