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

Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach (2405.12408v1)

Published 20 May 2024 in cs.RO, cs.SY, and eess.SY

Abstract: A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, thereby offering flexibility and active safety for robot manipulators in dynamic environments. First, discrete-time CBFs are employed to formulate the novel flexible CBFSC with dynamic decay rates for robot manipulators. Following that, the model predictive control (MPC) philosophy is applied, integrating flexible CBFSC as safety constraints into the receding-horizon optimization problem. Significantly, the decay rates of the designed CBFSC are incorporated as decision variables in the optimization problem, facilitating the dynamic enhancement of flexibility during the obstacle avoidance process. In particular, a novel cost function that integrates a penalty term is designed to dynamically adjust the safety margins of the CBFSC. Finally, experiments are conducted in various scenarios using a Universal Robots 5 (UR5) manipulator to validate the effectiveness of the proposed approach.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. M. Safeea, P. Neto, and R. Bearee, “On-Line Collision Avoidance for Collaborative Robot Manipulators by Adjusting Off-Line Generated Paths: An Industrial Use Case,” Robotics and Autonomous Systems, vol. 119, pp. 278-288, Jul. 2019.
  2. J. Burgner-Kahrs, D. C. Rucker, and H. Choset, “Continuum Robots for Medical Applications: A Survey,” IEEE Transactions on Robotics, vol. 31, no. 6, pp. 1261-1280, Dec. 2015.
  3. C. Weng, Q. Yuan, F. Suarez-Ruiz, and I. Chen, “A Telemanipulation-Based Human-Robot Collaboration Method to Teach Aerospace Masking Skills,” IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3076-3084, May 2020.
  4. B. Xiao, and S. Yin, “Exponential Tracking Control of Robotic Manipulators With Uncertain Dynamics and Kinematics,” IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 689-698, Feb. 2019.
  5. W. Zhang, H. Cheng , L. Hao, et al, “An Obstacle Avoidance Algorithm for Robot Manipulators Based on Decision-Making Force,” Robotics and Computer-Integrated Manufacturing, vol. 71, Oct. 2021.
  6. S. M. LaValle, “Rapidly-Exploring Random Trees: A New Tool for Path Planning,” Research Report, 1998.
  7. L. Jiang, S. Liu, Y. Cui, and H. Jiang, “Path Planning for Robotic Manipulator in Complex Multi-Obstacle Environment Based on Improved__\__RRT,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 6, pp. 4774-4785, Dec. 2022.
  8. P. E. Hart, N. J. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100-107, Jul. 1968.
  9. S. Liu, K. Mohta, N. Atanasov, and V. Kumar, “Search-Based Motion Planning for Aggressive Flight in SE(3),” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2439-2446, Jul. 2018.
  10. Z. Zhang, L. Zheng, J. Yu, Y. Li, and Z. Yu, “Three Recurrent Neural Networks and Three Numerical Methods for Solving a Repetitive Motion Planning Scheme of Redundant Robot Manipulators,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 3, pp. 1423-1434, Jun. 2017.
  11. Y. Hu, and S. X. Yang, “A Knowledge Based Genetic Algorithm for Path Planning of A Mobile Robot,” in IEEE International Conference on Robotics and Automation, ICRA, New Orleans, LA, USA, 2004, pp. 4350-4355, vol. 5.
  12. X. Zhang, A. Liniger, and F. Borrelli, “Optimization-Based Collision Avoidance,” IEEE Transactions on Control Systems Technology, vol. 29, no. 3, pp. 972-983, May 2021.
  13. O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” The International Journal of Robotics Research, vol. 5, no. 1, pp. 90-98, Mar. 1986.
  14. Y. Tian, X. Zhu, D. Meng, X. Wang, and B. Liang, “An Overall Configuration Planning Method of Continuum Hyper-Redundant Manipulators Based on Improved Artificial Potential Field Method,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4867-4874, Jul. 2021.
  15. T. Zhu, J. Mao, L. Han, C. Zhang, and J. Yang, “Real-Time Dynamic Obstacle Avoidance for Robot Manipulators Based on Cascaded Nonlinear MPC With Artificial Potential Field,” IEEE Transactions on Industrial Electronics, doi: 10.1109/TIE.2023.3306405.
  16. A. Hentout, A. Maoudj, and M. Aouache, “A Review of the Literature on Fuzzy-Logic Approaches for Collision-Free Path Planning of Manipulator Robots,” Artificial Intelligence Review, vol. 56, no. 4, pp. 3369-3444, Sept. 2023.
  17. E. A. Merchan-Cruz, and A. S. Morris, “Fuzzy-GA-Based Trajectory Planner for Robot Manipulators Sharing A Common Workspace,” IEEE Transactions on Robotics, vol. 22, no. 4, pp. 613-624, Aug. 2006.
