A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

[1]  Emilio Frazzoli,et al.  RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning , 2016, Int. J. Robotics Res..

[2]  Riccardo Bonalli,et al.  Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Yasar Ayaz,et al.  RRT*-SMART: A Rapid Convergence Implementation of RRT* , 2013 .

[4]  Emilio Frazzoli,et al.  Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games , 2010, WAFR.

[5]  Karl Berntorp,et al.  Sampling-based algorithms for optimal motion planning using closed-loop prediction , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning using process algebra specifications , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Raghvendra V. Cowlagi,et al.  Randomized sampling-based trajectory optimization for UAVs to satisfy linear temporal logic specifications , 2020 .

[8]  Jiankun Wang,et al.  Optimal Path Planning Using Generalized Voronoi Graph and Multiple Potential Functions , 2020, IEEE Transactions on Industrial Electronics.

[9]  Dan Halperin,et al.  New perspective on sampling-based motion planning via random geometric graphs , 2016, Robotics: Science and Systems.

[10]  Marcelo H. Ang,et al.  Obstacle-guided informed planning towards robot navigation in cluttered environments , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[11]  Brendan Englot,et al.  Belief roadmap search: Advances in optimal and efficient planning under uncertainty , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[12]  Debasish Ghose,et al.  Probabilistic analysis of sampling based path planning algorithms , 2013, 2013 IEEE International Symposium on Intelligent Control (ISIC).

[13]  Jur P. van den Berg,et al.  Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  Jonathan D. Gammell,et al.  Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Emilio Frazzoli,et al.  Revisiting the Asymptotic Optimality of RRT* , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Jonathan D. Gammell,et al.  Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Panagiotis Tsiotras,et al.  Non-Parametric Informed Exploration for Sampling-Based Motion Planning , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[18]  Emilio Frazzoli,et al.  An incremental sampling-based algorithm for stochastic optimal control , 2012, 2012 IEEE International Conference on Robotics and Automation.

[19]  Emilio Frazzoli,et al.  Sampling-based algorithms for continuous-time POMDPs , 2013, 2013 American Control Conference.

[20]  Claire J. Tomlin,et al.  Cost-Aware Path Planning Under Co-Safe Temporal Logic Specifications , 2017, IEEE Robotics and Automation Letters.

[21]  Alan Kuntz,et al.  Fast Anytime Motion Planning in Point Clouds by Interleaving Sampling and Interior Point Optimization , 2017, ISRR.

[22]  Lydia E. Kavraki,et al.  Exploring implicit spaces for constrained sampling-based planning , 2019, Int. J. Robotics Res..

[23]  Léonard Jaillet,et al.  Efficient asymptotically-optimal path planning on manifolds , 2013, Robotics Auton. Syst..

[24]  Lucia Pallottino,et al.  Motion primitive based random planning for loco-manipulation tasks , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

[25]  Csaba Szepesvári,et al.  Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Kostas E. Bekris,et al.  Efficient and Asymptotically Optimal Kinodynamic Motion Planning via Dominance-Informed Regions , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[27]  Siddhartha S. Srinivasa,et al.  Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search , 2017, Int. J. Robotics Res..

[28]  Kostas E. Bekris,et al.  Informed Asymptotically Near-Optimal Planning for Field Robots with Dynamics , 2017, FSR.

[29]  Fabio Tozeto Ramos,et al.  Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[30]  Leslie Pack Kaelbling,et al.  LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics , 2012, 2012 IEEE International Conference on Robotics and Automation.

[31]  Bin Liang,et al.  A Path Optimization Algorithm for Motion Planning with the Moving Target , 2018, 2018 IEEE International Conference on Mechatronics and Automation (ICMA).

[32]  Nicholas Roy,et al.  Asymptotically Optimal Planning under Piecewise-Analytic Constraints , 2016, WAFR.

[33]  Jae-Bok Song,et al.  Informed RRT* with improved converging rate by adopting wrapping procedure , 2018, Intell. Serv. Robotics.

[34]  Marco Pavone,et al.  An asymptotically-optimal sampling-based algorithm for Bi-directional motion planning , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[35]  Dan Halperin,et al.  Asymptotically-optimal Motion Planning using lower bounds on cost , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[36]  Alan Kuntz,et al.  Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search , 2019, Robotics: Science and Systems.

[37]  Robert Fitch,et al.  Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover , 2019, J. Field Robotics.

[38]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning with deterministic μ-calculus specifications , 2012, 2012 American Control Conference (ACC).

[39]  Anthony Stentz,et al.  Anytime RRTs , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Emilio Frazzoli,et al.  A martingale approach and time-consistent sampling-based algorithms for risk management in stochastic optimal control , 2013, 53rd IEEE Conference on Decision and Control.

