Improved fast replanning for robot navigation in unknown terrain

By Sven Koenig, Maxim Likhachev. Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002

354 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e4ed63ae63e6a8634e115925284f8c644b943623

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Citations in the corpus

The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance (2009, methodology): “Similar ly, the applicability of dynamic search processes, such as the D family of algorithms (Stentz, 1995; Koenig and Likhachev, 2002) and anytime methods (Van den Berg et al.”
Fast replanning for navigation in unknown terrain (2005, methodology): “We now use LPA* to develop D* Lite [22], that repeatedly determines shortest paths between the current vertex of the robot and the goal vertex as the edge costs of a graph change while the robot moves towards the goal vertex.”
Replanning with RRTs (2006, methodology): “The resulting algorithm, Dynamic Rapidly-exploring Random Trees, is a probabilistic analog to the widely-used D* family of deterministic replanning algorithms [7], [8].”
Using interpolation to improve path planning: The Field D* algorithm (2006, methodology): “• DOI: 10.1002/rob.20109 cremental A*, and D* Lite are extensions of A* that incrementally repair solution paths when changes occur in the underlying graph Stentz, 1995; Koenig & Likhachev, 2002a, b, c .”
Safe motion planning in dynamic environments (2005, positive): “Beside early work based on dynamic programming, like the dynamic A* algorithm (D*) [20], [21], the approach of motion planning has been mainly reconsidered with the new tools of probabilistic techniques based on graph or tree construction, showing impressive results [10], [11].”
Anytime Dynamic A*: An Anytime, Replanning Algorithm (2005, methodology): “To cope with imperfect information and dynamic environments, efficient replanning algorithms have been developed that correct previous solutions based on updated information (Stentz 1994; 1995; Koenig & Likhachev 2002b; 2002a; Ramalingam & Reps 1996; Barto, Bradtke, & Singh 1995).”
Lifelong Planning A (2004, methodology): “We have extended LPA* to solve the same path-planning problems as D*, resulting in our D* Lite [50].”
Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles (2008, methodology): “To cope with imperfect initial information and dynamic environments, efﬁcient incremental variants of A* search have been developed that update previous solutions based on new information (e.g. from sensors) [21, 22, 23].”
Survey of Robot 3D Path Planning Algorithms (2016, background): “Where LPA [48] represents Lifelong Planning A algorithm which is a repeating version of A (i.”
Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments (2010, methodology): “Much of the prior work on search algorithms for path planning (Ersson and Hu 2001 Koenig and Likhachev 2002 Ferguson and Stentz 2005 Nash et al. 2007) yields fast algorithms for discrete state spaces, but those algorithms tend to produce paths that are nonsmooth and do not generally satisfy the…”