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. ; Postprint (published version)
Sampling-based path planning on configuration-space costmaps
2010-01-01
Article (Journal)
Electronic Resource
English
costmap planning , Planning / Artificial intelligence , robots , sampling-based motion planning , Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial artificial , Planificació / Intel·ligència artificial , Classificació INSPEC::Cybernetics::Artificial intelligence::Planning (artificial intelligence) , path quality
DDC: | 629 |
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