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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Vydané v:	IEEE transactions on robotics Ročník 26; číslo 4; s. 635 - 646
Hlavní autori:	Jaillet, Léonard, Cortés, Juan, Siméon, T
Médium:	Journal Article Publikácia
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.08.2010 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	This paper addresses path planning considering a cost function defined over the configuration space. The proposed Transition-based RRT planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of RRTs 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. 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. 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. 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. [PUBLICATION ABSTRACT]
Author	Cortés, Juan Jaillet, Léonard Siméon, T
Author_xml	– sequence: 1 givenname: Léonard surname: Jaillet fullname: Jaillet, Léonard email: ljaillet@iri.upc.edu organization: Inst. de Robot. i Inf. Ind., Univ. Politec. de Catalunya, Barcelona, Spain – sequence: 2 givenname: Juan surname: Cortés fullname: Cortés, Juan email: jcortes@laas.fr organization: LAAS, CNRS, Toulouse, France – sequence: 3 givenname: T surname: Siméon fullname: Siméon, T email: nic@laas.fr organization: LAAS, CNRS, Toulouse, France
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SubjectTerms	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
Title	Sampling-Based Path Planning on Configuration-Space Costmaps
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