Simulation and analysis of peer-to-peer robot swarm network

D.V. Rahozin, V.Ye. Smirnov

Abstract


In the paper we describe the full cycle development of TDMA-based communication protocol for a swarm of robots, including simulation and hard-ware implementation. The protocol is targeted to have a robust, inter ference-immune transport in the network. First, we developed a simulator, based on SimPy simulation pack age, which helps us to run thousands of simulations for proving the concept of TDMA-based communications for robotic swarm. Second, using the simulator we developed a bunch of techniques for the TDMA transport to improve network robustness and simulations allowed to gather statistics and choose the better algorithm. Third, we employed so-called AI tools to implement parts of simulator and a helper technique to convert simulation code to embedded code, approaching “digital twin” paradigm. Finally, the simulated protocols are successfully ported to hardware, which supports LoRa protocol, but not limited to LoRa physical layer. The resulting embedded code works accordingly to simulation results and gathered statistics. The developed sim ulation environment and modern so-called AI tools allowed to shorten dramatically the embedded software development cycle and evaluate algorithm efficiency information from the simulation results before applying on real hardware.

Prombles in programming 2026; 2: 58-66


Keywords


swarm simulation; wireless network; digital twin; TDMA-based communication

Full Text:

PDF

References


Agrawal R., Faujdar N., Romero C.A.T., Sharma O., Abdulsahib G.M., Khalaf O.I, Mansoor R.F., Ghoneim O.A. Classification and comparison of ad hoc networks: A review. // Egyptian Informatics Journal, Vol. 24, Issue 1, 2023, p. 1-25.

Kimon P. Valavanis, George J. Vachtsevanos. Handbook of Unmanned Aerial Vehicles. Springer, 2014. 3022 p.

Mohsan, S.A.H., Othman, N.Q.H., Li, Y. et al. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intel Serv Robotics 16, 109–137 (2023).

Mueller, M., Smith, N., Ghanem, B. (2016). A Benchmark and Simulator for UAV Tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. LNCS, vol 9905. Springer, Cham.

Paredes W.D., Kaushal H., Vakilinia I.; Prodanoff Z. LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues. Sensors 2023, 23, 2403.

Rahozin D. (2008) Modeling synchronised sensor networks // Problems in Programming, special issue, 2008, #2-3. P. 721-729.

Kanzaki A., Uemukai T., Hara T., Nishio S. Dynamic TDMA Slot Assignment in Ad Hoc Networks // In Proc. 17th Int. Conf. on Advanced Information Networking and Applications (AINA’03), 2003. – P. 330–336.

Campanile L., Gribaudo M., Iacono M., Marulli F., Mastroianni, M. Computer Network Simulation with ns-3: A Systematic Literature Review. Electronics, 9(2), 2020, p. 272.

Virdis A., Kirsche M. Recent Advances in Network Simulation The OMNeT++ Environment and its Ecosystem: The OMNeT++ Environment and its Ecosystem, 2019. ISBN 978-3 030-12841-8.

Voigt T., Bor M., Roedig U. Alonso, J. Mitigating Inter-Network Interference in LoRa Networks. In EWSN ’17 Procs. of the 2017 Int. Conf. on Embedded Wireless Systems and Networks (pp. 323-328). ACM Press.

SimPy homepage.

Haridevan A., Kang J., Yuan M., Shan J. ROS2-Gazebo Simulator for Drone Applications. In Proc. of 2024 Intl. Conf. on Unmanned Aircraft Systems (ICUAS), p. 1232-1238.


Refbacks

  • There are currently no refbacks.