基于蚁群算法的群机器人自组织聚集行为建模

doi:10.16182/j.issn1004731x.joss.17-9119

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (2): 191-200.doi: 10.16182/j.issn1004731x.joss.17-9119

基于蚁群算法的群机器人自组织聚集行为建模

杨卫^1,2, 曾亮^1,2, 康新晨^1,2

1. 中北大学电子测试技术重点实验室,山西太原 030051;
2. 中北大学仪器科学与动态测试教育部重点实验室,山西太原 030051

收稿日期:2017-11-28 修回日期:2018-06-21 出版日期:2020-02-18 发布日期:2020-02-19
作者简介:杨卫(1957-),男,山西太原,本科,教授,研究方向为网域化微武器系统;曾亮(1995-),男,湖北黄冈,硕士生,研究方向为多智能体协同控制;唐新晨(1991-),男,山西忻州,硕士生,研究方向为机器视觉。
基金资助:
山西省高等学校科技创新项目(18002605)

Self-organizing Aggregation Behavior Modeling of Swarm Robots Based on Ant Colony Algorithm

Yang Wei^1,2, Zeng Liang^1,2, Kang Xinchen^1,2

1. Science and Technology on Electronic Test & Measurement Laboratory, North University of China, Taiyuan 030051, China;
2. Key Laboratory of Instrumentation Science & Dynamic Measurement Ministry of Education, North University of China, Taiyuan 030051, China

Received:2017-11-28 Revised:2018-06-21 Online:2020-02-18 Published:2020-02-19

摘要/Abstract

摘要： 针对群机器人聚集行为模式的形成,建立了基于质点系力学系统、以及感知状态加权的自组织运动模型,实现了机器人运动状态的自主转移。建立了关于聚集行为的聚集度、均匀度等评价指标,通过实验分析比较不同模型的聚集性能,为群机器人自组织聚集行为模式的形成奠定了基础。使用分析选定的模型,对有边界约束和无边界约束2种情况进行了自组织聚集行为仿真试验,实现了不同群体规模的最大范围的有效覆盖感知,验证了模型的正确性。

关键词: 蚁群算法, 群机器人, 自组织, 聚集, 运动模型

Abstract: Aiming at the formation of aggregation behavior model of swarm robots, a self-organizing motion model based on particle system mechanics and perceptual state weighting is established to realize the autonomous transfer of robot motion state. The aggregation degree and uniformity index of aggregation behavior are established, and the aggregation performances of different models are analyzed by experiment, which lay a foundation for the formation of self-organized aggregation behavior model of swarm robots. Using the selected model, the self-organizing aggregation behavior simulation experiments are conducted in two cases with boundary constraints and no boundary constraints. The maximum coverage of different groups of scale is realized, and the correctness of the model is verified.

Key words: ant colony algorithm, swarm robots, self-organization, aggregation, motion model

中图分类号:

TP391.9

杨卫, 曾亮, 康新晨. 基于蚁群算法的群机器人自组织聚集行为建模[J]. 系统仿真学报, 2020, 32(2): 191-200.

Yang Wei, Zeng Liang, Kang Xinchen. Self-organizing Aggregation Behavior Modeling of Swarm Robots Based on Ant Colony Algorithm[J]. Journal of System Simulation, 2020, 32(2): 191-200.

