基于自适应负载灰狼优化算法的多机器人通信研究

Multi-robot communication based on adaptive loaded grey wolf optimizer

针对多机器人系统在特殊任务背景下任务层并发大数据流量的远距离实时转发问题,提出自适应负载灰狼优化(ALGWO)算法。该文方法利用应用层感知任务及转发信息内容的属性,建立基于任务重要性、实时性和网络时延等综合因素的数据转发机制。结合ALGWO算法,通过改变路由发现过程中的路径耗费和全局路径寻优,实现实时数据的高效转发。相比于动态源路由(DSR)协议,该文方法可以找到所有的可达路径,并根据机器人的自身状态和任务感知选择当前状态下负载最小的路径。在寻路时采用源驱动方式,减小了网络的开销。采用泛洪的寻路方式,尝试所有可能的路径,所以该协议机制也是高稳健的。实验结果表明,该文方法具有较好的实时性和可移植性,具有实际应用价值。

Aiming at the remote real-time forwarding problem of large concurrent data traffic at task level in multi-robot system under special task background,an adaptive load gray wolf optimization(ALGWO)algorithm is proposed.A data forwarding mechanism is proposed based on task importance,real-time and network delay by using the attributes of application layer sensing tasks and forwarding information content.Combined with ALGWO algorithm,efficient forwarding of real-time data is realized by changing the path cost and global path optimization in the process of route discovery.Compared with dynamic source routing(DSR)protocol,this method can find all the reachable paths,and select the path with the least load according to the robot’s own state and task awareness.The source driven method is used to reduce the network overhead.The protocol mechanism is also highly robust because it uses flooding routing to try all possible paths.The experimental results show that this method has good real-time performance and portability,and has practical application value.