Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain

By Anna Yershova, Leonard Jaillet, Thierry Siméon, Steven M. LaValle. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

315 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4a4b49e74254eee506ec5e89a2353d4461faf19e

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Citations in the corpus

Sampling-Based Robot Motion Planning: A Review (2014, background): “Several planners use CC as a feedback mechanism to guide the search [72], [98], [99], [108], [115] adapt the sampling strategy [105], [106], or improve the connectivity of the environment [92], [93], [126].”
A survey on vision-based UAV navigation (2018, methodology): “The sampling-based path planning algorithm, such as the rapidly exploring random trees (RRT) proposed by Yershova et al. (2005), can keep motion path planning from failure when there is no prior information of the environment provided.”
A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning (2012, background): “, 2006) and Rapidly exploring Dense Trees (RDT) (Kuffner & LaValle, 2000; Strandbergtrees, 2004; Yershova et al., 2005; Zucker et al., 2007) are the two best known examples of sample-based planning methods.”
Path Planning for Autonomous Mobile Robots: A Review (2021, methodology): “[189] presented an improved version of RRT called Dynamic Domain RRT, which was aware of the obstacles existing in the environment during the expansion of the tree.”
Neural RRT*: Learning-Based Optimal Path Planning (2020, background): “Yershova et al. [15] present dynamic-domain RRT, which ignores the samples that are too far from the current tree.”
Real-Time Motion Planning With Applications to Autonomous Urban Driving (2009, methodology): “Several methods have been proposed to improve the efficiency [16]–[20].”
Survey of Robot 3D Path Planning Algorithms (2016, background): “Authors in [31] proposed a fast local escaping version called Dynamic Domain RRT, which will be analyzed below.”
Planning algorithms (2006, methodology): “RRTs have been used in several applications, and many variants have been developed [84, 140, 152, 202, 226, 246, 267, 364, 395, 498, 501, 502, 531, 634, 644, 645, 919, 920, 949, 979, 986].”
Sampling-Based Path Planning on Configuration-Space Costmaps (2010, background): “The problem of unbalanced refinement and exploration modes was addressed in [21] and [22] for standard RRTs.”