Deeply Informed Neural Sampling for Robot Motion Planning

S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” 1998

1998

Earlier work this paper cites.

C. Urmson and R. Simmons, “Approaches for heuristically biasing rrt growth,” in Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on , vol. 2. IEEE, 2003, pp. 1178–1183

2003

Earlier work this paper cites.

D. Ferguson and A. Stentz, “Anytime rrts,” in Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on . IEEE, 2006, pp. 5369–5375

2006

Earlier work this paper cites.

M. Rickert, O. Brock, and A. Knoll, “Balancing exploration and exploitation in motion planning,” in Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on . IEEE, 2008, pp. 2812–2817

2008

Earlier work this paper cites.

M. Zucker, J. Kuffner, and J. A. Bagnell, “Adaptive workspace biasing for sampling-based planners,” in Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on . IEEE, 2008, pp. 3757–3762

2008

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.

S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, “Contractive auto-encoders: Explicit invariance during feature extraction,” in Proceedings of the 28th International Conference on International Conference on Machine Learning . Omnipress, 2011, pp. 833–840

2011

Cited alongside, same era.

J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research , vol. 12, no. Jul, pp. 2121–2159, 2011

2011

Cited alongside, same era.

D. Berenson, P. Abbeel, and K. Goldberg, “A robot path planning framework that learns from experience,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on . IEEE, 2012, pp. 3671–3678

2012

Cited alongside, same era.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.” Journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

K. Hauser, “Lazy collision checking in asymptotically-optimal motion planning,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 2951–2957

2015

Later among the works it cites.

D. Coleman, I. A. Şucan, M. Moll, K. Okada, and N. Correll, “Experience-based planning with sparse roadmap spanners,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 900–905

2015

Later among the works it cites.

A. H. Qureshi and Y. Ayaz, “Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments,” Robotics and Autonomous Systems , vol. 68, pp. 1–11, 2015

Original

2015

Later among the works it cites.

A. H. Qureshi and Y. Ayaz, “Potential functions based sampling heuristic for optimal path planning,” Autonomous Robots , vol. 40, no. 6, pp. 1079–1093, 2016

2016

Later among the works it cites.