3-D world modeling based on combinatorial geometry for autonomous robot navigation
In applications of robotics to surveillance and mapping at nuclear facilities, the scene to be described is fundamentally three-dimensional. Usually, only partial information concerning the 3-D environment is known a-priori. Using an autonomous robot, this information may be updated using range data to provide an accurate model of the environment. Range data quantify the distances from the sensor focal plane to the object surface. In other words, the 3-D coordinates of discrete points on the object surface are known. The approach proposed herein for 3-D world modeling is based on the Combinatorial Geometry (C.G.) Method which is widely used in Monte Carlo particle transport calculations. First, each measured point on the object surface is surrounded by a small solid sphere with a radius determined by the range to that point. Then, the 3-D shapes of the visible surfaces are obtained by taking the (Boolean) union of all the spheres. The result is a concise and unambiguous representation of the object's boundary surfaces. The distances from discrete points on the robot's boundary surface to various objects are calculated effectively using the C.G. type of representation. This feature is particularly useful for navigation purposes. The efficiency of the proposed approach is illustrated by a simulation of a spherical robot navigating in a 3-D room with several static obstacles.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6947437
- Report Number(s):
- CONF-870354-1; ON: DE87002814
- Resource Relation:
- Conference: IEEE international conference on robotics and automation, Raleigh, NC, USA, 30 Mar 1987
- Country of Publication:
- United States
- Language:
- English
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