Rapidly-Exploring Random Trees: Progress and Prospects

By Steven M. LaValle, James J. Kuffner Jr.. 2000

862 citations recorded by the source corpus.

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Citations in the corpus

A Survey of Embodied AI: From Simulators to Research Tasks (2021, background): “While classic navigation approaches [90] are usually composed of hand-engineered sub-components like localization, mapping [91], path planning [92], [93] and locomotion.”
Learning to Explore using Active Neural SLAM (2020, methodology): “Maps are used to compute paths to goal locations via path planning (Kavraki et al., 1996; Lavalle and Kuffner Jr, 2000; Canny, 1988).”
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models (2023, unknown): “Tree-based search, where multiple branches of outcomes are explored during search, is widely used in many planning algorithms (Świechowski et al., 2023; LaValle et al., 2001) and Reinforcement Learning (RL) (Hafner et al.”
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning (2017, background): “Sampling-based planners, such as Probabilistic Roadmaps (PRMs) [15] and Rapidly Exploring Random Trees (RRTs) [17], [19], efficiently solve this problem by approximating the topology of the configuration space (C-space), the space of all possible robot configurations.”
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments (2016, background): “Randomized algorithms such as PRM, RRT and RRT* have enjoyed success due to their simplicity and performance in high-dimensional spaces [14, 17, 12].”
CHOMP: Covariant Hamiltonian optimization for motion planning (2013, background): “Randomized approaches are understood to be probabilistically complete [50], but capable of solving many challenging problems quite efficiently [7].”
A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty (2012, unknown): “To implement this action we use an extension of the Rapidly-Exploring Random Tree (RRT) (Lavalle and Kuffner 2000) planner, namely the Constrained Bi-directional RRT planner (CBiRRT) (Berenson et al 2009a).”
Affordances from Human Videos as a Versatile Representation for Robotics (2023, background): “Knowledge of a robot body is often known, hence reaching a point in the 3D space is feasible using motion planning [51, 56, 57].”
Differentially constrained mobile robot motion planning in state lattices (2009, background): “Another type of probabilistic planning was rapidly-exploring random trees (RRT) introduced by LaValle and Kuffner (2001).”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, methodology): “One could try to get around the size of the input space by considering samplingbased planning algorithms, such as Monte-Carlo-tree-search [29, 19] or Rapid Random Trees (RRT) [105].”
Two Body Problem: Collaborative Visual Task Completion (2019, methodology): “Upon having obtained a map of the environment, planning methods [13, 44, 52] yield a sequence of actions to achieve the goal.”
Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles (2008, background): “Recognizing this mismatch, other researchers have concentrated on improving the quality of global planning, so that a global path can be easily tracked by the vehicle [13, 14, 15, 16, 17].”
Unifying Map and Landmark Based Representations for Visual Navigation (2017, background): “Many path planning meth-ods [5,16,21] assume direct access to a noiseless map, and often a noiseless robot location.”
Survivability: Measuring and ensuring path diversity (2009, background): “A discussion of this issue in the context of rapidly exploring random trees appears in [8].”