Abstract
Several sophisticated sampling strategies have been proposed recently to address the narrow passage problem for probabilistic roadmap (PRM) planning. They all have unique strengths and weaknesses in different environments, but in general, none seems sufficient on its own. In this paper, we present a new approach that adaptively combines multiple sampling strategies for PRM planning. Using this approach, we describe an adaptive hybrid sampling (AHS) strategy using two component samplers: the bridge test, a specialized sampler for narrow passages, and the uniform sampler. We tested the AHS strategy on robots with two to eight degrees of freedom. These preliminary tests show that the AHS strategy achieves consistently good performance, compared with fixed-weight hybrid sampling strategies.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
