CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-10-18
Cited: 0
Clicked: 1703
Silan LI, Shengyu ZHANG, Tao JIANG. Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300295 @article{title="Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks", %0 Journal Article TY - JOUR
多跳有损自组网下多机器人集群分裂模型构建1华中科技大学网络空间安全学院6G移动通信研究中心,中国武汉市,430074 2华中科技大学武汉光电国家研究中心,中国武汉市,430074 摘要:在多机器人集群系统中,不可靠的通信网络可能引发群体分裂现象,进而为集群任务带来不利影响。本文研究网络拓扑特征参数对集群分裂现象的影响,以期为多机器人集群系统的网络构建提供理论指导。具体地,首先针对多机器人集群系统提出一种分布式"通信-计算-执行"协议,以表征多跳有损自组织网络下机器人的信息交互和运动控制过程。该协议考虑了信息的单跳及多跳传输成功概率,并利用离散时间Olfati-Saber模型实现集群控制。基于该协议,针对特定初始状态下的集群场景构建了分裂预测模型。该模型明确了与集群分裂现象相关的关键系统状态特征及网络拓扑特征,可在确定性网络拓扑下根据系统初始状态完成群体分裂预测。根据这些特征,进一步利用基于反向传播神经网络的数据拟合方法,构建了集群分裂概率模型,可表征网络拓扑参数与集群分裂概率之间的函数关系。仿真结果验证了所提预测模型和集群分裂概率模型的有效性和准确性。最后,对多机器人集群自组网的构建提出指导建议。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Alam MM, Arafat MY, Moh S, et al., 2022. Topology control algorithms in multi-unmanned aerial vehicle networks: an extensive survey. J Netw Comput Appl, 207:103495. ![]() [2]Andrei N, 2007. Scaled conjugate gradient algorithms for unconstrained optimization. Comput Optim Appl, 38(3):401-416. ![]() [3]Antonelli G, Arrichiello F, Chiaverini S, 2010. Flocking for multi-robot systems via the null-space-based behavioral control. Swarm Intell, 4(1):37-56. ![]() [4]Arafat MY, Moh S, 2022. A Q-learning-based topology-aware routing protocol for flying ad hoc networks. IEEE Int Things J, 9(3):1985-2000. ![]() [5]Arafat MY, Poudel S, Moh S, 2021. Medium access control protocols for flying ad hoc networks: a review. IEEE Sens J, 21(4):4097-4121. ![]() [6]Chung FRK, 1997. Spectral graph theory. Proc CBMS Regional Conf Series in Mathematics. ![]() [7]Gundlach JH, Schlamminger S, Spitzer CD, et al., 2007. Laboratory test of Newton’s second law for small accelerations. Phys Rev Lett, 98(15):150801. ![]() [8]Hafeez KA, Zhao L, Mark JW, et al., 2013. Distributed multichannel and mobility-aware cluster-based MAC protocol for vehicular ad hoc networks. IEEE Trans Veh Technol, 62(8):3886-3902. ![]() [9]He GY, Li HF, 2017. Distributed control for multirobot systems with collision-free motion coordination. Proc 10th Int Symp on Computational Intelligence and Design, p.72-76. ![]() [10]Huang XQ, Liu AJ, Zhou HB, et al., 2021. FMAC: a self-adaptive MAC protocol for flocking of flying ad hoc network. IEEE Int Things J, 8(1):610-625. ![]() [11]Ibuki T, Wilson S, Yamauchi J, et al., 2020. Optimization-based distributed flocking control for multiple rigid bodies. IEEE Robot Autom Lett, 5(2):1891-1898. ![]() [12]Jiang T, Wu YY, 2008. An overview: peak-to-average power ratio reduction techniques for OFDM signals. IEEE Trans Broadcast, 54(2):257-268. ![]() [13]Jiang T, Yang Y, Song YH, 2005. Exponential companding technique for PAPR reduction in OFDM systems. IEEE Trans Broadcast, 51(2):244-248. ![]() [14]Jiang T, Liu YY, Xiao LX, et al., 2024. PCC polar codes for future wireless communications: potential applications and design guidelines. IEEE Wirel Commun, 31(3):414-420. ![]() [15]Lee SK, 2022. Distributed deformable configuration control for multi-robot systems with low-cost platforms. Swarm Intell, 16(3):169-209. ![]() [16]Li SL, He SY, Zhang Y, et al., 2022a. Edge intelligence enabled heterogeneous multi-robot networks: hybrid framework, communication issues, and potential solutions. IEEE Netw, 36(6):108-115. ![]() [17]Li SL, Hu XY, Jiang T, et al., 2022b. Hop count distribution for minimum hop-count routing in finite ad hoc networks. IEEE Trans Wirel Commun, 21(7):5317-5332. ![]() [18]Li SL, Zhang SY, He GJ, et al., 2024. Discrete-time flocking control in multi-robot systems with random link failures. IEEE