Sampling-Based Path Planning on Configuration-Space Costmaps

This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT)...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 26; no. 4; pp. 635 - 646
Main Authors: Jaillet, Léonard, Cortés, Juan, Siméon, T
Format: Journal Article Publication
Language:English
Published: New York, NY IEEE 01.08.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Biological system modeling
Classificació INSPEC
Computational biology
Computational modeling
Computer Science
Computer science; control theory; systems
Control theory. Systems
Cost function
Costmap planning
Criteria
Cybernetics
Exact sciences and technology
Informàtica
Intel·ligència artificial artificial
Mathematical models
Motion planning
Optimization
Optimization methods
Orbital robotics
Path planning
path quality
Planificació / Intel·ligència artificial
Planning / Artificial intelligence
Randomized algorithms
Robotics
robots
Saddle points
sampling-based motion planning
Stochastic models
Stochastic processes
Stochasticity
Testing
Trees
Valleys
Àrees temàtiques de la UPC
Quality criterion
Path planning
Real time
Saddle point
Robotics
path quality
Stochastic programming
Optimal trajectory
sampling-based motion planning
Costmap planning
Sampling
Incomplete information
Motion control
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Description
Summary:This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2010.2049527