在无协商、指派及中心控制的自主水下航行器（AUV）集群控制中，AUV群集在进行避碰、多目标追踪等行为时群集分裂的现象不可避免，如果分裂不可控，易出现AUV丢失或分裂后的子群规模差距过大等问题。为了面对外部刺激时群集能进行可控分群，提出一种融合拓扑交互和信息耦合度的分群控制算法，该算法使用信息耦合度作为评判指标衡量个体间的影响作用，并根据AUV在水下通信难、感知难的特点，引入了拓扑交互机制，减少群集组群和分群所需要的信息，提升AUV组群和分群的效果。仿真实验验证该算法的可行性和有效性。
In the AUV cluster control without negotiation, assignment and central control, the phenomenon of swarm splitting is inevitable when AUV swarm performs collision avoidance, multi-target tracking and other behaviors. If the splitting is uncontrollable, problems such as loss of AUV or large gap of subgroup size after splitting are likely to occur. In order to face the external stimulation can cluster the controlled group, this paper proposes a fission control algorithm combining topological interaction and ICD, The algorithm uses the degree of information coupling as a judgment index to measure the influence of individuals, and according to the characteristics of the AUV underwater communication, perception, introduced the topology interaction mechanism, reduce the cluster group and group the information they need to improve the AUV group and the effect of clustering. Simulation results show the feasibility and effectiveness of the proposed algorithm.
2022,44(1): 104-107 收稿日期：2021-03-05
DOI：10.3404/j.issn.1672-7649.2022.01.020
分类号：TP13
基金项目：国防科技创新特区项目（18-H863-00-TS-002-034-01）
作者简介：吴函(1996-)，男，硕士研究生，研究方向为水下机器人控制技术
参考文献：
[1] 黄琰, 李岩, 俞建成, 等. AUV智能化现状与发展趋势[J]. 机器人, 2020, 42(2): 215−231.
[2] 刘宗春, 田彦涛, 李成凤. 动态阻尼环境下多领导者群体机器人系统协同跟踪控制[J]. 机器人, 2011(4): 3–11
[3] CHEN J, GAUCI M, PRICE M J, et al. Segregation in swarms of e-puck robots based on the brazil nut effect[C]//Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1. 2012: 163−170.
[4] MORGAN D S, SCHWARTZ I B. Dynamic coordinated control laws in multiple agent models[J]. Physics Letters A, 2005, 340(1–4): 121–131
[5] 刘明雍, 雷小康, 彭星光. 融合邻域自适应跟随的群集系统分群控制方法研究[J]. 西北工业大学学报, 2013, 31(2): 250–254
[6] 刘明雍, 雷小康, 杨盼盼, 等. 基于信息耦合度的群集系统自组织分群方法[J]. 控制与决策, 2015, 30(2): 271–276
[7] 杨盼盼, 刘明雍, 雷小康, 等. 基于自组织结对行为的群集机器人分群控制方法[J]. 西北工业大学学报, 2015, 33(1): 147–152
[8] TURGUT A E, BOZ İ C, OKAY İ E, et al. Interaction network effects on position-and velocity-based models of collective motion[J]. Journal of the Royal Society Interface, 2020, 17(169): 20200165
[9] BALLERINI M, CABIBBO N, CANDELIER R, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study[J]. Proceedings of the National Academy of Sciences, 2008, 105(4): 1232–1237
[10] 于月平, 段海滨, 范彦铭, 等. 仿欧椋鸟大规模超机动行为的无人机集群转弯控制[J]. 机器人, 2020, 42(4): 385−393.
