• Corpus ID: 7734815

Anytime Dynamic A*: An Anytime, Replanning Algorithm

@inproceedings{Likhachev2005AnytimeDA,
  title={Anytime Dynamic A*: An Anytime, Replanning Algorithm},
  author={Maxim Likhachev and Dave Ferguson and Geoffrey J. Gordon and Anthony Stentz and Sebastian Thrun},
  booktitle={International Conference on Automated Planning and Scheduling},
  year={2005}
}
We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. [] Key Method When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated…

Anytime, Dynamic Planning in High-dimensional Search Spaces

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  • 2007
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References

SHOWING 1-10 OF 32 REFERENCES

ARA*: Anytime A* with Provable Bounds on Sub-Optimality

An anytime heuristic search, ARA*, is proposed, which tunes its performance bound based on available search time, and starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows.

Heuristic Search-Based Replanning

This paper introduces a novel replanning method for symbolic planning with heuristic search-based planners, currently the most popular planners, and provides an experimental feasibility study that demonstrates the promise of SHERPA for heuristic Search-based Replanning.

The Delayed D* Algorithm for Efficient Path Replanning

  • D. FergusonA. Stentz
  • Computer Science
    Proceedings of the 2005 IEEE International Conference on Robotics and Automation
  • 2005
A new replanning algorithm is presented that generates equivalent paths to Focussed Dynamic A* while requiring about half its computation time and incrementally repairs previous paths and focusses these repairs towards the current robot position.

The Focussed D* Algorithm for Real-Time Replanning

An extension to D* that focusses the repairs to significantly reduce the total time required for the initial path calculation and subsequent replanning operations for dynamic environments where arc costs can change during the traverse of the solution path.

Path planning and navigation of mobile robots in unknown environments

  • T. ErssonXiaoming Hu
  • Computer Science
    Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180)
  • 2001
The replanning problem is solved using the network simplex method and the applicability of the planner is demonstrated by integrating it with a navigation control strategy.

Improved fast replanning for robot navigation in unknown terrain

  • Sven KoenigM. Likhachev
  • Computer Science
    Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)
  • 2002
This paper introduces an alternative to Focussed Dynamic A* that implements the same navigation strategy but is algorithmically different, and shows results that will make D*-like replanning algorithms even more popular and enable robotics researchers to adapt them to additional applications.

Efficient search and hierarchical motion planning by dynamically maintaining single-source shortest paths trees

This paper embeds a single-source shortest paths tree in the connectivity graph of the approximate representation of the robot configuration space, and develops a new, dynamic algorithm to update the single- sources tree to reflect changes to the underlying connectivity graph.

An Analysis of Time-Dependent Planning

This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to