Abstrmt-In this paper we present a new appmach to path planning in high-dimensional static conlignration spaces The concept of cell decomposition is combined with probabilistic sampling to obtain a method called Probabilistic Cell Decomposition (PCD). The use of lazy evaluation techniques and supervised sampling in important areas leads to a very competitive path planning method. It is shown that PCD is probabilistic complete. PCD is easily scalable and applicable to many diflerent kinds of problems. Experimental results show that PCD perform well under various conditions. Rigid body movements, maze like problems as well as path planning problems for chainlike robotic platforms have been solved successfully using the pcoposed algorithm.
Lingelbach REF combined cell composition with probabilistic sampling to obtain a method called Probabilistic Cell Decomposition.
Path planning using probabilistic cell decomposition
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
Mathematics Computer Science