基于概率图模型的多机器人自组织协同围捕方法

Multi-robot self-organizing cooperative pursuit method based on probabilistic graphical model

多机器人协同围捕是群体智能在对抗环境下的典型运用.在感知能力受限、环境结构未知、目标状态不确定的真实环境中,多机器人协同围捕面临环境适应性、任务可扩展性等多方面挑战.针对这一问题,本文提出一种基于概率图模型的自组织协同围捕方法.首先,建立围捕机器人和围捕对象的运动学模型,并给出围捕任务的数学描述.在此基础上,构建可扩展的协同围捕“感知–决策”概率图模型结构,并为模型中各节点状态设计概率分布参数估计方法;同时,将围捕任务阶段化,设计狼群狩猎行为启发的围捕策略,以提高围捕效率.最后,开展数值仿真和软件在环实验,验证了所提方法的节点可扩展性、环境适应性、系统抗风险性和模型可迁移性.

Multi-robot cooperative pursuit is a typical application of collective intelligence in adversarial environments.In real environments where perception is limited,environmental structure is unknown,and the target status is uncertain,multi-robot cooperative pursuit faces many challenges such as the environmental adaptability and the task scalability.To address this problem,a self-organized cooperative pursuit method based on the probabilistic graphical models is proposed.First,the kinematic models of the pursuit robots and target are established,and the mathematical description of the pursuit problem is given.On this basis,a scalable cooperative pursuit“perception-decision”probabilistic graphical model structure is constructed,and a probability distribution parameter estimation method is designed for the states of each node in the model.Then,inspired by hunting behaviors of wolves,a staged pursuit strategy is designed to improve the capture efficiency.Finally,the numerical simulation and software-in-the-loop experiments are conducted to verify the node scalability,environmental adaptability,system risk resistance,and model transferability of the proposed method.