RRT-connect: An efficient approach to single-query path planning

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

4,060 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Robotics & Control

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Citations in the corpus

Reverse Curriculum Generation for Reinforcement Learning (2017, positive): “A promising future line of work is to combine the present automatic curriculum based on start state generation with goal generation [5], similar to classical results in planning [38].”
A Lifelong Learning Approach to Mobile Robot Navigation (2021, background): “a) Classical navigation: Classical navigation systems [5]–[8] are designed to be applicable to a wide variety of environments, but they are usually static, operating under a fixed set of prespecified hyper-parameters and therefore lacking the ability to improve with experience and adapt to a specifi”
Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, methodology): “Closely after its introduction in the literature, several theoretical guarantees provided by the RRT algorithm were shown, including probabilistic completeness [29] and exponential rate of decay of the probability of failure [35].”
Replanning with RRTs (2006, methodology): “Note that this algorithm can be used for the navigation of either a single robot or a team of robots.”
Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles (2021, neutral): “Especially RRT-Connect results in quite jagged trajectories, as it terminates on the first found connecting path.”
Humanoid motion generation system on HRP2-JSK for daily life environment (2005, background): “RRT connect planner [10], then generate motions of fixed limbs.”
A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, background): “Meanwhile, [106] proposed a bidirectional search approach between the initial and goal trees to rapidly grow towards each other, making a connection on both trees to generate the shortest path for uniform searching.”
Automated Construction of Robotic Manipulation Programs (2010, methodology): “1) shows the modifications to the RRT-Connect algorithm [Kuffner and LaValle (2000)] that samples goals.”
Sampling-Based Robot Motion Planning: A Review (2014, methodology): “Biasing attempt to greedily connect the goal configuration to the current tree [96].”
Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge (2017, methodology): “On average, motion planning using RRTConnect [4] planner takes 0.”
Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs (2014, methodology): “BIT* was compared to the OMPL implementations of RRT, RRT-Connect [39], RRT*, Informed RRT*, and FMT*.”