Fast replanning for navigation in unknown terrain

By Sven Koenig, Maxim Likhachev. IEEE Transactions on robotics, 2005

705 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e7823d7c5fdf7145e241fc70b13958815231eee8

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Citations in the corpus

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, neutral): “Examples include A* (see, e.g., [233]), D* (see, e.g., [128]), and fast marching (see, e.g., [89]).”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, methodology): “On the other hand, Anytime Dynamic A* (ADA*) [119] combines ideas behind D* Lite and ARA* to produce an anytime search algorithm for real-time replanning in dynamic environments.”
A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres (2012, background): “Other more efﬁcient variations exist in the form of and Fo-cussed D ⁄ and D ⁄ Lite (Koenig and Likhachev, 2005), which is a modiﬁed version of the LPA ⁄ (Lifelong Planning A ⁄ ) heuristic (Likhachev and Koenig, 2005).”
Survey of Robot 3D Path Planning Algorithms (2016, background): “Koenig and Likhachev [56] extended LPA ∗ to the case where the goal changes between replanning episodes; it is like a simplified version of D ∗ , called D ∗ Lite, but it is proposed based on LPA ∗ .”
A Fully Autonomous Indoor Quadrotor (2012, background): “A variation of the roll translates in a motion along the y axis, a variation in the pitch results in a motion along the x-axis and a variation of the thrust results in a change in the vertical velocity.”
Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs (2014, methodology): “Graphsearch techniques have still been successful as planning algorithms [6] on a variety of graph types [7], [8], including 1 J. D. Gammell and T. D. Barfoot are with the Autonomous Space Robotics Lab at the University of Toronto, Toronto, Ontario, Canada.”
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2021, methodology): “To address this we adopt a weighted variant of the D* algorithm [39] with costs that penalize moving near obstacles.”