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Robotic Shepherding in Cluttered and Unknown Environments using Control Barrier Functions (2407.15701v2)

Published 22 Jul 2024 in cs.RO and cs.MA

Abstract: This paper introduces a novel control methodology designed to guide a collective of robotic-sheep in a cluttered and unknown environment using robotic-dogs. The dog-agents continuously scan the environment and compute a safe trajectory to guide the sheep to their final destination. The proposed optimization-based controller guarantees that the sheep reside within a desired distance from the reference trajectory through the use of Control Barrier Functions (CBF). Additional CBF constraints are employed simultaneously to ensure inter-agent and obstacle collision avoidance. The efficacy of the proposed approach is rigorously tested in simulation, which demonstrates the successful herding of the robotic-sheep within complex and cluttered environments.

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References (30)
  1. I. Lachow, “The upside and downside of swarming drones,” Bulletin of the atomic scientists, vol. 73, no. 2, pp. 96–101, 2017.
  2. S. Yin, R. He, J. Li, L. Chen, and S. Zhang, “Research on the operational mode of manned/unmanned collaboratively detecting drone swarm,” in IEEE International Conference on Unmanned Systems (ICUS), Beijing, China, Nov. 2021, pp. 560–564.
  3. C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” in Proceedings of the 14th annual conference on Computer graphics and interactive techniques, NY, United States, Aug. 1987, pp. 25–34.
  4. N. K. Long, K. Sammut, D. Sgarioto, M. Garratt, and H. A. Abbass, “A comprehensive review of shepherding as a bio-inspired swarm-robotics guidance approach,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 4, pp. 523–537, 2020.
  5. G. Antonelli, F. Arrichiello, and S. Chiaverini, “The entrapment/escorting mission,” IEEE Robotics and Automation Magazine, vol. 15, no. 1, pp. 22–29, 2008.
  6. D. Zhu, R. Lv, X. Cao, and S. X. Yang, “Multi-AUV hunting algorithm based on bio-inspired neural network in unknown environments,” International Journal of Advanced Robotic Systems, vol. 12, no. 11, p. 166, 2015.
  7. S. Van Havermaet, P. Simoens, T. Landgraf, and Y. Khaluf, “Steering herds away from dangers in dynamic environments,” Royal Society Open Science, vol. 10, no. 5, p. 230015, 2023.
  8. M. Bacon and N. Olgac, “Swarm herding using a region holding sliding mode controller,” Journal of Vibration and Control, vol. 18, no. 7, pp. 1056–1066, 2012.
  9. A. Pierson and M. Schwager, “Bio-inspired non-cooperative multi-robot herding,” in IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, May 2015, pp. 1843–1849.
  10. ——, “Controlling noncooperative herds with robotic herders,” IEEE Transactions on Robotics, vol. 34, no. 2, pp. 517–525, 2017.
  11. V. S. Chipade and D. Panagou, “Multiagent planning and control for swarm herding in 2-D obstacle environments under bounded inputs,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1956–1972, 2021.
  12. E. Sebastián and E. Montijano, “Multi-robot implicit control of herds,” in IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 2021, pp. 1601–1607.
  13. E. Sebastián, E. Montijano, and C. Sagüés, “Adaptive multirobot implicit control of heterogeneous herds,” IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3622–3635, 2022.
  14. S. Zhang, X. Lei, M. Duan, X. Peng, and J. Pan, “A distributed outmost push approach for multi-robot herding,” IEEE Transactions on Robotics, vol. 40, pp. 1706–1723, 2024.
  15. J. Grover, N. Mohanty, C. Liu, W. Luo, and K. Sycara, “Noncooperative herding with control barrier functions: Theory and experiments,” in IEEE 61st Conference on Decision and Control (CDC), Cancún, Mexico, Dec. 2022, pp. 80–86.
  16. N. Mohanty, J. Grover, C. Liu, and K. Sycara, “Distributed multirobot control for non-cooperative herding,” in International Symposium on Distributed Autonomous Robotic Systems.   Montbéliard, France: Springer, Nov. 2022, pp. 317–332.
  17. M. Nagumo, “Über die lage der integralkurven gewöhnlicher differentialgleichungen,” Proceedings of the Physico-Mathematical Society of Japan. 3rd Series, vol. 24, pp. 551–559, 1942.
  18. S. Prajna and A. Jadbabaie, “Safety verification of hybrid systems using barrier certificates,” in International Workshop on Hybrid Systems: Computation and Control.   Springer, 2004, pp. 477–492.
  19. P. Wieland and F. Allgöwer, “Constructive safety using control barrier functions,” IFAC Proceedings Volumes, vol. 40, no. 12, pp. 462–467, 2007.
  20. V. M. Gonçalves, D. Chaikalis, A. Tzes, and F. Khorrami, “Safe multi-agent drone control using control barrier functions and acceleration fields,” Robotics and Autonomous Systems, vol. 172, p. 104601, 2024.
  21. T. Ibuki, S. Wilson, J. Yamauchi, M. Fujita, and M. Egerstedt, “Optimization-based distributed flocking control for multiple rigid bodies,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1891–1898, 2020.
  22. S. Khan, M. Baranwal, and S. Sukumar, “Decentralized safe control for multi-robot navigation in dynamic environments with limited sensing,” in 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2024.   Auckland, New Zealand: ACM, p. 2330–2332.
  23. B. Dai, R. Khorrambakht, P. Krishnamurthy, V. Goncalves, A. Tzes, and F. Khorrami, “Safe navigation and obstacle avoidance using differentiable optimization based control barrier functions,” IEEE Robotics and Automation Letters, vol. 8, no. 9, pp. 5376–5383, 2023.
  24. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in 18th IEEE European control conference (ECC), Naples, ITALY, Jun. 2019, pp. 3420–3431.
  25. V. M. Gonçalves, P. Krishnamurthy, A. Tzes, and F. Khorrami, “Control Barrier Functions With Circulation Inequalities,” IEEE Transactions on Control Systems Technology, vol. 32, no. 4, pp. 1426–1441, 2024.
  26. B. Dai, P. Krishnamurthy, and F. Khorrami, “Learning a better control barrier function,” in IEEE 61st Conference on Decision and Control (CDC), 2022, pp. 945–950.
  27. W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2D LiDaR SLAM,” in IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 2016, pp. 1271–1278.
  28. H. U. Unlu, D. Chaikalis, A. Tsoukalas, and A. Tzes, “UAV-Navigation Using Map Slicing and Safe Path Computation,” in 9th IEEE International Conference on Automation, Robotics and Applications (ICARA), Abu Dhabi, UAE, Feb. 2023, pp. 208–212.
  29. J. Chaussard, M. Couprie, and H. Talbot, “Robust skeletonization using the discrete λ𝜆\lambdaitalic_λ-medial axis,” Pattern Recognition Letters, vol. 32, no. 9, pp. 1384–1394, 2011.
  30. D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 2011, pp. 2520–2525.

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