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We present LQR-CBF-RRT*, an incremental sampling-based algorithm for offline motion planning.
J. Van Den Berg, “Iterated lqr smoothing for locally-optimal feedback control of systems with non-linear dynamics and non-quadratic cost,” in 2014 American control conference . IEEE, 2014, pp. 1912–1918
1918
Earlier work this paper cites.
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE transactions on Robotics and Automation , vol. 12, no. 4, pp. 566–580, 1996
1996
Earlier work this paper cites.
S. M. LaValle et al. , “Rapidly-exploring random trees: A new tool for path planning,” 1998
1998
Earlier work this paper cites.
R. Rubinstein, “The Cross-Entropy Method for Combinatorial and Continuous Optimization,” Methodology And Computing In Applied Probability , vol. 1, no. 2, pp. 127–190, 1999
1999
Earlier work this paper cites.
H. K. Khalil, “Nonlinear systems third edition,” Patience Hall , vol. 115, 2002
2002
Earlier work this paper cites.
A. Shkolnik, M. Walter, and R. Tedrake, “Reachability-guided sampling for planning under differential constraints,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 2859–2865
2009
Earlier work this paper cites.
R. Tedrake, I. R. Manchester, M. Tobenkin, and J. W. Roberts, “Lqr-trees: Feedback motion planning via sums-of-squares verification,” The International Journal of Robotics Research , vol. 29, no. 8, pp. 1038–1052, 2010
2010
Earlier work this paper cites.
E. Glassman and R. Tedrake, “A quadratic regulator-based heuristic for rapidly exploring state space,” in 2010 IEEE International Conference on Robotics and Automation . IEEE, 2010, pp. 5021–5028
2010
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Optimal kinodynamic motion planning using incremental sampling-based methods,” in 49th IEEE conference on decision and control (CDC) . IEEE, 2010, pp. 7681–7687
2010
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The international journal of robotics research , vol. 30, no. 7, pp. 846–894, 2011
2011
Earlier work this paper cites.
F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan, “Rrt*-smart: Rapid convergence implementation of rrt* towards optimal solution,” in 2012 IEEE international conference on mechatronics and automation . IEEE, 2012, pp. 1651–1656
2012
Earlier work this paper cites.
M. Kobilarov, “Cross-entropy motion planning,” The International Journal of Robotics Research , vol. 31, no. 7, pp. 855–871, 2012
2012
Earlier work this paper cites.
A. Perez, R. Platt, G. Konidaris, L. Kaelbling, and T. Lozano-Perez, “Lqr-rrt*: Optimal sampling-based motion planning with automatically derived extension heuristics,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 2537–2542
2012
Earlier work this paper cites.
D. Bertsekas, Dynamic programming and optimal control: Volume I . Athena scientific, 2012, vol. 4
2012
Earlier work this paper cites.
Y. Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 4906–4913
2012
Earlier work this paper cites.
M. Kobilarov, “Cross-entropy motion planning,” International Journal of Robotics Research , vol. 31, no. 7, pp. 855–871, 2012
2012
Cited alongside, same era.
D. J. Webb and J. Van Den Berg, “Kinodynamic rrt*: Asymptotically optimal motion planning for robots with linear dynamics,” in 2013 IEEE international conference on robotics and automation . IEEE, 2013, pp. 5054–5061
2013
Cited alongside, same era.
J. Bialkowski, S. Karaman, M. Otte, and E. Frazzoli, “Efficient collision checking in sampling-based motion planning,” in Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics . Springer, 2013, pp. 365–380
2013
Cited alongside, same era.
L. M. Argentim, W. C. Rezende, P. E. Santos, and R. A. Aguiar, “Pid, lqr and lqr-pid on a quadcopter platform,” in 2013 International Conference on Informatics, Electronics and Vision (ICIEV) . IEEE, 2013, pp. 1–6
2013
Cited alongside, same era.
