Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 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

Safe Navigation using Density Functions (2306.15830v2)

Published 27 Jun 2023 in cs.RO

Abstract: This paper presents a novel approach for safe control synthesis using the dual formulation of the navigation problem. The main contribution of this paper is in the analytical construction of density functions for almost everywhere navigation with safety constraints. In contrast to the existing approaches, where density functions are used for the analysis of navigation problems, we use density functions for the synthesis of safe controllers. We provide convergence proof using the proposed density functions for navigation with safety. Further, we use these density functions to design feedback controllers capable of navigating in cluttered environments and high-dimensional configuration spaces. The proposed analytical construction of density functions overcomes the problem associated with navigation functions, which are known to exist but challenging to construct, and potential functions, which suffer from local minima. Application of the developed framework is demonstrated on simple integrator dynamics and fully actuated robotic systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (28)
  1. Steven M LaValle. Planning algorithms. Cambridge university press, 2006.
  2. Rapidly-exploring random trees: Progress and prospects: Steven m. lavalle, iowa state university, a james j. kuffner, jr., university of tokyo, tokyo, japan. Algorithmic and Computational Robotics, pages 303–307, 2001.
  3. A randomized roadmap method for path and manipulation planning. In Proceedings of IEEE international conference on robotics and automation, volume 1, pages 113–120. IEEE, 1996.
  4. Sampling-based algorithms for optimal motion planning. The international journal of robotics research, 30(7):846–894, 2011.
  5. Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions. The International journal of robotics research, 34(7):883–921, 2015.
  6. Batch informed trees (bit): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. In 2015 IEEE international conference on robotics and automation (ICRA), pages 3067–3074. IEEE, 2015.
  7. A mathematical introduction to robotic manipulation. CRC press, 2017.
  8. Control barrier functions: Theory and applications. In 2019 18th European control conference (ECC), pages 3420–3431. IEEE, 2019.
  9. Control barrier function based quadratic programs for safety critical systems. IEEE Transactions on Automatic Control, 62(8):3861–3876, 2016.
  10. Dynamic locomotion in the mit cheetah 3 through convex model-predictive control. In 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 1–9. IEEE, 2018.
  11. Onboard safety guarantees for racing drones: High-speed geofencing with control barrier functions. IEEE Robotics and Automation Letters, 7(2):2897–2904, 2022.
  12. Oussama Khatib. Real-time obstacle avoidance for manipulators and mobile robots. In Autonomous robot vehicles, pages 396–404. Springer, 1986.
  13. Bruce Krogh. A generalized potential field approach to obstacle avoidance control. In Proc. SME Conf. on Robotics Research: The Next Five Years and Beyond, Bethlehem, PA, 1984, pages 11–22, 1984.
  14. Elon Rimon. Exact robot navigation using artificial potential functions. Yale University, 1990.
  15. D. E. Koditschek. The Control of Natural Motion in Mechanical Systems. Journal of Dynamic Systems, Measurement, and Control, 113(4):547–551, 12 1991.
  16. Savvas G Loizou. Closed form navigation functions based on harmonic potentials. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, pages 6361–6366. IEEE, 2011.
  17. Navigation functions for everywhere partially sufficiently curved worlds. In 2012 IEEE International Conference on Robotics and Automation, pages 2115–2120. IEEE, 2012.
  18. Robot navigation in complex workspaces using conformal navigation transformations. IEEE Robotics and Automation Letters, 8(1):192–199, 2022.
  19. Umesh Vaidya. Optimal motion planning using navigation measure. International Journal of Control, 91(5):989–998, 2018.
  20. On analysis and synthesis of safe control laws. In 42nd Allerton Conference on Communication, Control, and Computing, 2004.
  21. Density functions for navigation-function-based systems. IEEE Transactions on Automatic Control, 53(2):612–617, 2008.
  22. An application of rantzer’s dual lyapunov theorem to decentralized formation stabilization. In 2007 European Control Conference (ECC), pages 882–888. IEEE, 2007.
  23. Data-driven optimal control of nonlinear dynamics under safety constraints. IEEE Control Systems Letters, 6:2240–2245, 2022.
  24. Convex approach to data-driven off-road navigation via linear transfer operators. IEEE Robotics and Automation Letters, pages 1–8, 2023.
  25. Anders Rantzer. A dual to Lyapunov’s stability theorem. Systems & Control Letters, 42(3):161–168, 2001.
  26. Lyapunov measure for almost everywhere stability. IEEE Transactions on Automatic Control, 53(1):307–323, 2008.
  27. Loring W Tu. Manifolds. In An Introduction to Manifolds, pages 47–83. Springer, 2011.
  28. Almost everywhere stability: Linear transfer operator approach. Journal of Mathematical analysis and applications, 368:144–156, 2010.
Citations (4)

Summary

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