Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
120 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Resilient source seeking with robot swarms (2309.02937v3)

Published 6 Sep 2023 in cs.RO, cs.SY, and eess.SY

Abstract: We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from measurements of the field strength at the robot locations and their relative positions concerning the centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the robustness of the algorithm by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots always calculate an ascending direction compensates for the loss of individuals and mitigates issues arising from the actuator and sensor noises.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (15)
  1. G.-Z. Yang, J. Bellingham, P. E. Dupont, P. Fischer, L. Floridi, R. Full, N. Jacobstein, V. Kumar, M. McNutt, R. Merrifield et al., “The grand challenges of science robotics,” Science robotics, vol. 3, no. 14, p. eaar7650, 2018.
  2. M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, pp. 1–41, 2013.
  3. P. Ogren, E. Fiorelli, and N. E. Leonard, “Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment,” IEEE Transactions on Automatic control, vol. 49, no. 8, pp. 1292–1302, 2004.
  4. V. Kumar, D. Rus, and S. Singh, “Robot and sensor networks for first responders,” IEEE Pervasive computing, vol. 3, no. 4, pp. 24–33, 2004.
  5. K. McGuire, C. De Wagter, K. Tuyls, H. Kappen, and G. C. de Croon, “Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment,” Science Robotics, vol. 4, no. 35, p. eaaw9710, 2019.
  6. W. Li, J. A. Farrell, S. Pang, and R. M. Arrieta, “Moth-inspired chemical plume tracing on an autonomous underwater vehicle,” IEEE Transactions on Robotics, vol. 22, no. 2, pp. 292–307, 2006.
  7. J. N. Twigg, J. R. Fink, L. Y. Paul, and B. M. Sadler, “Rss gradient-assisted frontier exploration and radio source localization,” in 2012 IEEE International Conference on Robotics and Automation.   IEEE, 2012, pp. 889–895.
  8. E. Rosero and H. Werner, “Cooperative source seeking via gradient estimation and formation control,” in 2014 UKACC International Conference on Control (CONTROL).   IEEE, 2014, pp. 634–639.
  9. S. A. Barogh and H. Werner, “Cooperative source seeking with distance-based formation control and non-holonomic agents,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 7917–7922, 2017.
  10. L. Briñón-Arranz, L. Schenato, and A. Seuret, “Distributed source seeking via a circular formation of agents under communication constraints,” IEEE Transactions on Control of Network Systems, vol. 3, no. 2, pp. 104–115, 2015.
  11. L. Briñón-Arranz, A. Renzaglia, and L. Schenato, “Multirobot symmetric formations for gradient and hessian estimation with application to source seeking,” IEEE Transactions on Robotics, vol. 35, no. 3, pp. 782–789, 2019.
  12. R. Fabbiano, F. Garin, and C. Canudas-de Wit, “Distributed source seeking without global position information,” IEEE Transactions on Control of Network Systems, vol. 5, no. 1, pp. 228–238, 2016.
  13. Z. Li, K. You, and S. Song, “Cooperative source seeking via networked multi-vehicle systems,” Automatica, vol. 115, p. 108853, 2020.
  14. J. Cochran and M. Krstic, “Nonholonomic source seeking with tuning of angular velocity,” IEEE Transactions on Automatic Control, vol. 54, no. 4, pp. 717–731, 2009.
  15. S. Al-Abri and F. Zhang, “A distributed active perception strategy for source seeking and level curve tracking,” IEEE Transactions on Automatic Control, vol. 67, no. 5, pp. 2459–2465, 2021.
Citations (2)

Summary

We haven't generated a summary for this paper yet.