• Corpus ID: 7734815

Anytime Dynamic A*: An Anytime, Replanning Algorithm

  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},
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

  • D. FergusonA. Stentz
  • Computer Science
    Proceedings 2007 IEEE International Conference on Robotics and Automation
  • 2007
A sampling-based path planning and replanning algorithm that produces anytime solutions that provides low-cost solutions to high-dimensional search problems involving partially-known or dynamic environments.

A New Anytime Dynamic Navigation Algorithm

In this paper, a new algorithm named Improved Anytime D*(IAD*) is introduced and experiment comparison is made to show IAD* better outperforms Any time D* in various random benchmarks.

iADA*: Improved Anytime Path Planning and Replanning Algorithm for Autonomous Vehicle

The iADA* algorithm is designed to provide an efficient solution to a complex, dynamic search environment when the locally changes affected, and is based on the currently popular anytime heuristic search algorithm, which is Anytime Dynamic A*(ADA*).

Anytime path planning and replanning in dynamic environments

This work presents an efficient, anytime method for path planning in dynamic environments that takes into account all prior information about both the static and dynamic elements of the environment, and efficiently updates the solution when changes to either are observed.

Anytime incremental planning with E-Graphs

This work extends planning with Experience Graphs to work in an anytime fashion so a first solution is found quickly using prior experience so that the dependence on this experience is reduced in order to produce closer to optimal solutions.

Replanning: A powerful planning strategy for hard kinodynamic problems

The results show that using a sampling-based planner in a loop, this paper can guide the robot to its goal using a low dimensional navigation function using only bounded memory.

Anytime Motion Replanning in State Lattices for Wheeled Robots

The state lattice approach for motion planning of wheeled robots usign the AD* algorithm is applied and the planning algorithm is anytime and dynamic, i.e., the path is improved incrementally and the algorithm can replan.

Way Point Based Deliberative Path Planner for Navigation

An Anytime Genetic algorithm combines the benefits of an Anytime and an Evolutionary Algorithm to efficiently provide solutions to complex, Dynamic Search Problems.

ITOMP: Incremental Trajectory Optimization for Real-Time Replanning in Dynamic Environments

We present a novel optimization-based algorithm for motion planning in dynamic environments. Our approach uses a stochastic trajectory optimization framework to avoid collisions and satisfy

Anytime search in dynamic graphs




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