基于动态密度引导的多机器人编队队形变换方法

doi:10.16183/j.cnki.jsjtu.2024.209

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (11): 1783-1797.doi: 10.16183/j.cnki.jsjtu.2024.209

基于动态密度引导的多机器人编队队形变换方法

曹凯¹^,²(), 陈阳泉², 李康¹, 陈超波¹, 阎坤¹, 刘伟超¹

1.西安工业大学电子信息工程学院,西安 710021
2.加州大学默塞德分校 MESA Lab,美国默塞德 CA 95343

收稿日期:2024-06-06 修回日期:2024-06-22 接受日期:2024-06-24 出版日期:2024-11-28 发布日期:2024-12-02
作者简介:曹凯(1984—),副教授,从事多机器人控制、集群控制、源定位的研究;E-mail:caokai@xatu.edu.cn.
基金资助:
国家自然科学基金青年基金(62103315);信息融合技术教育部重点实验室开放基金(202312-IFTKFKT-007);陕西省科技厅项目(2022QFY01-16);陕西省科技厅项目(2023-ZDLNY-61)

Dynamic Density-Guided Method for Multi-Robot Formation Transformation

CAO Kai¹^,²(), CHEN Yangquan², LI Kang¹, CHEN Chaobo¹, YAN Kun¹, LIU Weichao¹

1. School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China
2. MESA Lab, University of California, Merced CA 95343, USA

Received:2024-06-06 Revised:2024-06-22 Accepted:2024-06-24 Online:2024-11-28 Published:2024-12-02

摘要/Abstract

摘要：

针对地面移动机器人编队的队形控制问题,提出了一种基于动态密度引导的多机器人编队队形切换方法.为实现机器人编队不同队形的切换,使用质心维诺划分(CVT)编队控制算法,避免机器人队形切换过程中的碰撞.根据CVT算法的特性,通过给定队形的密度函数,构建初始队形密度函数与期望密度函数之间的过渡密度生成动态密度,并利用CVT算法引导编队中的机器人移动,完成编队队形的切换与重构.仿真结果表明,相比直接使用期望密度函数引导队形切换,该方法不仅成功解决了部分形态编队切换失败问题,而且降低了切换过程中编队整体的平均位置误差.

关键词: 多机器人, 质心维诺划分, 编队控制, 队形切换

Abstract:

This paper addresses the formation control problem for ground mobile robot formations and proposes a formation transition method based on dynamic density guidance. To achieve different formation transitions, a centroidal Voronoi tessellations (CVT) formation control algorithm is utilized to avoid collisions during the transition process. By leveraging the properties of the CVT algorithm, a dynamic density is generated by constructing a transition density function between the initial formation density function and the desired density function. The CVT algorithm then guides the robots in the formation to move and complete the transition and reconstruction of the formation. The simulation results demonstrate that, compared to using the desired density function directly, this method not only successfully resolves certain formation transition failures but also reduces the average positional error of the formation during the transition process.

Key words: multi-robot, centroidal Voronoi tessellations, formation control, formation transformation

中图分类号:

TP242

曹凯, 陈阳泉, 李康, 陈超波, 阎坤, 刘伟超. 基于动态密度引导的多机器人编队队形变换方法[J]. 上海交通大学学报, 2024, 58(11): 1783-1797.

CAO Kai, CHEN Yangquan, LI Kang, CHEN Chaobo, YAN Kun, LIU Weichao. Dynamic Density-Guided Method for Multi-Robot Formation Transformation[J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1783-1797.