[41]  Emilio Frazzoli,et al.  Anytime computation of time-optimal off-road vehicle maneuvers using the RRT* , 2011, IEEE Conference on Decision and Control and European Control Conference.

[42]  Donghyuk Kim,et al.  Simultaneous planning of sampling and optimization: study on lazy evaluation and configuration free space approximation for optimal motion planning algorithm , 2019, Autonomous Robots.

[43]  Marco Pavone,et al.  Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions , 2013, ISRR.

[44]  Songhwai Oh,et al.  Robust multi-layered sampling-based path planning for temporal logic-based missions , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[45]  Robert Platt,et al.  Optimal sampling-based planning for linear-quadratic kinodynamic systems , 2013, 2013 IEEE International Conference on Robotics and Automation.

[46]  Marin Kobilarov,et al.  Cross-entropy motion planning , 2012, Int. J. Robotics Res..

[47]  Wolfram Burgard,et al.  BI2RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[48]  Kostas E. Bekris,et al.  Asymptotically Near-Optimal Planning With Probabilistic Roadmap Spanners , 2013, IEEE Transactions on Robotics.

[49]  Nikolaus Correll,et al.  C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup , 2013, IEEE Transactions on Robotics.

[50]  Kostas E. Bekris,et al.  The Importance of a Suitable Distance Function in Belief-Space Planning , 2015, ISRR.

[51]  Ron Alterovitz,et al.  Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision , 2013, ISRR.

[52]  Han-Lim Choi,et al.  A successive approximation-based approach for optimal kinodynamic motion planning with nonlinear differential constraints , 2013, 52nd IEEE Conference on Decision and Control.

[53]  Marco Pavone,et al.  Optimal sampling-based motion planning under differential constraints: The driftless case , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[54]  Michael C. Yip,et al.  Deeply Informed Neural Sampling for Robot Motion Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[55]  Rüdiger Dillmann,et al.  RRT∗-Connect: Faster, asymptotically optimal motion planning , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[56]  Baris Akgün,et al.  Sampling heuristics for optimal motion planning in high dimensions , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[57]  Leonardo Bobadilla,et al.  Sampling-based planning algorithms for multi-objective missions , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[58]  Jong-Hwan Kim,et al.  Fast-BIT∗: Modified heuristic for sampling-based optimal planning with a faster first solution and convergence in implicit random geometric graphs , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[59]  Kostas E. Bekris,et al.  A study on the finite-time near-optimality properties of sampling-based motion planners , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[60]  Kostas E. Bekris,et al.  dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning , 2019, Autonomous Robots.

[61]  Andrea Lockerd Thomaz,et al.  Hierarchical rejection sampling for informed kinodynamic planning in high-dimensional spaces , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[62]  Kostas E. Bekris,et al.  Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs , 2019, 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).

[63]  George V. Moustakides,et al.  Geometric probability results for bounding path quality in sampling-based roadmaps after finite computation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[64]  Marco Pavone,et al.  Deterministic sampling-based motion planning: Optimality, complexity, and performance , 2015, ISRR.

[65]  Emilio Frazzoli,et al.  Sampling-based algorithm for filtering using Markov chain approximations , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[66]  Siddhartha S. Srinivasa,et al.  Generalizing Informed Sampling for Asymptotically-Optimal Sampling-Based Kinodynamic Planning via Markov Chain Monte Carlo , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[67]  Rajeev Motwani,et al.  Path planning in expansive configuration spaces , 1997, Proceedings of International Conference on Robotics and Automation.

[68]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[69]  Geoffrey A. Hollinger,et al.  Sampling-based robotic information gathering algorithms , 2014, Int. J. Robotics Res..

[70]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[71]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[72]  H. Jin Kim,et al.  Planning and Control for Collision-Free Cooperative Aerial Transportation , 2018, IEEE Transactions on Automation Science and Engineering.

[73]  Wheeler Ruml,et al.  Abstraction-Guided Sampling for Motion Planning , 2012, SOCS.

[74]  Daniel D. Lee,et al.  Learning Implicit Sampling Distributions for Motion Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[75]  Emilio Frazzoli,et al.  Optimal sampling-based Feedback Motion Trees among obstacles for controllable linear systems with linear constraints , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[76]  Dan Halperin,et al.  Asymptotically near-optimal RRT for fast, high-quality, motion planning , 2013, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[77]  Siddhartha S. Srinivasa,et al.  LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[78]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[79]  Michael A. Goodrich,et al.  MORRF*: Sampling-Based Multi-Objective Motion Planning , 2015, IJCAI.