参考文献

[1] Ying T, Zheng Z.Research Advance in Swarm Robotics[J]. Defence Technology (S2217-9147), 2013, 9(1): 31-63.
[2] Wen G, Huang J, Wang C, et al.Group consensus control for heterogeneous multi-agent systems with fixed and switching topologies[J]. International Journal of Control (S0020-7179), 2016, 89(2): 259-269.
[3] Dong J, Chen H T, Liu S. A Behavior-Based Policy for Multirobot Formation Control[J]. Applied Mechanics & Materials (S1660-9336), 2012, 220-223: 1181-1185.
[4] Zhang X Y, Duan H B, Luo Q N.Levenberg-Marquardt based artificial physics method for mobile robot oscillation alleviation[J]. Science China (Physics, Mechanics & Astronomy)(S1674-7348), 2014, 57(9): 1771-1777.
[5] 陈世明, 李慧敏, 谢竟, 等. 基于局部估计的功率驱动多智能体网络牵制蜂拥控制算法[J]. 控制与决策, 2013(8): 1190-1194.
Chen Shiming, Li Huimin, Xie Jing, et al.Pinning flocking control algorithm for power-driven multi-agent networks based on local estimation[J]. Control and Decision, 2013, 28(8): 1190-1194.
[6] Pei H, Chen S, Lai Q.A local flocking algorithm of multi-agent dynamic systems[J]. International Journal of Control (S0020-7179), 2015, 88(11): 2242-2249.
[7] Desai J P.A Graph Theoretic Approach for Modeling Mobile Robot Team Formations[J]. Journal of Robotic Systems (S0741-2223), 2002, 19(11): 511-525.
[8] Zhen K, Yucelen T, Doucette E, et al. A Finite-time Consensus Framework over Time-varying Graph Topologies with Temporal Constraints[J]. Journal of Dynamic Systems Measurement & Control (S0022-0434), 2017, 139(7): 071012-1-071012-6.
[9] Sperati V, Trianni V, Nolfi S.Self-organised path formation in a swarm of robots[J]. Swarm Intelligence (S1935-3820), 2011, 5(2): 97-119.
[10] Spears W M, Spears D F, Hamann J C, et al.Distributed, Physics-Based Control of Swarms of Vehicles[J]. Autonomous Robots (S0929-5593), 2004, 17(2/3): 137-162.
[11] 徐望宝, 陈雪波. 一种基于人工力矩的动态队形控制方法[J]. 控制理论应用, 2009, 26(11): 1232-1238.
Xu Wangbao, Chen Xuebo.A dynamic formation control method based on artificial torque[J]. Control Theory and Applications, 2009, 26(11): 1232-1238.
[12] 徐望宝, 陈雪波, 赵杰. 个体机器人局部路径规划的人工力矩方法[J]. 大连理工大学学报, 2012, 52(3): 418-425.
Xu Wangbao, Chen Xuebo, Zhao Jie.Artificial moment method for local path planning of single robot[J]. Journal of Dalian University of Technology, 2012, 52(3): 418-425.
[13] Brooks R A.A Robust Layered Control System for a Mobile Robot[J]. IEEE Journal of Robotics and Automation (S1545-5955), 1986, 2(1): 14-23.
[14] Francesca G, Brambilla M, Trianni V, et al.Analysing an evolved robotic behaviour using a biological model of collegial decision making[C]// International Conference on Simulation of Adaptive Behavior. Berlin, Heidelberg: Springer, 2012: 381-390.
[15] Soysal O, Sahin E.Probabilistic aggregation strategies in swarm robotic systems[C]// Swarm Intelligence Symposium, 2005. Pasadena: IEEE, 2005: 325-332.
[16] Brambilla M, Pinciroli C, Birattari M, et al.Property-driven design for swarm robotics[C]// Proceedings of the 11^th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012). Valencia Spain: International Foundation for Autonomous Agents and Multiagent Systems, 2009: 139-146.
[17] 刘强. 基于智能体的多机器人系统学习方法研究[D].哈尔滨: 哈尔滨工业大学, 2016: 21-33.
Liu Qiang.Agent-based Research Learning Methods in Multi-robot System[D]. Harbin: Harbin Institute of Technology, 2016: 21-33.

基于蚁群算法的群机器人自组织聚集行为建模

Self-organizing Aggregation Behavior Modeling of Swarm Robots Based on Ant Colony Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	梁江涛, 王慧琴. 基于改进蚁群算法的建筑火灾疏散路径规划研究[J]. 系统仿真学报, 2022, 34(5): 1044-1053.
[2]	邓向阳, 张立民, 方伟, 汤淼. 基于双向汇聚引导蚁群算法的机器人路径规划[J]. 系统仿真学报, 2022, 34(5): 1101-1108.
[3]	宁涛, 苟涛, 刘向东. 考虑低碳约束的生鲜农产品冷链物流策略仿真研究[J]. 系统仿真学报, 2022, 34(4): 797-805.
[4]	胡蓉, 陈文博, 钱斌, 郭宁, 向凤红. 学习型蚁群算法求解绿色多车场车辆路径问题[J]. 系统仿真学报, 2021, 33(9): 2095-2108.
[5]	王桐, 高山, 戴天殊. 基于AODV的高适应性车载网络通信协议[J]. 系统仿真学报, 2021, 33(7): 1661-1669.
[6]	王青云, 焦德忠, 史铄, 彭根燕, 孙俊华, 段雨昕. 改进的蚁群干扰资源分配方法[J]. 系统仿真学报, 2021, 33(12): 2967-2974.
[7]	李霏, 苏中. 基于自适应免疫优化的污水处理过程控制[J]. 系统仿真学报, 2021, 33(12): 3012-3020.
[8]	史岩, 张立华, 董受全, 王珏. 基于ACO算法和Bezier曲线优化的巡航导弹航路规划[J]. 系统仿真学报, 2020, 32(1): 122-129.
[9]	邢志伟, 蒋骏贤, 罗晓, 罗谦, 杨扬. 离港值机排队聚集规律及队列模型研究[J]. 系统仿真学报, 2019, 31(8): 1548-1554.
[10]	李默, 赵亮. 基于OPNET的定向天线自组织网邻居发现算法仿真[J]. 系统仿真学报, 2018, 30(5): 1707-1714.
[11]	廖鹰, 易卓, 胡晓峰, 田园, 陶九阳. 基于三维卷积神经网络的战场聚集行为预测[J]. 系统仿真学报, 2018, 30(3): 801-808.
[12]	黄颖坤, 金炜东. 基于SLIDE+SVM的雷达辐射源信号识别[J]. 系统仿真学报, 2017, 29(9): 1944-1949.
[13]	文元桥, 杜磊, 黄亚敏, 孙腾达, 肖长诗, 周春辉. 水上交通流宏观复杂度建模与仿真[J]. 系统仿真学报, 2017, 29(4): 826-831.
[14]	徐小平, 朱秋秋, 王峰. 求解圆排列问题的粒子群蚁群优化算法[J]. 系统仿真学报, 2017, 29(2): 248-254.
[15]	范厚明, 郭振峰, 杨宇. 预约机制下送箱集卡多集装箱码头调度问题[J]. 系统仿真学报, 2017, 29(12): 3051-3060.