Systems, Man, and Cybernetics

 

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• [P628] Generalized Sampling based Motion Planners with Application to Nonholonomic Systems
  Author(s): Suman Chakravorty
  
  

  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 the robot map/ workspace. The proposed planners are analyzed and shown to be probabilistically complete. The algorithms are applied to the motion planning of a nonholonomc unicycle robot in several maps of varying degrees of difficulty and results show that the generalized methods have excellent performance.

  
   

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