Multiresolution path planning for mobile robots

Kambhampati, Subbarao; Davis, Larry S.

doi:10.1109/jra.1986.1087051

Cited by 392 publications

(154 citation statements)

References 11 publications

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“…One popular approach is to use quadtrees rather than uniform resolution grids ͑Samet, 1982; Kambhampati & Davis, 1986͒. Quadtrees offer a compact representation by allowing large constant-cost regions of the environment to be modeled as single cells.…”

Section: Multi-resolution Grid Representationsmentioning

confidence: 99%

Using interpolation to improve path planning: The Field D^* algorithm

Ferguson¹,

Stentz²

2006

Journal of Field Robotics

335

177

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We present an interpolation-based planning and replanning algorithm for generating low-cost paths through uniform and nonuniform resolution grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent's motion to a small set of possible headings ͑e.g., 0, /4 , /2, etc.͒. As a result, even "optimal" gridbased planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and produce paths with a range of continuous headings. Consequently, it is particularly well suited to planning low-cost trajectories for mobile robots. In this paper, we introduce a version of the algorithm for uniform resolution grids and a version for nonuniform resolution grids. Together, these approaches address two of the most significant shortcomings of grid-based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids. We demonstrate our approaches on a number of example planning problems, compare them to related algorithms, and present several implementations on real robotic systems.

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Section: Multi-resolution Grid Representationsmentioning

confidence: 99%

Using interpolation to improve path planning: The Field D^* algorithm

Ferguson¹,

Stentz²

2006

Journal of Field Robotics

335

177

View full text Add to dashboard Cite

show abstract

“…Presented by Kambhampati and Davis [KAM86], a method using a quad-tree hierarchical representation of the workspace is exploited to gain a computational savings in the search. Their technique was more efficient because it did not consider the excess detail in parts of the space that did not substantially affect the planning operation.…”

Section: Search and Heuristic Methodsmentioning

confidence: 99%

Simultaneous Planning and Control for Autonomous Ground Vehicles

Galluzzo¹

2009

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“…This approach is borrowed from robots motion planning (Behnke, 2003;Kambhampati & Davis, 1986;LaValle, 2006;Logan & Sloman, 1997;Undeger et al, 2001). The decision about the next move (its direction, speed, etc.)…”

Section: Paths Planning Algorithms In Terrain-based Simulationmentioning

confidence: 99%

“…The goal of the method is not only computation time reduction but, first of all, using it for multiresolution path planning (to apply similarity in decision processes on different command level and decomposingmerging approach). The method differs from very effective representations of terrain using quadtree (Kambhampati & Davis, 1986) because of two main reasons: (1) elements of quadtree which represent a terrain have irregular sizes, (2) in majority applications quadtree represents only binary terrain with two types of region: open (passable) and closed (impassable). Hence, this approach is very effective for mobile robots, but it is not adequate, for example, to represent battlefield environment (Tarapata, 2003).…”

mentioning

confidence: 99%

Automatization of Decision Processes in Conflict Situations: Modelling, Simulation and Optimization

Tarapata¹

2008

Automation and Robotics

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Multiresolution path planning for mobile robots

Cited by 392 publications

References 11 publications

Using interpolation to improve path planning: The Field D^* algorithm

Using interpolation to improve path planning: The Field D^* algorithm

Simultaneous Planning and Control for Autonomous Ground Vehicles

Automatization of Decision Processes in Conflict Situations: Modelling, Simulation and Optimization

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Multiresolution path planning for mobile robots

Cited by 392 publications

References 11 publications

Using interpolation to improve path planning: The Field D* algorithm

Using interpolation to improve path planning: The Field D* algorithm

Simultaneous Planning and Control for Autonomous Ground Vehicles

Automatization of Decision Processes in Conflict Situations: Modelling, Simulation and Optimization

Contact Info

Product

Resources

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Using interpolation to improve path planning: The Field D^* algorithm

Using interpolation to improve path planning: The Field D^* algorithm