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Safe Robot Control using Occupancy Grid Map-based Control Barrier Function (OGM-CBF) (2405.10703v3)

Published 17 May 2024 in cs.RO

Abstract: Safe control in unknown environments is a significant challenge in robotics. While Control Barrier Functions (CBFs) are widely used to guarantee system safety, they often assume known environments with predefined obstacles. The proposed method constructs CBFs directly from perception sensor input and introduces a new first-order barrier function for a 3D kinematic robot motion model. The proposed CBF is constructed by combining Occupancy Grid Mapping (OGM) and Signed Distance Functions (SDF). The OGM framework abstracts sensor inputs, making the solution compatible with any sensor modality capable of generating occupancy maps. Moreover, the OGM enhances situational awareness along the robot's motion trajectory, by integrating both current and previously mapped data. The SDF encapsulates complex obstacle shapes defined by OGM into real-time computable values, enabling the method to handle obstacles of arbitrary shapes. This enables a single constraint in the CBF-QP optimization for each point on the robot, regardless of the number or shape of obstacles. The effectiveness of the proposed approach is demonstrated through simulations on autonomous driving in the CARLA simulator and real-world experiments with an industrial mobile robot, using a simplified 2D version of the method.

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References (28)
  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. 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), pp. 3420–3431, IEEE, 2019.
  3. Q. Nguyen, A. Hereid, J. W. Grizzle, A. D. Ames, and K. Sreenath, “3d dynamic walking on stepping stones with control barrier functions,” in 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 827–834, IEEE, 2016.
  4. 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, pp. 6271–6278, IEEE, 2014.
  5. L. Wang, A. D. Ames, and M. Egerstedt, “Safety barrier certificates for collisions-free multirobot systems,” IEEE Transactions on Robotics, vol. 33, no. 3, pp. 661–674, 2017.
  6. 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, 2020.
  7. Z. Qin, K. Zhang, Y. Chen, J. Chen, and C. Fan, “Learning safe multi-agent control with decentralized neural barrier certificates,” arXiv preprint arXiv:2101.05436, 2021.
  8. A. Singletary, A. Swann, Y. Chen, and A. D. Ames, “Onboard safety guarantees for racing drones: High-speed geofencing with control barrier functions,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2897–2904, 2022.
  9. 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.
  10. 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.
  11. W. Xiao, T.-H. Wang, R. Hasani, M. Chahine, A. Amini, X. Li, and D. Rus, “Barriernet: Differentiable control barrier functions for learning of safe robot control,” IEEE Transactions on Robotics, 2023.
  12. W. Xiao, T.-H. Wang, M. Chahine, A. Amini, R. Hasani, and D. Rus, “Differentiable control barrier functions for vision-based end-to-end autonomous driving,” arXiv preprint arXiv:2203.02401, 2022.
  13. 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.
  14. 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), pp. 7139–7145, IEEE, 2020.
  15. H. Abdi, G. Raja, and R. Ghabcheloo, “Safe control using vision-based control barrier function (v-cbf),” in 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 782–788, IEEE, 2023.
  16. J. Liu, M. Li, J. W. Grizzle, and J.-K. Huang, “Clf-cbf constraints for real-time avoidance of multiple obstacles in bipedal locomotion and navigation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10497–10504, IEEE, 2023.
  17. F. Blanchini, “Set invariance in control,” Automatica, vol. 35, no. 11, pp. 1747–1767, 1999.
  18. H. Moravec and A. Elfes, “High resolution maps from wide angle sonar,” in Proceedings. 1985 IEEE international conference on robotics and automation, vol. 2, pp. 116–121, IEEE, 1985.
  19. G. N. DeSouza and A. C. Kak, “Vision for mobile robot navigation: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 2, pp. 237–267, 2002.
  20. T. Collins and J. Collins, “Occupancy grid mapping: An empirical evaluation,” in 2007 mediterranean conference on control & automation, pp. 1–6, IEEE, 2007.
  21. L. Matthies and A. Elfes, “Integration of sonar and stereo range data using a grid-based representation,” in Proceedings. 1988 IEEE International Conference on Robotics and Automation, pp. 727–733, IEEE, 1988.
  22. S. B. Thrun, “Exploration and model building in mobile robot domains,” in IEEE international conference on neural networks, pp. 175–180, IEEE, 1993.
  23. S. Thrun, “Learning occupancy grid maps with forward sensor models,” Autonomous robots, vol. 15, pp. 111–127, 2003.
  24. T. Chan and W. Zhu, “Level set based shape prior segmentation,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 1164–1170, IEEE, 2005.
  25. C. Dapogny and P. Frey, “Computation of the signed distance function to a discrete contour on adapted triangulation,” Calcolo, vol. 49, pp. 193–219, 2012.
  26. A. Muller, “Energy optimal control of serial manipulators avoiding collisions,” in Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM’04., pp. 299–304, IEEE, 2004.
  27. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning, pp. 1–16, PMLR, 2017.
  28. Springer Science & Business Media, 2011.
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Authors (3)
  1. Golnaz Raja (1 paper)
  2. Teemu Mökkönen (1 paper)
  3. Reza Ghabcheloo (16 papers)
Citations (1)
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