Trans Veh Technol, early access. ![]() [19]Liao WX, Wu MQ, Zhao M, et al., 2017. Hop count limitation analysis in wireless multi-hop networks. Int J Distrib Sens Netw, 13(1). ![]() [20]Liu W, Gao ZJ, 2020. A distributed flocking control strategy for UAV groups. Comput Commun, 153:95-101. ![]() [21]Mohamed RE, Hunjet R, Elsayed S, et al., 2023. Connectivity-aware particle swarm optimisation for swarm shepherding. IEEE Trans Emerg Top Comput Intell, 7(3):661-683. ![]() [22]Møller MF, 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neur Netw, 6(4):525-533. ![]() [23]Olfati-Saber R, 2006. Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Contr, 51(3):401-420. ![]() [24]Olfati-Saber R, Iftekhar L, 2012. Flocking for networks of mobile robots with nonlinear dynamics. Proc 9th Int Conf on Informatics in Control, Automation and Robotics, p.353-359. ![]() [25]Patterson S, Bamieh B, El Abbadi A, 2010. Convergence rates of distributed average consensus with stochastic link failures. IEEE Trans Autom Contr, 55(4):880-892. ![]() [26]Reynolds CW, 1987. Flocks, herds and schools: a distributed behavioral model. Proc 14th Annual Conf on Computer Graphics and Interactive Techniques, p.25-34. ![]() [27]Sastry S, 1999. Lyapunov stability theory. In: Sastry S (Ed.), Nonlinear Systems: Analysis, Stability, and Control. Springer, New York, USA. ![]() [28]Shao JL, Zheng WX, Shi L, et al., 2021. Leader–follower flocking for discrete-time Cucker–Smale models with lossy links and general weight functions. IEEE Trans Autom Contr, 66(10):4945-4951. ![]() [29]Shen GQ, Lei L, Li ZL, et al., 2022. Deep reinforcement learning for flocking motion of multi-UAV systems: learn from a digital twin. IEEE Int Things J, 9(13):11141-11153. ![]() [30]Su HS, Wang XF, Lin ZL, 2007. Flocking of multi-agents with a virtual leader part I: with a minority of informed agents. Proc 46th IEEE Conf on Decision and Control, p.2937-2942. ![]() [31]Sun JQ, Xiong FR, Schütze O, et al., 2019. Cell Mapping Methods. Springer Singapore, Singapore. ![]() [32]Toh C, Delwar M, Allen D, 2002. Evaluating the communication performance of an ad hoc wireless network. IEEE Trans Wirel Commun, 1(3):402-414. ![]() [33]Wang FC, Chen Y, 2020. Fast convergent flocking control of multi-agent systems with switching communication topology. Proc American Control Conf, p.695-700. ![]() [34]Wang T, Qu DM, Jiang T, 2016. Parity-check-concatenated polar codes. IEEE Commun Lett, 20(12):2342-2345. ![]() [35]Wang YP, Zheng KX, Tian DX, et al., 2020. Cooperative channel assignment for VANETs based on multiagent reinforcement learning. Front Inform Technol Electron Eng, 21(7):1047-1058. ![]() [36]Yazdani S, Haeri M, 2017. A sampled-data algorithm for flocking of multi-agent systems. Proc Artificial Intelligence and Robotics, p.147-152. ![]() [37]Yazdani S, Su HS, 2022. A fully distributed protocol for flocking of time-varying linear systems with dynamic leader and external disturbance. IEEE Trans Syst Man Cybern Syst, 52(2):1234-1242. ![]() [38]Yazdani S, Haeri M, Su HS, 2019. Sampled-data leader–follower algorithm for flocking of multi-agent systems. IET Contr Theory Appl, 13(5):609-619. ![]() [39]Yazdani S, Haeri M, Su HS, 2020. Multi-rate sampled-data algorithm for leader-follower flocking. IET Contr Theory Appl, 14(19):3038-3046. ![]() [40]Yuan X, Feng ZY, Xu WJ, et al., 2018. Secure connectivity analysis in unmanned aerial vehicle networks. Front Inform Technol Electron Eng, 19(3):409-422. ![]() [41]Zheng JY, Dong JG, Xie LH, 2017. Synchronization of the delayed Vicsek model. IEEE Trans Autom Contr, 62(11):5866-5872. ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>