Anytime Dynamic A*: An Anytime, Replanning Algorithm

By Maxim Likhachev, D. Ferguson, Geoffrey J. Gordon, Anthony Stentz, Sebastian Thrun. International Conference on Automated Planning and Scheduling, 2005

709 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ecc3671d1bab0f0304ace3bd30087f13e7b5c9d8

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Citations in the corpus

Optimal path planning of mobile robots: A review (2012, background): “path planning problem is the configuration space (Cspace) approach (Lozano-Perez and Wesley, 1979). Though the idea was also exercised by Udupa (1977) in his doctoral thesis, it was Lozano-Perez who extensively used this in the perspective of path planning.”
Using interpolation to improve path planning: The Field D* algorithm (2006, methodology): “Likhachev et al. 2003, 2005 present an approach that uses the cost-to-goal value function of the global planner to focus an anytime global 4D trajectory planner.”
A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres (2012, background): “ARA ⁄ (The Anytime Repairing A ⁄ ) and AD ⁄ (Anytime Dynamic A ⁄ ) algorithms are clearly discussed by the developers in Likhachev et al. (2005).”
Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles (2008, methodology): “More algorithmic details about AD* can be found in Likhachev et al. (2005) .”
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.”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, background): “On the other hand, Anytime Dynamic A* (ADA*) [113] combines ideas behind D* Lite and ARA* to produce an anytime search algorithm for real-time replanning in dynamic environments.”
Motion planning in urban environments (2008, background): “In parallel, researchers have concentrated on improving the quality of global planning, so that a global path can be easily tracked by the vehicle (Knepper & Kelly, 2006; LaValle & Kuffner, 2001; Likhachev, Gordon, & Thrun, 2003; Likhachev et al., 2005; Pivtoraiko & Kelly, 2005; Song & Amato, 2001).”
Team Cornell's Skynet: Robust perception and planning in an urban environment (2008, methodology): “To efficiently generate complex plans over large, obstacle-laden environments, the planner relies on an anytime, replanning search algorithm known as Anytime D∗ ( Likhachev, Ferguson, Gordon, Stentz, & Thrun, 2005 ).”