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参考文献 20

[1]	GUO S, LIU B, ZHANG S, et al. Continuous-time gaussian process trajectory generation for multi-robot formation via probabilistic inference[C]//International Conference on Intelligent Robots and Systems. Prague, Czech Republic: IEEE, 2021: 9247-9253.
[2]	王树凤, 张钧鑫, 张俊友. 基于人工势场和虚拟领航者的智能车辆编队控制[J]. 上海交通大学学报, 2020, 54(3): 305-311.
	WANG Shufeng, ZHANG Junxin, ZHANG Junyou. Intelligent vehicles formation control based on artificial potential field and virtual leader[J]. Journal of Shanghai Jiao Tong University, 2020, 54(3): 305-311.
[3]	HUANG J, ZHOU S, TU H, et al. Distributed optimization algorithm for multi-robot formation with virtual reference center[J]. Journal of Automatica Sinica, 2022, 9(4): 732-734.
[4]	FEOLA L, TRIANNI V. Adaptive strategies for team formation in minimalist robot swarms[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 4079-4085.
[5]	REZECK P, CHAIMOWICZ L. Chemistry-inspired pattern formation with robotic swarms[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 9137-9144.
[6]	CORTES J, MARTINEZ S, KARATAS T, et al. Coverage control for mobile sensing networks[J]. IEEE Transactions on Robotics and Automation, 2004, 20(2): 243-255.
[7]	BREITENMOSER A, SCHWAGER M, METZGER J C, et al. Voronoi coverage of non-convex environments with a group of networked robots[C]//2010 IEEE International Conference on Robotics and Automation. Anchorage, AK, USA: IEEE, 2010: 4982-4989.
[8]	KANTAROS Y, THANOU M, TZES A. Distributed coverage control for concave areas by a heterogeneous robot-swarm with visibility sensing constraints[J]. Automatica, 2015, 53: 195-207.
[9]	ZHENG L, ZHAO J, CHENG Y, et al. Geometry-constrained crowd formation animation[J]. Computers & Graphics, 2014, 38: 268-276.
[10]	TERUEL E, ARAGUES R, LÓPEZ-NICOLÁS G. A distributed robot swarm control for dynamic region coverage[J]. Robotics and Autonomous Systems, 2019, 119: 51-63. doi: 10.1016/j.robot.2019.06.002
[11]	郑利平, 程亚军, 周乘龙, 等. 异构群体队形光滑变换控制方法[J]. 计算机辅助设计与图形学学报, 2015, 27(10): 1963-1970.
	ZHENG Liping, CHENG Yajun, ZHOU Chenglong, et al. Research on smooth formation control of heterogeneous crowds[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(10): 1963-1970.
[12]	ZHENG X, ZONG C, CHENG J, et al. Visually smooth multi-UAV formation transformation[J]. Graphical Models, 2021, 116: 101111.
[13]	BAI Y, WANG Y, XIONG X, et al. Adaptive multi-agent control with dynamic obstacle avoidance in a limited region[C]//American Control Conference. Atlanta, USA: IEEE, 2022: 4695-4700.
[14]	XU X, DIAZ-MERCADO Y. Multi-robot control using coverage over time-varying domains[C]//International Symposium on Multi-Robot and Multi-Agent Systems. New Brunswick, USA: IEEE, 2019: 179-181.
[15]	XU X, DIAZ-MERCADO Y. Multi-robot control using coverage over time-varying non-convex domains[C]//IEEE International Conference on Robotics and Automation. Paris, France: IEEE, 2020: 4536-4542.
[16]	GUO D, BAI Y, SVININ M, et al. Robust adaptive multi-agent coverage control for flood monitoring[C]//International Siberian Conference on Control and Communications. Kazan, Russia: IEEE, 2021: 1-5.
[17]	XU X, SHI G, TOKEKAR P, et al. Interactive multi-robot aerial cinematography through hemispherical manifold coverage[C]//International Conference on Intelligent Robots and Systems. Kyoto, Japan: IEEE, 2022: 11528-11534.
[18]	TERUEL E, ARAGUES R, LÓPEZ-NICOLÁS G. A practical method to cover evenly a dynamic region with a swarm[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1359-1366.
[19]	EISENBERGER M, NOVOTNY D, KERCHENBAUM G, et al. Neuromorph: Unsupervised shape interpolation and correspondence in one go[C]//Conference on Computer Vision and Pattern Recognition. Nashville, TN, USA: IEEE, 2021: 7473-7483.
[20]	PEYRÉ G, CUTURI M. Computational optimal transport: With applications to data science[J]. Foundations and Trends in Machine Learning, 2019, 11(5/6): 355-607.

ρ(q)	密度分布	维诺图	队形
$\mathrm{e}^{-\sigma(a x+b y+c)^{2}}$			直线形
$\mathrm{e}^{-\sigma\left[a\left\|x-x_{\mathrm{c}}\right\|+b\left(y-y_{\mathrm{c}}\right)+c\right]^{2}}$			V形
$\mathrm{e}^{-\sigma\left[a\left(x-x_{\mathrm{c}}\right)^{2}+b\left(y-y_{\mathrm{c}}\right)^{2}-r^{2}\right]^{2}}$			环形

仿真	切换方式	编队平均位置误差	迭代次数
1	直接切换	—	—
	本文方法	0.038	63
2	直接切换	0.037	72
	本文方法	0.035	69
3	直接切换	—	—
	本文方法	0.045	60
4	直接切换	0.037	69
	本文方法	0.035	73

基于动态密度引导的多机器人编队队形变换方法

Dynamic Density-Guided Method for Multi-Robot Formation Transformation

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