Similar articles

Abstract: We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.

多跳有损自组网下多机器人集群分裂模型构建

[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 10^th 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 9^th 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 14^th 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 46^th 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.