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A Collision Cone Approach for Control Barrier Functions (2403.07043v1)

Published 11 Mar 2024 in cs.RO

Abstract: This work presents a unified approach for collision avoidance using Collision-Cone Control Barrier Functions (CBFs) in both ground (UGV) and aerial (UAV) unmanned vehicles. We propose a novel CBF formulation inspired by collision cones, to ensure safety by constraining the relative velocity between the vehicle and the obstacle to always point away from each other. The efficacy of this approach is demonstrated through simulations and hardware implementations on the TurtleBot, Stoch-Jeep, and Crazyflie 2.1 quadrotor robot, showcasing its effectiveness in avoiding collisions with dynamic obstacles in both ground and aerial settings. The real-time controller is developed using CBF Quadratic Programs (CBF-QPs). Comparative analysis with the state-of-the-art CBFs highlights the less conservative nature of the proposed approach. Overall, this research contributes to a novel control formation that can give a guarantee for collision avoidance in unmanned vehicles by modifying the control inputs from existing path-planning controllers.

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References (40)
  1. J. Sun, J. Tang, and S. Lao, “Collision avoidance for cooperative uavs with optimized artificial potential field algorithm,” IEEE Access, vol. 5, pp. 18 382–18 390, 2017.
  2. A. W. Singletary, K. Klingebiel, J. R. Bourne, N. A. Browning, P. T. Tokumaru, and A. D. Ames, “Comparative analysis of control barrier functions and artificial potential fields for obstacle avoidance,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8129–8136, 2021.
  3. I. Kolmanovsky, E. Garone, and S. Di Cairano, “Reference and command governors: A tutorial on their theory and automotive applications,” in 2014 American Control Conference, 2014, pp. 226–241.
  4. S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin, “Hamilton-jacobi reachability: A brief overview and recent advances,” in 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 2242–2253.
  5. Y. Lin and S. Saripalli, “Collision avoidance for uavs using reachable sets,” in 2015 International Conference on Unmanned Aircraft Systems (ICUAS), 2015, pp. 226–235.
  6. Z. Li, “Comparison between safety methods control barrier function vs. reachability analysis,” 2021. [Online]. Available: https://arxiv.org/abs/2106.13176
  7. M. Castillo-Lopez, S. A. Sajadi-Alamdari, J. L. Sanchez-Lopez, M. A. Olivares-Mendez, and H. Voos, “Model predictive control for aerial collision avoidance in dynamic environments,” in 2018 26th Mediterranean Conference on Control and Automation (MED), 2018, pp. 1–6.
  8. S. Yu, M. Hirche, Y. Huang, H. Chen, and F. Allgöwer, “Model predictive control for autonomous ground vehicles: a review,” Autonomous Intelligent Systems, vol. 1, 12 2021.
  9. A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function-based quadratic programs with application to adaptive cruise control,” in 53rd IEEE Conference on Decision and Control, 2014, pp. 6271–6278.
  10. 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, aug 2017. [Online]. Available: https://doi.org/10.1109%2Ftac.2016.2638961
  11. A. Manjunath and Q. Nguyen, “Safe and robust motion planning for dynamic robotics via control barrier functions,” in 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 2122–2128.
  12. S. He, J. Zeng, B. Zhang, and K. Sreenath, “Rule-based safety-critical control design using control barrier functions with application to autonomous lane change,” 2021. [Online]. Available: https://arxiv.org/abs/2103.12382
  13. M. Abduljabbar, N. Meskin, and C. G. Cassandras, “Control barrier function-based lateral control of autonomous vehicle for roundabout crossing,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 859–864.
  14. W. Xiao, C. G. Cassandras, and C. A. Belta, “Bridging the gap between optimal trajectory planning and safety-critical control with applications to autonomous vehicles,” Automatica, vol. 129, p. 109592, 2021.
  15. C. Li, Z. Zhang, A. Nesrin, Q. Liu, F. Liu, and M. Buss, “Instantaneous local control barrier function: An online learning approach for collision avoidance,” arXiv preprint arXiv:2106.05341, 2021.
  16. M. Marley, R. Skjetne, and A. R. Teel, “Synergistic control barrier functions with application to obstacle avoidance for nonholonomic vehicles,” in American Control Conference (ACC), 2021, pp. 243–249.
  17. Y. Chen, H. Peng, and J. Grizzle, “Obstacle avoidance for low-speed autonomous vehicles with barrier function,” IEEE Transactions on Control Systems Technology, vol. 26, no. 1, pp. 194–206, 2018.
