2000 character limit reached
Continuous-Time Line-of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems (2410.22596v2)
Published 29 Oct 2024 in math.OC and cs.RO
Abstract: Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot's line-of-sight (LoS), for reliable operation. This paper tackles the challenge of maintaining LoS on such keypoints during robot movement. We propose a novel method that addresses these issues by ensuring applicability to various sensor footprints, adaptability to arbitrary nonlinear system dynamics, and constant enforcement of LoS throughout the robot's path. Our experiments show that the proposed approach achieves significantly reduced LoS violation and runtime compared to existing state-of-the-art methods in several representative and challenging scenarios.
- G. Huang, “Visual-inertial navigation: A concise review,” in 2019 Int. Conf. on Robotics and Automation (ICRA), pp. 9572–9582, IEEE, May 2019.
- D. Scaramuzza and F. Fraundorfer, “Visual odometry [tutorial],” IEEE Robot. Autom. Mag., vol. 18, pp. 80–92, Dec. 2011.
- C. Huang et al., “ACT: An autonomous drone cinematography system for action scenes,” in 2018 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 7039–7046, IEEE, May 2018.
- A. Alcántara, J. Capitán, R. Cunha, and A. Ollero, “Optimal trajectory planning for cinematography with multiple unmanned aerial vehicles,” Rob. Auton. Syst., vol. 140, p. 103778, June 2021.
- C. R. Hayner, S. C. Buckner, D. Broyles, E. Madewell, K. Leung, and B. Açikmeşe, “HALO: Hazard-aware landing optimization for autonomous systems,” in 2023 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3261–3267, IEEE, May 2023.
- J. M. Carson et al., “The SPLICE project: Continuing NASA development of GN&C technologies for safe and precise landing,” in AIAA Scitech 2019 Forum, (Reston, Virginia), American Institute of Aeronautics and Astronautics, Jan. 2019.
- A. E. Johnson and J. F. Montgomery, “Overview of terrain relative navigation approaches for precise lunar landing,” in 2008 IEEE Aerospace Conf., pp. 1–10, IEEE, Mar. 2008.
- B. Zhou, J. Pan, F. Gao, and S. Shen, “RAPTOR: Robust and perception-aware trajectory replanning for quadrotor fast flight,” IEEE Trans. Robot., vol. 37, pp. 1992–2009, Dec. 2021.
- J. Tordesillas and J. P. How, “Panther: Perception-aware trajectory planner in dynamic environments,” IEEE Access, vol. 10, pp. 22662–22677, 2022.
- I. Spasojevic, V. Murali, and S. Karaman, “Perception-aware time optimal path parameterization for quadrotors,” in 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3213–3219, IEEE, May 2020.
- V. Murali, I. Spasojevic, W. Guerra, and S. Karaman, “Perception-aware trajectory generation for aggressive quadrotor flight using differential flatness,” in 2019 American Control Conf. (ACC), pp. 3936–3943, IEEE, July 2019.
- D. Falanga, P. Foehn, P. Lu, and D. Scaramuzza, “PAMPC: Perception-aware model predictive control for quadrotors,” in 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1–8, Oct. 2018.
- T. P. Reynolds, M. Szmuk, D. Malyuta, M. Mesbahi, B. Açıkmeşe, and J. M. Carson, “Dual quaternion-based powered descent guidance with state-triggered constraints,” Journal of Guidance, Control, and Dynamics, vol. 43, no. 9, pp. 1584–1599, 2020.
- M. Kelly, “An introduction to trajectory optimization: How to do your own direct collocation,” SIAM Rev. Soc. Ind. Appl. Math., vol. 59, pp. 849–904, Jan. 2017.
- D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in 2011 IEEE Int. Conf. on Robotics and Automation, pp. 2520–2525, May 2011.
- C. Richter, A. Bry, and N. Roy, “Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments,” in Robotics Research: The 16th Int. Symposium ISRR (M. Inaba and P. Corke, eds.), pp. 649–666, Cham: Springer Int. Publishing, 2016.
- W. Ding, W. Gao, K. Wang, and S. Shen, “An efficient B-spline-based kinodynamic replanning framework for quadrotors,” IEEE Trans. Rob., vol. 35, pp. 1287–1306, Dec. 2019.