E. Okyere, A. Bousbaine, G. T. Poyi, A. K. Joseph, and J. M. Andrade, “Lqr controller design for quad-rotor helicopters,” The Journal of Engineering , vol. 2019, no. 17, pp. 4003–4007, 2019
2019
Later among the works it cites.
J. Chen, W. Zhan, and M. Tomizuka, “Autonomous driving motion planning with constrained iterative lqr,” IEEE Transactions on Intelligent Vehicles , vol. 4, no. 2, pp. 244–254, 2019
2019
Later among the works it cites.
W. Xiao and C. Belta, “Control barrier functions for systems with high relative degree,” in Proc. of 58th IEEE Conference on Decision and Control , Nice, France, 2019, pp. 474–479
2019
Later among the works it cites.
M. Kleinbort, K. Solovey, Z. Littlefield, K. E. Bekris, and D. Halperin, “Probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. x–xvi, 2019
2019
Later among the works it cites.
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E. F. Camacho and C. B. Alba, Model predictive control . Springer science & business media, 2013
2013
Cited alongside, same era.
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Informed rrt: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2014, pp. 2997–3004
2014
Cited alongside, same era.
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 . IEEE, 2014, pp. 6271–6278
2014
Cited alongside, same era.
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
2015
Cited alongside, same era.
J. Pan and D. Manocha, “Fast probabilistic collision checking for sampling-based motion planning using locality-sensitive hashing,” The International Journal of Robotics Research , vol. 35, no. 12, pp. 1477–1496, 2016
2016
Cited alongside, same era.
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
2016
Cited alongside, same era.
W. Sun, J. van den Berg, and R. Alterovitz, “Stochastic extended lqr for optimization-based motion planning under uncertainty,” IEEE Transactions on Automation Science and Engineering , vol. 13, no. 2, pp. 437–447, 2016
2016
Cited alongside, same era.
Y. Li, Z. Littlefield, and K. E. Bekris, “Asymptotically optimal sampling-based kinodynamic planning,” The International Journal of Robotics Research , vol. 35, no. 5, pp. 528–564, 2016
2016
Cited alongside, same era.
Y. Li, W. Wei, Y. Gao, D. Wang, and Z. Fan, “Pq-rrt*: An improved path planning algorithm for mobile robots,” Expert systems with applications , vol. 152, p. 113425, 2020
2020
Later among the works it cites.
R. Cheng, M. J. Khojasteh, A. D. Ames, and J. W. Burdick, “Safe multi-agent interaction through robust control barrier functions with learned uncertainties,” in 2020 59th IEEE Conference on Decision and Control (CDC) . IEEE, 2020, pp. 777–783
2020
Later among the works it cites.
2020
Later among the works it cites.
K. Solovey, L. Janson, E. Schmerling, E. Frazzoli, and M. Pavone, “Revisiting the asymptotic optimality of rrt,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2189–2195
2020
Later among the works it cites.
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) . IEEE, 2021, pp. 2122–2128
2021
Later among the works it cites.
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) . IEEE, 2021, pp. 3856–3863
2021
Later among the works it cites.
A. Shankar, S. Elbaum, and C. Detweiler, “Freyja: A full multirotor system for agile & precise outdoor flights,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 217–223
2021
Later among the works it cites.
A. Ahmad, C. Belta, and R. Tron, “Adaptive sampling-based motion planning with control barrier functions,” in 2022 IEEE 61st Conference on Decision and Control (CDC) . IEEE, 2022, pp. 4513–4518
2022
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2022
Later among the works it cites.
J. Blumenkamp, S. Morad, J. Gielis, Q. Li, and A. Prorok, “A framework for real-world multi-robot systems running decentralized gnn-based policies,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 8772–8778
2022
Later among the works it cites.
M. Cai, E. Aasi, C. Belta, and C.-I. Vasile, “Overcoming exploration: Deep reinforcement learning for continuous control in cluttered environments from temporal logic specifications,” IEEE Robotics and Automation Letters , vol. 8, no. 4, pp. 2158–2165, 2023
2023
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