  18. T. D. Son and Q. Nguyen, “Safety-critical control for non-affine nonlinear systems with application on autonomous vehicle,” in 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 7623–7628.
  19. Y. Chen, A. Singletary, and A. D. Ames, “Guaranteed obstacle avoidance for multi-robot operations with limited actuation: A control barrier function approach,” IEEE Control Systems Letters, vol. 5, no. 1, pp. 127–132, 2021.
  20. G. Wu and K. Sreenath, “Safety-critical control of a planar quadrotor,” in 2016 American Control Conference (ACC), 2016, pp. 2252–2258.
  21. W. Xiao and C. Belta, “Control barrier functions for systems with high relative degree,” CoRR, vol. abs/1903.04706, 2019. [Online]. Available: http://arxiv.org/abs/1903.04706
  22. ——, “High-order control barrier functions,” IEEE Transactions on Automatic Control, vol. 67, no. 7, pp. 3655–3662, 2022.
  23. P. Thontepu, B. G. Goswami, M. Tayal, N. Singh, S. S. P I, S. S. M G, S. Sundaram, V. Katewa, and S. Kolathaya, “Collision cone control barrier functions for kinematic obstacle avoidance in ugvs,” in 2023 Ninth Indian Control Conference (ICC), 2023, pp. 293–298.
  24. M. Tayal and S. Kolathaya, “Control barrier functions in dynamic uavs for kinematic obstacle avoidance: a collision cone approach,” arXiv preprint arXiv:2303.15871, 2023.
  25. B. G. Goswami, M. Tayal, K. Rajgopal, P. Jagtap, and S. Kolathaya, “Collision cone control barrier functions: Experimental validation on ugvs for kinematic obstacle avoidance,” arXiv preprint arXiv:2310.10839, 2023.
  26. P. Fiorini and Z. Shiller, “Motion planning in dynamic environments using the relative velocity paradigm,” in [1993] Proceedings IEEE International Conference on Robotics and Automation, 1993, pp. 560–565 vol.1.
  27. ——, “Motion planning in dynamic environments using velocity obstacles,” The International Journal of Robotics Research, vol. 17, no. 7, pp. 760–772, 1998. [Online]. Available: https://doi.org/10.1177/027836499801700706
  28. A. Chakravarthy and D. Ghose, “Obstacle avoidance in a dynamic environment: a collision cone approach,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 28, no. 5, pp. 562–574, 1998.
  29. ——, “Generalization of the collision cone approach for motion safety in 3-d environments,” Autonomous Robots, vol. 32, no. 3, pp. 243–266, Apr 2012. [Online]. Available: https://doi.org/10.1007/s10514-011-9270-z
  30. D. Bhattacharjee, A. Chakravarthy, and K. Subbarao, “Nonlinear model predictive control and collision-cone-based missile guidance algorithm,” Journal of Guidance, Control, and Dynamics, vol. 44, no. 8, pp. 1481–1497, 2021. [Online]. Available: https://doi.org/10.2514/1.G005879
  31. M. Babu, Y. Oza, A. K. Singh, K. M. Krishna, and S. Medasani, “Model predictive control for autonomous driving based on time scaled collision cone,” in 2018 European Control Conference (ECC).   IEEE, 2018, pp. 641–648.
  32. P. Polack, F. Altché, B. d’Andréa Novel, and A. de La Fortelle, “The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?” in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 812–818.
  33. C. Folkestad, S. X. Wei, and J. W. Burdick, “Koopnet: Joint learning of koopman bilinear models and function dictionaries with application to quadrotor trajectory tracking,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1344–1350.
  34. 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), 2019, pp. 3420–3431.
  35. M. Igarashi, I. Tezuka, and H. Nakamura, “Time-varying control barrier function and its application to environment-adaptive human assist control,” IFAC-PapersOnLine, vol. 52, no. 16, pp. 735–740, 2019, 11th IFAC Symposium on Nonlinear Control Systems NOLCOS 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896319318804
  36. X. Xu, P. Tabuada, J. W. Grizzle, and A. D. Ames, “Robustness of control barrier functions for safety critical control.” IFAC-PapersOnLine, vol. 48, no. 27, pp. 54–61, 2015, analysis and Design of Hybrid Systems ADHS. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896315024106
  37. E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org, 2016–2019.
  38. G. M. Hoffmann, C. J. Tomlin, M. Montemerlo, and S. Thrun, “Autonomous automobile trajectory tracking for off-road driving: Controller design, experimental validation and racing,” in 2007 American Control Conference, 2007, pp. 2296–2301.
  39. J. Panerati, H. Zheng, S. Zhou, J. Xu, A. Prorok, and A. P. Schoellig, “Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.
  40. Bitcraze. (2018) Crazyflie python library. hhttps://github.com/bitcraze/crazyflie-lib-python.
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