Downloads

https://doi.org/10.53941/ams.2024.100005
Ji, Z., Zhu, D., & Du, B. Bandit-Based Multi-Agent Source Seeking with Safety Guarantees. Applied Mathematics and Statistics. 2024, 1(1), 5. doi: https://doi.org/10.53941/ams.2024.100005
Volume 1, lssue 1, Dec 2024
Copyright (c) 2024 by the authors.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Article

Bandit-Based Multi-Agent Source Seeking with Safety Guarantees

Zhibin Ji † , Dingqi Zhu † and Bin Du *

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211016, China

* Correspondence: iniesdu@nuaa.edu.cn

† These authors contributed equally to this work.

Received: 2 November 2024; Revised: 24 December 2024;Accepted: 26 December 2024; Published: 27 December 2024

 

Abstract: In this paper, we focus on a multi-agent source seeking problem where the safety of agents is characterized by a set of linear constraints. In particular, the safety constraints are also dependent on the unknown environment states, which makes the source seeking problem challenging to solve. To overcome such a challenge, we introduce a new notion of measurable path and then specify the reachability condition for all agents. A time-sequence of exploration is further introduced to help the agents to escape the stuck positions. To provide a performance guarantee for our source seeking algorithm, we perform the regret analysis and show a sub-linear cumulative regret. Finally, we evaluate the effectiveness of our SafeSearch algorithm through a set of simulations.

Keywords:

source seeking bandit algorithm safety constraint conffdence bound

References

  1. Fabbiano, R.; Garin, F.; Canudas-de Wit, C. Distributed source seeking without global position information. IEEE Trans.Control. Netw. Syst. 2016, 5, 228–238.
  2. Cochran, J.; Krstic, M. Nonholonomic source seeking with tuning of angular velocity. IEEE Trans. Autom. Control 2009,54, 717–731.
  3. Cochran, J.; Kanso, E.; Kelly, S.D.; et al. Source seeking for two nonholonomic models of fsh locomotion. IEEE Trans.Robot. 2009, 25, 1166–1176.
  4. Rolf, E.; Fridovich-Keil, D.; Simchowitz, M.; et al. A successive-elimination approach to adaptive robotic source seeking.IEEE Trans. Robot. 2020, 37, 34–47.
  5. Luo, W.; Tay, W.P.; Leng, M. Infection spreading and source identifcation: A hide and seek game. IEEE Trans. SignalProcess. 2016, 64, 4228–4243.
  6. Poveda, J.I.; Benosman, M.; Vamvoudakis, K.G. Data-enabled extremum seeking: a cooperative concurrent learning-basedapproach. Int. J. Adapt. Control. Signal Process. 2021, 35, 1256–1284.
  7. Ghaffarkhah, A.; Mostof, Y. Path planning for networked robotic surveillance. IEEE Trans. Signal Process. 2012,60, 3560–3575.
  8. Qian, K.; Claudel, C. Real-time mobile sensor management framework for city-scale environmental monitoring. J. Comput.Sci. 2020, 45, 101205.
  9. Sugiyama, H.; Tsujioka, T.; Murata, M. Real-time exploration of a multi-robot rescue system in disaster areas. Adv. Robot.2013, 27, 1313–1323.
  10. Li, S.; Kong, R.; Guo, Y. Cooperative distributed source seeking by multiple robots: Algorithms and experiments.IEEE/ASME Trans. Mechatron. 2014, 19, 1810–1820.
  11. Fabbiano, R.; De Wit, C.C.; Garin, F. Source localization by gradient estimation based on Poisson integral. Automatica2014, 50, 1715–1724.
  12. Brino´n-Arranz, L.; Schenato, L.; Seuret, A. Distributed source seeking via a circular formation of agents under communi-cation constraints. IEEE Trans. Control. Netw. Syst. 2015, 3, 104–115.
  13. Li, S.; Guo, Y. Distributed source seeking by cooperative robots: All-to-all and limited communications. In Proceedingsof the 2012 IEEE international Conference on Robotics and Automation, St Paul, Minnesota, USA, 14–18 May 2012;pp. 1107–1112.
  14. Poveda, J.I.; Benosman, M.; Teel, A.R.; et al. Robust coordinated hybrid source seeking with obstacle avoidance inmultivehicle autonomous systems. IEEE Trans. Autom. Control 2021, 67, 706–721.
  15. Jin, Z.; Li, H.; Ahn, C.K. Multiagent Distributed Source Seeking Under Globally Coupled Constraints: Algorithms andExperiments. IEEE Internet Things J. 2024, 11, 38936–38949.
  16. Ramirez-Llanos, E.; Martinez, S. Stochastic source seeking for mobile robots in obstacle environments via the SPSAmethod. IEEE Trans. Autom. Control 2018, 64, 1732–1739.
  17. Atanasov, N.; Le Ny, J.; Michael, N.; et al. Stochastic source seeking in complex environments. In Proceedings of the 2012IEEE International Conference on Robotics and Automation, Saint Paul, MI, USA, 14–18 May 2012; pp. 3013–3018.
  18. Amani, S.; Alizadeh, M.; Thrampoulidis, C. Linear stochastic bandits under safety constraints. Adv. Neural Inf. Process.Syst. 2019, 32, 1.
  19. Du, B.; Qian, K.; Iqbal, H.; et al. Multi-robot dynamical source seeking in unknown environments. In Proceedingsof the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021;pp. 9036–9042.
  20. Du, B.; Qian, K.; Claudel, C.; et al. Multiagent online source seeking using bandit algorithm. IEEE Trans. Autom. Control2022, 68, 3147–3154.
  21. Huang, C.; Du, B.; Chen, M. Multi-UAV Cooperative Online Searching Based on Voronoi Diagrams. IEEE Trans. Aerosp.Electron. Syst. 2024, 60, 3038–3049.
  22. Kia, S.S.; Van Scoy, B.; Cortes, J.; et al. Tutorial on dynamic average consensus: The problem, its applications, and thealgorithms. IEEE Control Syst. Mag. 2019, 39, 40–72.
  23. Du, B.; Mao, R.; Kong, N.; et al. Distributed data fusion for on-scene signal sensing with a multi-UAV system. IEEETrans. Control. Netw. Syst. 2020, 7, 1330–1341.
  24. Mou, S.; Morse, A.S. Finite-time distributed averaging. In Proceedings of the 2014 American Control Conference,Portland, OR, USA, 4–6 June 2014; pp. 5260–5263.
  25. Charalambous, T.; Yuan, Y.; Yang, T.; et al. Distributed fnite-time average consensus in digraphs in the presence of timedelays. IEEE Trans. Control. Netw. Syst. 2015, 2, 370–381.
  26. Du, B.; Qian, K.; Claudel, C.; et al. Jacobi-style iteration for distributed submodular maximization. IEEE Trans. Autom.Control 2022, 67, 4687–4702.