Departmental Papers (ESE)
Abstract
A navigation function is a scalar valued function on a robot configuration space which encodes the task of moving to a desired destination without hitting any obstacles. Our program of research concerns the construction of navigation functions on a family of configuration spaces whose “geometric expressiveness” is rich enough for navigation amidst real world obstacles. A sphere world is a compact connected subset of En whose boundary is the finite union of disjoint (n-1)-spheres. In previous work we have constructed navigation functions for every sphere world. In this paper we embark upon the task of extending the construction of navigation function to “star worlds.” A star world is a compact connected subset of En obtained by removing from a compact star shaped set a finite number of smaller disjoint open star shaped sets.This paper introduces a family of transformations from any star world into a suitable sphere world model, and demonstrates that these transformations are actually analytic diffeomorphisms. Since the defining properties of navigation functions are invariant under diffeomorphism, this construction, in composition with the previously developed navigation function on the corresponding model sphere world, immediately induces a navigation function on the star world.
For more information: Kod*Lab
Document Type
Conference Paper
Subject Area
GRASP, Kodlab
Date of this Version
10-20-1988
Publication Source
IEEE International Conference on Robotics and Automation
Bib Tex
@inproceedings{rimon-conference-1989, author = {Elon Rimon and Daniel Koditschek}, title = {The Construction of Analytic Diffeomorphisms for Exact Robot Navigation on Star Worlds}, booktitle = {Proceedings of IEEE Conference on Robotics and Automation}, year = {1989}, }
Date Posted: 25 July 2014
This document has been peer reviewed.
Comments
NOTE: At the time of publication, author Daniel Koditschek was affiliated with
Yale University. Currently, he is a faculty member in the Department of Electrical
and Systems Engineering at the University of Pennsylvania.