Generalized Sampling based Motion Planners with Application to Nonholonomic Systems Conference Paper

abstract

• In this paper, generalized versions of the probabilistic sampling based planners, Probabilisitic Road Maps (PRM) and Rapidly exploring Random Tree (RRT), are presented. The generalized planners, Generalized Proababilistic Road Map (GPRM) and the Generalized Rapidly Exploring Random Tree (GRRT), are designed to account for uncertainties in the robot motion model as well as uncertainties in the robot map/ workspace. The proposed planners are analyzed and shown to be probabilistically complete. The algorithms are tested by solving the motion planning problem of a nonholonomic unicycle robot in several maps of varying degrees of difficulty and results show that the generalized methods have excellent performance in such situations. 2009 IEEE.

name of conference

• 2009 IEEE International Conference on Systems, Man and Cybernetics

authors

• Chakravorty, Suman

published proceedings

• 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9

author list (cited authors)

• Chakravorty, S., & Kumar, S.

citation count

• 9

complete list of authors

• Chakravorty, Suman||Kumar, S

publication date

• October 2009

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics  Journal

keywords

• 40 Engineering
• 4007 Control Engineering, Mechatronics And Robotics
• 46 Information And Computing Sciences
• 4602 Artificial Intelligence

Digital Object Identifier (DOI)

• 10.1109/ICSMC.2009.5346705

International Standard Book Number (ISBN) 13

• 978-1-4244-2793-2

start page

• 4077

end page

• 4082

URL

• http://dx.doi.org/10.1109/icsmc.2009.5346705