Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 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

Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments (2402.16449v1)

Published 26 Feb 2024 in cs.RO and cs.AI

Abstract: This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles. This framework addresses two significant challenges associated with traditional dynamic control barrier functions (D-CBFs): their online construction and the diminished real-time performance caused by utilizing multiple D-CBFs. To tackle the first challenge, the framework's perception component begins with clustering point clouds via the DBSCAN algorithm, followed by encapsulating these clusters with the minimum bounding ellipses (MBEs) algorithm to create elliptical representations. By comparing the current state of MBEs with those stored from previous moments, the differentiation between static and dynamic obstacles is realized, and the Kalman filter is utilized to predict the movements of the latter. Such analysis facilitates the D-CBF's online construction for each MBE. To tackle the second challenge, we introduce buffer zones, generating Type-II D-CBFs online for each identified obstacle. Utilizing these buffer zones as activation areas substantially reduces the number of D-CBFs that need to be activated. Upon entering these buffer zones, the system prioritizes safety, autonomously navigating safe paths, and hence referred to as the exploration mode. Exiting these buffer zones triggers the system's transition to goal-seeking mode. We demonstrate that the system's states under this framework achieve safety and asymptotic stabilization. Experimental results in simulated and real-world environments have validated our framework's capability, allowing a LiDAR-equipped mobile robot to efficiently and safely reach the desired location within dynamic environments containing multiple obstacles.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. C. Dawson, S. Gao, and C. Fan, “Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control,” IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1749–1767, 2023.
  2. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in 2019 18th European control conference (ECC).   IEEE, 2019, pp. 3420–3431.
  3. J. Zeng, B. Zhang, and K. Sreenath, “Safety-critical model predictive control with discrete-time control barrier function,” in 2021 American Control Conference (ACC), 2021, pp. 3882–3889.
  4. A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3861–3876, 2016.
  5. J. Lin, H. Zhu, and J. Alonso-Mora, “Robust vision-based obstacle avoidance for micro aerial vehicles in dynamic environments,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 2682–2688.
  6. M. Srinivasan, A. Dabholkar, S. Coogan, and P. A. Vela, “Synthesis of control barrier functions using a supervised machine learning approach,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 7139–7145.
  7. K. Long, C. Qian, J. Cortés, and N. Atanasov, “Learning barrier functions with memory for robust safe navigation,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4931–4938, 2021.
  8. A. S. Lafmejani, S. Berman, and G. Fainekos, “Nmpc-lbf: Nonlinear mpc with learned barrier function for decentralized safe navigation of multiple robots in unknown environments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 10 297–10 303.
  9. R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick, “End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 3387–3395.
  10. R. K. Cosner, Y. Yue, and A. D. Ames, “End-to-end imitation learning with safety guarantees using control barrier functions,” in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022, pp. 5316–5322.
  11. C. Dawson, B. Lowenkamp, D. Goff, and C. Fan, “Learning safe, generalizable perception-based hybrid control with certificates,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1904–1911, 2022.
  12. C. Dawson, Z. Qin, S. Gao, and C. Fan, “Safe nonlinear control using robust neural lyapunov-barrier functions,” in Conference on Robot Learning.   PMLR, 2022, pp. 1724–1735.
  13. F. B. Mathiesen, S. C. Calvert, and L. Laurenti, “Safety certification for stochastic systems via neural barrier functions,” IEEE Control Systems Letters, vol. 7, pp. 973–978, 2023.
  14. B. Brito, B. Floor, L. Ferranti, and J. Alonso-Mora, “Model predictive contouring control for collision avoidance in unstructured dynamic environments,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4459–4466, 2019.
  15. Z. Jian, Z. Yan, X. Lei, Z. Lu, B. Lan, X. Wang, and B. Liang, “Dynamic control barrier function-based model predictive control to safety-critical obstacle-avoidance of mobile robot,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 3679–3685.
  16. W. S. Cortez, X. Tan, and D. V. Dimarogonas, “A robust, multiple control barrier function framework for input constrained systems,” IEEE Control Systems Letters, vol. 6, pp. 1742–1747, 2021.
  17. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., “A density-based algorithm for discovering clusters in large spatial databases with noise,” in kdd, vol. 96, no. 34, 1996, pp. 226–231.
  18. E. Welzl, “Smallest enclosing disks (balls and ellipsoids),” in New Results and New Trends in Computer Science: Graz, Austria, June 20–21, 1991 Proceedings.   Springer, 2005, pp. 359–370.
  19. H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.
  20. G. Welch, G. Bishop, et al., “An introduction to the kalman filter,” 1995.
  21. Y. Zhang, L. Kong, S. Zhang, X. Yu, and Y. Liu, “Improved sliding mode control for a robotic manipulator with input deadzone and deferred constraint,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 12, pp. 7814–7826, 2023.
  22. T. Faulwasser and R. Findeisen, “Nonlinear model predictive control for constrained output path following,” IEEE Transactions on Automatic Control, vol. 61, no. 4, pp. 1026–1039, 2015.
  23. G. Grimm, M. J. Messina, S. E. Tuna, and A. R. Teel, “Model predictive control: for want of a local control lyapunov function, all is not lost,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 546–558, 2005.
Citations (3)
View on Semantic Scholar

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now