- B. Houska, H. J. Ferreau, and M. Diehl, “ACADO toolkit—an open‐source framework for automatic control and dynamic optimization,” Optim. Control Appl. Methods, vol. 32, pp. 298–312, May 2011.
- S. G. Johnson, “The NLopt nonlinear-optimization package.”
- A. Wächter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Math. Program., vol. 106, pp. 25–57, Mar. 2006.
- P. T. Boggs and J. W. Tolle, “Sequential quadratic programming,” Acta Numer., vol. 4, pp. 1–51, Jan. 1995.
- D. Malyuta et al., “Convex optimization for trajectory generation: A tutorial on generating dynamically feasible trajectories reliably and efficiently,” IEEE Control Systems Magazine, vol. 42, no. 5, pp. 40–113, 2022.
- Y. Mao, D. Dueri, M. Szmuk, and B. Açıkmeşe, “Successive convexification of non-convex optimal control problems with state constraints,” IFAC-PapersOnLine, vol. 50, pp. 4063–4069, July 2017.
- R. Bonalli, A. Cauligi, A. Bylard, and M. Pavone, “GuSTO: Guaranteed sequential trajectory optimization via sequential convex programming,” in 2019 Int. Conf. on Robotics and Automation (ICRA), pp. 6741–6747, IEEE, May 2019.
- Y. Yu, P. Elango, U. Topcu, and B. Açıkmeşe, “Proportional–integral projected gradient method for conic optimization,” Automatica, vol. 142, p. 110359, Aug. 2022.
- P. Elango, D. Luo, A. G. Kamath, S. Uzun, T. Kim, and B. Açıkmeşe, “Successive convexification for trajectory optimization with Continuous-Time constraint satisfaction,” Apr. 2024.
- M. Szmuk, Successive Convexification & High Performance Feedback Control for Agile Flight. PhD thesis, University of Washington, June 2019.
- M. Jacquet and A. Franchi, “Enforcing vision-based localization using perception constrained n-mpc for multi-rotor aerial vehicles,” in 2022 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1818–1824, 2022.
- M. Szmuk and B. Açıkmeşe, “Successive convexification for 6-DoF mars rocket powered landing with Free-Final-Time,” Feb. 2018.
- Cambridge, England: Cambridge University Press, Mar. 2004.
- A. G. Kamath et al., “Real-Time sequential conic optimization for Multi-Phase rocket landing guidance,” IFAC-PapersOnLine, vol. 56, pp. 3118–3125, Jan. 2023.
- P. J. Antsaklis and A. N. Michel, Linear systems. Secaucus, NJ: Birkhauser Boston, 1 ed., Oct. 2005.
- T. Reynolds, D. Malyuta, M. Mesbahi, B. Açıkmeşe, and J. M. Carson, “A Real-Time algorithm for Non-Convex powered descent guidance,” in AIAA Scitech 2020 Forum, AIAA SciTech Forum, American Institute of Aeronautics and Astronautics, Jan. 2020.
- D. Drusvyatskiy and A. S. Lewis, “Error bounds, quadratic growth, and linear convergence of proximal methods,” Math. Oper. Res., vol. 43, pp. 919–948, Aug. 2018.
- Y. Song, A. Romero, M. Müller, V. Koltun, and D. Scaramuzza, “Reaching the limit in autonomous racing: Optimal control versus reinforcement learning,” Sci Robot, vol. 8, p. eadg1462, Sept. 2023.
- P. Virtanen et al., “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nature Methods, vol. 17, pp. 261–272, 2020.
- A. Agrawal, R. Verschueren, S. Diamond, and S. Boyd, “A rewriting system for convex optimization problems,” Journal of Control and Decision, vol. 5, no. 1, pp. 42–60, 2018.
- S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling language for convex optimization,” Journal of Machine Learning Research, vol. 17, no. 83, pp. 1–5, 2016.
- P. J. Goulart and Y. Chen, “Clarabel: An interior-point solver for conic programs with quadratic objectives,” arXiv [math.OC], May 2024.
- J. Bradbury et al., “JAX: composable transformations of Python+NumPy programs,” 2018.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
