• Corpus ID: 9849236

Optimal and Efficient Path Planning for Unknown and Dynamic Environments

@inproceedings{Stentz1993OptimalAE,
  title={Optimal and Efficient Path Planning for Unknown and Dynamic Environments},
  author={Anthony Stentz},
  year={1993}
}
  • A. Stentz
  • Published 1 August 1993
  • Computer Science
Abstract : The task of planning trajectories for a mobile robot has received considerable attention in the research literature. Algorithms exist for handling a variety of robot shapes, configurations, motion constraints, and environments. Most of the work assumes the robot has a complete and accurate model of its environment before it begins to move; less attention has been paid to the problem of unknown or partially-known environments. This situation occurs for an exploratory robot or one that… 

Optimal and efficient path planning for partially-known environments

  • A. Stentz
  • Computer Science
    Proceedings of the 1994 IEEE International Conference on Robotics and Automation
  • 1994
TLDR
A new algorithm, D*, is introduced, capable of planning paths in unknown, partially known, and changing environments in an efficient, optimal, and complete manner.

Can robots help each other to plan optimal paths in dynamic maps?

TLDR
A knowledge sharing architecture is proposed in which the robots share the knowledge of new obstacles and blocked paths with each other, which enables the robots to update their map with the new and remote obstacles, and plan efficient paths with the timely information.

Worst-case robot navigation in deterministic environments

TLDR
The worst-case analysis compares the performance of the robot with the optimal decision tree over the set of possible locations and gives an O(log 3 n)-approximation algorithm and also shows a Ω(log 2–e n) lower bound for the grid graphs commonly used in practice.

Transient Virtual Obstacles for Safe Robot Navigation in Indoor Environments

TLDR
This paper proposes Transient Virtual Obstacles (TVO): a method to actively block desired paths by using virtual obstacles, which can be placed or removed by a user anywhere in the map for programmable intervals.

Real-time randomized path planning for robot navigation

  • James BruceM. Veloso
  • Computer Science
    IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2002
TLDR
This work builds a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots, and demonstrates that ERRT is significantly more efficient for replanning than a basic RRT planner.

A Parallel Randomized Path Planner for Robot Navigation

TLDR
This paper illustrates how it is possible to implement a parallel version of RRT based motion planner which yields optimal speed up and introduces a parallel extension of previous RRT work, the process splitting and parallel cost penalty search with a comment on Real Time Stagnancy reduction.

The D* Algorithm for Real-Time Planning of Optimal Traverses

TLDR
A new algorithm, D*, capable of planning optimal traverses in real-time through focussed state expansion, which can be used not only for route planning but for any graph-based cost optimization problem for which arc costs change during the traverse of the solution path.

Global path and action planning for mobile robot using a spatiotemporal graph in environments with predictable moving obstacles

TLDR
This paper proposes a method to perform a global path plan by using a spatiotemporal graph for environments with moving obstacles that perform predictable behavior and shows that it is possible to adjust the robot's behavior by using A* algorithm from the balance of moving cost and waiting cost.

Sensor-based planning with the freespace assumption

TLDR
It is demonstrated that planning with the freespace assumption can make good performance guarantees on some restricted graph topologies (such as grids) but is not worst-case optimal in general.

Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning

TLDR
An extension to the hybrid A* path planner that allows autonomous underwater vehicle (AUVs) to plan paths in 3D environments is presented and it is shown that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate.
...

References

SHOWING 1-10 OF 22 REFERENCES

Dynamic path planning for a mobile automaton with limited information on the environment

TLDR
A lower bound on the length of paths generated by any algorithm operating with uncertainty is formulated, and two nonheuristic path planning algorithms are described.

A mobile robot exploration algorithm

  • A. Zelinsky
  • Computer Science
    IEEE Trans. Robotics Autom.
  • 1992
TLDR
An algorithm for path planning to a goal with a mobile robot in an unknown environment is presented and makes use of the quadtree data structure to model the environment and uses the distance transform methodology to generate paths for the robot to execute.

A 'retraction' method for learned navigation in unknown terrains for a circular robot

TLDR
The authors present an algorithmic network framework for solving the problem of learned navigation of a circular robot R, of radius delta (>or=0), through a terrain whose model is not a priori known.

A probabilistic framework for dynamic motion planning in partially known environments

  • Rajeev Sharma
  • Computer Science
    Proceedings 1992 IEEE International Conference on Robotics and Automation
  • 1992
TLDR
A probabilistic model based on discrete events that abstracts the dynamic interaction between the mobile robot and the unknown part of the environment is proposed and makes it possible to design and evaluate motion planning strategies that consider both the known portion of the environments and the portion that is unknown but satisfies a probability distribution.

Multiple robot path coordination using artificial potential fields

  • C. W. Warren
  • Physics
    Proceedings., IEEE International Conference on Robotics and Automation
  • 1990
TLDR
In the method used to perform path planning, a trial path through the c-span-time is chosen and then modified under the influence of the potential fields until an appropriate path is found.

Algorithmic framework for learned robot navigation in unknown terrains

  • N. Rao
  • Computer Science
    Computer
  • 1989
A framework is presented that uses the same strategy to solve both the learned navigation and terrain model acquisition. It is shown that any abstract graph structure that satisfies a set of four

A unified solution to coverage and search in explored and unexplored terrains using indirect control

TLDR
An algorithm which solves the coverage and search problems in either explored or unexplored terrains is described and it is proved that the algorithm guarantees complete coverage and a thorough search if physically possible.

A motion planner for multiple mobile robots

  • David ParsonsJ. Canny
  • Computer Science
    Proceedings., IEEE International Conference on Robotics and Automation
  • 1990
TLDR
An algorithm is described for planning the motions of several mobile robots which share the same workspace, where the decomposition used is based on the idea of a product operation defined on the cells in a decomposition of a two-dimensional free space.

Motion planning in a dynamic domain

  • K. FujimuraH. Samet
  • Computer Science
    Proceedings., IEEE International Conference on Robotics and Automation
  • 1990
TLDR
A method of determining whether or not there is a translational collision-free motion for a polygonal robot from an initial position to a final position and of planning such a motion, if it exists, is presented.

On motion planning amidst transient obstacles

  • K. Fujimura
  • Computer Science
    Proceedings 1992 IEEE International Conference on Robotics and Automation
  • 1992
The author discusses important class of dynamic obstacles, that is, obstacles that appear and disappear in the environment. This formulation allows modeling of a number of time-varying situations