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Motion planning (MP) is one of the core robotics problems requiring fast methods for finding a collision-free robot motion path connecting the given start and goal states.
An algorithm for planning collision-free paths among polyhedral obstacles
Tomás Lozano-Pérez and Michael A Wesley · 1979
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Automatic differentiation: Techniques and applications
Louis B Rall · 1981
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Newton’s method with a model trust region modification
Danny C Sorensen · 1982
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Viscosity solutions of hamilton-jacobi equations
Michael G Crandall and Pierre-Louis Lions · 1983
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The potential field approach and operational space formulation in robot control
Oussama Khatib · 1986
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A fast marching level set method for monotonically advancing fronts
James A Sethian · 1996
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Analysis of probabilistic roadmaps for path planning
Lydia E Kavraki, Mihail N Kolountzakis, and J-C Latombe · 1998
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Path planning using lazy prm
Robert Bohlin and Lydia E Kavraki · 2000
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RRT-connect: An efficient approach to single-query path planning
James J Kuffner and Steven M LaValle · 2000
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Rapidly-exploring random trees: Progress and prospects
Steven M LaValle, James J Kuffner, BR Donald, et al · 2001
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Introduction to numerical continuation methods
Eugene L Allgower and Kurt Georg · 2003
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Sampling-based algorithms for optimal motion planning
Sertac Karaman and Emilio Frazzoli · 2011
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The path to efficiency: Fast marching method for safer, more efficient mobile robot trajectories
Alberto Valero-Gomez, Javier V Gomez, Santiago Garrido, and Luis Moreno · 2013
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Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
Jonathan D Gammell, Siddhartha S Srinivasa, and Timothy D Barfoot · 2014
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Lazy collision checking in asymptotically-optimal motion planning
Kris Hauser · 2015
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Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions
Lucas Janson, Edward Schmerling, Ashley Clark, and Marco Pavone · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Potential functions based sampling heuristic for optimal path planning
Motion planning networks
Ahmed H Qureshi, Anthony Simeonov, Mayur J Bency, and Michael C Yip · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Motion planning networks: Bridging the gap between learning-based and classical motion planners
Ahmed Hussain Qureshi, Yinglong Miao, Anthony Simeonov, and Michael C Yip · 2020
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Eikonet: Solving the eikonal equation with deep neural networks
Jonathan D Smith, Kamyar Azizzadenesheli, and Zachary E Ross · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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Ahmed Hussain Qureshi and Yasar Ayaz · 2016
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Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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A fast marching algorithm for the factored eikonal equation
Eran Treister and Eldad Haber · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Learning sampling distributions for robot motion planning
Brian Ichter, James Harrison, and Marco Pavone · 2018
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Deeply informed neural sampling for robot motion planning
Ahmed H Qureshi and Michael C Yip · 2018
Cited alongside, same era.
Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments
Zaid Tahir, Ahmed H Qureshi, Yasar Ayaz, and Raheel Nawaz · 2018
Cited alongside, same era.
Differentiable spatial planning using transformers
Devendra Singh Chaplot, Deepak Pathak, and Jitendra Malik · 2021
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Cost-to-go function generating networks for high dimensional motion planning
Jinwook Huh, Volkan Isler, and Daniel D Lee · 2021
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Characterizing possible failure modes in physics-informed neural networks
Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, and Michael W Mahoney · 2021
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Phase transitions, distance functions, and implicit neural representations
Yaron Lipman · 2021
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A note on two problems in connexion with graphs
Edsger W Dijkstra · 2022
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Learning continuous environment fields via implicit functions
Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Ming-Hsuan Yang, and Jan Kautz · 2022
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Visco grids: Surface reconstruction with viscosity and coarea grids
Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali Thabet, and Yaron Lipman · 2022
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NTFields: Neural time fields for physics-informed robot motion planning
Ruiqi Ni and Ahmed H Qureshi · 2023
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