Adaptive vs. conventional potential field approaches for solving navigation problems of a real car-like wheeled robot

Adaptive Potential Field Methods (APFMs) have been proposed in this paper and their performances have been compared among them and with that of Conventional Potential Field Method (CPFM) to solve navigation problems of the mobile robot. The performance of a potential field method (PFM) depends on its chosen attractive and repulsive potential functions and the constant terms associated with them. Robots that navigate using the CPFM may not find time-optimal path and may suffer from the deadlock situations. APFM could solve the said problems by changing the constant terms associated with the potential functions to cope with the varying situations of the environment. The performances of the proposed adaptive and CPFMs have been tested through computer simulations and on a real car-like wheeled robot. The proposed PFM is found to perform better than the conventional one.