  18. B. Sangiovanni, A. Rendiniello, G. P. Incremona, A. Ferrara, and M. Piastra, “Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators,” in 2018 European Control Conference (ECC), Limassol, Cyprus, 2018, pp. 2063-2068.
  19. P. Chen, J. Pei, W. Lu, and M. Li, “A Deep Reinforcement Learning Based Method for Real-Time Path Planning and Dynamic Obstacle Avoidance,” Neurocomputing, vol. 497, pp. 64-75, May 2022.
  20. D. Guo, and Y. Zhang, “A New Inequality-Based Obstacle-Avoidance MVN Scheme and Its Application to Redundant Robot Manipulators,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 1326-1340, Nov. 2012.
  21. Y. Zhang, and J. Wang, “Obstacle Avoidance for Kinematically Redundant Manipulators Using A Dual Neural Network,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 1, pp. 752-759, Feb. 2004.
  22. Z. Xu, X. Zhou, H. Wu, X. Li, and S. Li, “Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach With Guaranteed Performance,” IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3887-3897, Apr. 2022.
  23. J. Liu, J. Yang, S. Li, and X. Wang, “Single-Loop Robust Model Predictive Speed Regulation of PMSM Based on Exogenous Signal Preview,” IEEE Transactions on Industrial Electronics, vol. 70, no. 12, pp. 12719-12729, Dec. 2023.
  24. J. Nubert, J. Kohler, V. Berenz, F. Allgower, and S. Trimpe, “Safe and Fast Tracking on A Robot Manipulator: Robust MPC and Neural Network Control,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3050-3057, Apr. 2020.
  25. A. Zube, “Cartesian Nonlinear Model Predictive Control of Redundant Manipulators Considering Obstacles,” in 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 2015, pp. 137-142.
  26. A. S. Sathya, J. Gillis, G. Pipeleers, and J. Swevers, “Real-time Robot Arm Motion Planning and Control With Nonlinear Model Predictive Control Using Augmented Lagrangian on A First-Order Solver,” in 2020 European Control Conference (ECC), St. Petersburg, Russia, 2020, pp. 507-512.
  27. M. Rubagotti, B. Sangiovanni, A. Nurbayeva, G. P. Incremona, A. Ferrara, and A. Shintemirov, “Shared Control of Robot Manipulators With Obstacle Avoidance: A Deep Reinforcement Learning Approach,” IEEE Control Systems Magazine, vol. 43, no. 1, pp. 44-63, Feb. 2023.
  28. J. Zeng, B. Zhang, and K. Sreenath, “Safety-Critical Model Predictive Control With Discrete-Time Control Barrier Function,” in 2021 American Control Conference (ACC), New Orleans, LA, USA, 2021, pp. 3882-3889.
  29. 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.
  30. A. Agrawal, and K. Sreenath, “Discrete Control Barrier Functions for Safety-Critical Control of Discrete Systems With Application to Bipedal Robot Navigation,” Robotics: Science and Systems, vol. 13, 2017.
  31. Y. Xiong, D. Zhai, M. Tavakoli, and Y. Xia, “Discrete-Time Control Barrier Function: High-Order Case and Adaptive Case,” IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3231-3239, May 2023.
  32. A. Singletary, S. Kolathaya, and A. D. Ames, “Safety-Critical Kinematic Control of Robotic Systems,” IEEE Control Systems Letters, vol. 6, pp. 139-144, Jan. 2021.
  33. M. A. Murtaza, S. Aguilera, M. Waqas, and S. Hutchinson, “Safety Compliant Control for Robotic Manipulator With Task and Input Constraints,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10659-10664, Oct. 2022.
  34. Y. Tang, X. Chu, J. Huang, and K. W. Samuel Au, “Learning-Based MPC With Safety Filter for Constrained Deformable Linear Object Manipulation,” IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2877-2884, Mar. 2024.
  35. J. Zeng, Z. Li, and K. Sreenath, “Enhancing Feasibility and Safety of Nonlinear Model Predictive Control With Discrete-Time Control Barrier Functions,” in 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, 2021, pp. 6137-6144.
  36. J. Zeng, B. Zhang, Z. Li, and K. Sreenath, “Safety-Critical Control Using Optimal-Decay Control Barrier Function With Guaranteed Point-Wise Feasibility,” in 2021 American Control Conference (ACC), New Orleans, LA, USA, 2021, pp. 3856-3863.
  37. Z. Lu, K. Feng, J. Xu, H. Chen, and Y. Lou, “Robot Safe Planning in Dynamic Environments Based on Model Predictive Control Using Control Barrier Function.” arXiv preprint arXiv:2404.05952, 2024.
  38. R. Periotto, M. Ferizbegovic, F. S. Barbosa, and R. C. Sundin, “MPC-CBF With Adaptive Safety Margins for Safety-Critical Teleoperation over Imperfect Network Connections,” arXiv preprint arXiv:2403.18650, 2024.
Citations (1)
View on Semantic Scholar

Summary

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

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