[80]  Jongwoo Lim,et al.  Anytime RRBT for handling uncertainty and dynamic objects , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[81]  Jonathan P. How,et al.  An optimizing sampling-based motion planner with guaranteed robustness to bounded uncertainty , 2014, 2014 American Control Conference.

[82]  Yasar Ayaz,et al.  Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments , 2015, Robotics Auton. Syst..

[83]  Kostas E. Bekris,et al.  Pushing the Boundaries of Asymptotic Optimality for Sampling-based Roadmaps In Motion And Task Planning , 2019, ArXiv.

[84]  Marco Pavone,et al.  Learning Sampling Distributions for Robot Motion Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[85]  Kostas E. Bekris,et al.  Sparse roadmap spanners for asymptotically near-optimal motion planning , 2014, Int. J. Robotics Res..

[86]  Yue Zhang,et al.  An approach to speed up RRT , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[87]  Vasumathi Raman,et al.  Sampling-based synthesis of maximally-satisfying controllers for temporal logic specifications , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[88]  Emilio Frazzoli,et al.  Free-configuration biased sampling for motion planning , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[89]  Donghyuk Kim,et al.  Harmonious Sampling for Mobile Manipulation Planning , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[90]  Inna Sharf,et al.  Sampling-based A* algorithm for robot path-planning , 2014, Int. J. Robotics Res..

[91]  Alejandro Perez,et al.  Optimal Bidirectional Rapidly-Exploring Random Trees , 2013 .

[92]  Emilio Frazzoli,et al.  Verification and Synthesis of Admissible Heuristics for Kinodynamic Motion Planning , 2017, IEEE Robotics and Automation Letters.

[93]  Panagiotis Tsiotras,et al.  Incremental sampling-based motion planners using policy iteration methods , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[94]  Michael Meurer,et al.  Dispertio: Optimal Sampling For Safe Deterministic Motion Planning , 2020, IEEE Robotics and Automation Letters.

[95]  Fabio Ramos,et al.  Bayesian Local Sampling-Based Planning , 2020, IEEE Robotics and Automation Letters.

[96]  Kostas E. Bekris,et al.  Improving the scalability of asymptotically optimal motion planning for humanoid dual-arm manipulators , 2017, 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids).

[97]  Brian Paden,et al.  The Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning , 2016, WAFR.

[98]  Emilio Frazzoli,et al.  Optimal kinodynamic motion planning using incremental sampling-based methods , 2010, 49th IEEE Conference on Decision and Control (CDC).

[99]  Emilio Frazzoli,et al.  Probabilistically-sound and asymptotically-optimal algorithm for stochastic control with trajectory constraints , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[100]  Wolfram Burgard,et al.  Optimal, sampling-based manipulation planning , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[101]  Shunli Li,et al.  Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment , 2019, Autonomous Robots.

[102]  Songhwai Oh,et al.  Fast Sampling-Based Cost-Aware Path Planning With Nonmyopic Extensions Using Cross Entropy , 2017, IEEE Transactions on Robotics.

[103]  Emilio Frazzoli,et al.  Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[104]  Kris Hauser,et al.  Asymptotically Optimal Planning by Feasible Kinodynamic Planning in a State–Cost Space , 2015, IEEE Transactions on Robotics.

[105]  Sonia Martínez,et al.  Optimal kinodynamic motion planning in environments with unexpected obstacles , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[106]  Minghui Zhu,et al.  Robust adaptive motion planning in the presence of dynamic obstacles , 2016, 2016 American Control Conference (ACC).

[107]  Pieter Abbeel,et al.  Toward asymptotically optimal motion planning for kinodynamic systems using a two-point boundary value problem solver , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[108]  Marco Pavone,et al.  Optimal sampling-based motion planning under differential constraints: The drift case with linear affine dynamics , 2014, 2015 54th IEEE Conference on Decision and Control (CDC).

[109]  Sertac Karaman,et al.  Perception-Driven Sparse Graphs for Optimal Motion Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[110]  Donghyuk Kim,et al.  Volumetric Tree*: Adaptive Sparse Graph for Effective Exploration of Homotopy Classes , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[111]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[112]  Emilio Frazzoli,et al.  Asymptotically optimal feedback planning using a numerical Hamilton-Jacobi-Bellman solver and an adaptive mesh refinement , 2016, Int. J. Robotics Res..

[113]  Lydia E. Kavraki,et al.  Anytime solution optimization for sampling-based motion planning , 2013, 2013 IEEE International Conference on Robotics and Automation.

[114]  Jur P. van den Berg,et al.  Robust belief space planning under intermittent sensing via a maximum eigenvalue-based bound , 2016, Int. J. Robotics Res..

[115]  Marco Pavone,et al.  Sample Complexity of Probabilistic Roadmaps via ε-nets , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[116]  Emilio Frazzoli,et al.  Anytime Motion Planning using the RRT* , 2011, 2011 IEEE International Conference on Robotics and Automation.

