DocumentCode :
320726
Title :
A probability-based approach to model-based path planning
Author :
Mantegh, I. ; Jenkin, M.R.M. ; Goldenberg, A.A.
Author_Institution :
Robotics & Autom. Lab., Toronto Univ., Ont., Canada
Volume :
2
fYear :
1997
fDate :
7-11 Sep 1997
Firstpage :
1189
Abstract :
By capitalizing on the known properties of harmonic potential functions this work develops a new approach to probability-based path planning that is intuitive, free from local traps (local minima) and computationally less complex than many existing methods. Although the approach presented here is based on the hill-climbing method, it is still able to guarantee goal attainment. Furthermore the algorithm presented here is able to handle arbitrary-shaped geometries and does not require any geometrical or topological approximation at the environment representation level
Keywords :
computational complexity; path planning; probability; robots; arbitrary-shaped geometries; computational complexity; environment representation; harmonic potential functions; hill-climbing method; local minima; model-based path planning; probability-based path planning; robots; Automation; Collision avoidance; Computational geometry; Computer science; Industrial engineering; Intelligent robots; Laboratories; Mechanical factors; Orbital robotics; Path planning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 1997. IROS '97., Proceedings of the 1997 IEEE/RSJ International Conference on
Conference_Location :
Grenoble
Print_ISBN :
0-7803-4119-8
Type :
conf
DOI :
10.1109/IROS.1997.655160
Filename :
655160
Link To Document :
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