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Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states.
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2014
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H. C. Yeh, J. Denny, A. Lindsey, S. L. Thomas, and N. M. Amato, “UMAPRM: uniformly sampling the medial axis,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2014, pp. 5798–5803
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T. Kunz and M. Stilman, “Probabilistically complete kinodynamic planning for robot manipulators with acceleration limits,” in IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS) , 2014, pp. 3713–3719
2014
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J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Batch informed trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2015, pp. 3067–3074
2015
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S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” I. J. Robotics Res. , vol. 30, no. 7, pp. 846–894, 2011
2011
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B. Akgun and M. Stilman, “Sampling heuristics for optimal motion planning in high dimensions,” in IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS) . IEEE, 2011, pp. 2640–2645
2011
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A. Perez, R. Platt, G. Konidaris, L. P. Kaelbling, and T. Lozano-Pérez, “LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2012, pp. 2537–2542
2012
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H. Yeh, S. L. Thomas, D. Eppstein, and N. M. Amato, “UOBPRM: A uniformly distributed obstacle-based PRM,” in IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS) , 2012, pp. 2655–2662
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 IEEE Int. Conf. Robotics and Automation (ICRA) , 2013, pp. 5054–5061
2013
Cited alongside, same era.
D. Devaurs, T. Siméon, and J. Cortés, “Enhancing the transition-based RRT to deal with complex cost spaces,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2013, pp. 4120–4125
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 IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS) , 2014, pp. 2997–3004
2014
Cited alongside, same era.
O. Salzman and D. Halperin, “Asymptotically-optimal motion planning using lower bounds on cost,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2015, pp. 4167–4172
2015
Later among the works it cites.
C. Xie, J. P. van den Berg, S. Patil, and P. Abbeel, “Toward asymptotically optimal motion planning for kinodynamic systems using a two-point boundary value problem solver,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2015, pp. 4187–4194
2015
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T. Kunz, A. Thomaz, and H. Christensen, “Hierarchical rejection sampling for informed kinodynamic planning in high-dimensional spaces,” in IEEE Int. Conf. Robotics and Automation (ICRA) , 2016, pp. 89–96
2016
Later among the works it cites.
K. Solovey, O. Salzman, and D. Halperin, “New perspective on sampling-based motion planning via random geometric graphs,” in Robotics: Science and Systems (RSS) , 2016
2016
Later among the works it cites.
O. Salzman and D. Halperin, “Asymptotically near-optimal RRT for fast, high-quality motion planning,” IEEE Trans. Robotics , vol. 32, no. 3, pp. 473–483, 2016
2016
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S. D. Pendleton, W. Liu, H. Andersen, Y. H. Eng, E. Frazzoli, D. Rus, and M. H. Ang, “Numerical approach to reachability guided sampling-based motion planning under differential constraints,” IEEE Robotics and Automation Letters , 2017
2017
Closest in time.
Y. Abbasi-Yadkori, P. Bartlett, V. Gabillon, and A. Malek, “Hit-and-Run for Sampling and Planning in Non-Convex Spaces,” in International Conference on Artificial Intelligence and Statistics , vol. 54, 2017, pp. 888–895
2017
Closest in time.