[117]  Minjun Peng,et al.  DL-RRT* algorithm for least dose path Re-planning in dynamic radioactive environments , 2019, Nuclear Engineering and Technology.

[118]  Panagiotis Tsiotras,et al.  Use of relaxation methods in sampling-based algorithms for optimal motion planning , 2013, 2013 IEEE International Conference on Robotics and Automation.

[119]  Emilio Frazzoli,et al.  Sampling-based optimal motion planning for non-holonomic dynamical systems , 2013, 2013 IEEE International Conference on Robotics and Automation.

[120]  Clément Gosselin,et al.  Dynamic Point-To-Point Trajectory Planning for Three Degrees-of-Freedom Cable-Suspended Parallel Robots Using Rapidly Exploring Random Tree Search , 2020 .

[121]  Donghyuk Kim,et al.  Kinodynamic Comfort Trajectory Planning for Car-Like Robots , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[122]  Jonathan P. How,et al.  Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees , 2013 .

[123]  Alin Albu-Schäffer,et al.  Repetition sampling for efficiently planning similar constrained manipulation tasks , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[124]  Marco Pavone,et al.  Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies , 2018, Robotics: Science and Systems.

[125]  Kris Hauser,et al.  Lazy collision checking in asymptotically-optimal motion planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[126]  Dirk Schulz,et al.  Hierarchical rough terrain motion planning using an optimal sampling-based method , 2013, 2013 IEEE International Conference on Robotics and Automation.

[127]  Emilio Frazzoli,et al.  Optimal motion planning with the half-car dynamical model for autonomous high-speed driving , 2013, 2013 American Control Conference.

[128]  Gamini Dissanayake,et al.  Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring , 2016, Int. J. Robotics Res..

[129]  Donghyuk Kim,et al.  Adaptive Lazy Collision Checking for Optimal Sampling-based Motion Planning , 2018, 2018 15th International Conference on Ubiquitous Robots (UR).

[130]  Siddhartha S. Srinivasa,et al.  Informed Sampling for Asymptotically Optimal Path Planning , 2018, IEEE Transactions on Robotics.

[131]  Robert Fitch,et al.  Distance and Steering Heuristics for Streamline-Based Flow Field Planning , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[132]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[133]  Kostas E. Bekris,et al.  Asymptotically optimal sampling-based kinodynamic planning , 2014, Int. J. Robotics Res..

[134]  Kai Oliver Arras,et al.  Kinodynamic motion planning on Gaussian mixture fields , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[135]  Junghwan Lee,et al.  Cloud RRT∗: Sampling Cloud based RRT∗ , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[136]  Panagiotis Tsiotras,et al.  Dynamic programming guided exploration for sampling-based motion planning algorithms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[137]  Gianni Ferretti,et al.  Sampling-based optimal kinodynamic planning with motion primitives , 2018, Autonomous Robots.

[138]  Emilio Frazzoli,et al.  Incremental sampling-based algorithm for minimum-violation motion planning , 2013, 52nd IEEE Conference on Decision and Control.

[139]  Lars-Peter Ellekilde,et al.  Kernel density estimation based self-learning sampling strategy for motion planning of repetitive tasks , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[140]  Didier Devaurs,et al.  Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms , 2016, IEEE Transactions on Automation Science and Engineering.

[141]  Lydia E. Kavraki,et al.  Analysis of probabilistic roadmaps for path planning , 1998, IEEE Trans. Robotics Autom..

[142]  Panagiotis Tsiotras,et al.  Machine learning guided exploration for sampling-based motion planning algorithms , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[143]  Ron Alterovitz,et al.  Rapidly-exploring roadmaps: Weighing exploration vs. refinement in optimal motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[144]  Benjamin Kuipers,et al.  Feedback motion planning via non-holonomic RRT* for mobile robots , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[145]  Nicholas Roy,et al.  Rapidly-exploring Random Belief Trees for motion planning under uncertainty , 2011, 2011 IEEE International Conference on Robotics and Automation.

[146]  Devin J. Balkcom,et al.  A fast streaming spanner algorithm for incrementally constructing sparse roadmaps , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[147]  Panagiotis Tsiotras,et al.  Deformable Rapidly-Exploring Random Trees , 2017, Robotics: Science and Systems.

[148]  Yasar Ayaz,et al.  Potential functions based sampling heuristic for optimal path planning , 2015, Autonomous Robots.

[149]  Kiril Solovey,et al.  The Critical Radius in Sampling-Based Motion Planning , 2018, Robotics: Science and Systems.

[150]  Siddhartha S. Srinivasa,et al.  Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).