Mobile Robot Path Planing Using Gauss Potential Functions and Neural Network : Chapter 30 (CROSBI ID 26828)
Prilog u knjizi | izvorni znanstveni rad
Podaci o odgovornosti
Kasać, Josip ; Brezak, Danko ; Majetić, Dubravko ; Novaković, Branko
engleski
Mobile Robot Path Planing Using Gauss Potential Functions and Neural Network : Chapter 30
This work deals with the problem of potential field based mobile robot motion planning in unorganised environment. The new approach, using a combination of negative gradient and vortex field based on Gauss potential functions, is proposed. Radial Basis Function Neural Network (RBF Neural Network) learns the dependence between Gauss function parameters and velocity of mobile robot (or relative velocity between robot and obstacle in dynamical environment) ensuring passage between two closely spaced obstacles and smooth path condition. This approach overcomes some standard problems in classical potential field methods like local minima avoidance, problems of no passage between closely spaced obstacles, avoidance of moving obstacles and trajectory oscillations.
mobile robot, motion planning, potential field, RBF neural network, real-time control
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Podaci o prilogu
287-298.
objavljeno
Podaci o knjizi
DAAAM International Scientific Book 2002
Katalinić, Branko
Beč: DAAAM International Vienna
2002.
3-901509-30-5