The artificial potential field (APF) is a popular method of choice for robot navigation,
as it offers an intuitive model clearly defining all attractive and repulsive forces acting
on the robot [3] [25] [29] [43] [50]. However, there are drawbacks that limit the usage
of this method. For instance, the local minima problem that gets a robot trapped, and
the Goal-Non-Reachable-with-Obstacle-Nearby (GNRON) problem, as reported in [51]
[5] [23] [2] and [3]. In order to avoid these limitations, this research focuses on devising
a methodology of combining the artificial potential field with a selection of optimal
search algorithms. This work investigates the performance of the method when using
different optimal search algorithms such as the A* algorithm and the any-angle path-
planning Theta* Search, in combination with different types of artifcial potential field
generators. We also present a novel integration technique, whereby the Potential Field
approach is utilized as an internal component of an optimal search algorithm, consid-
ering the safeness of the calculated paths. Furthermore, this study also explores the
optimization of several auxiliary algorithms used in conjunction with the APF-Optimal
search integration: There are three different methods proposed for implementing the
line-of-sight (LOS) component of the Theta* search, namely the simple line-of-sight
checking algorithm, the modified Bresenham's line algorithm and the modified Cohen-
Sutherland algorithm. Contrary to the studies presented in [5], [42], [48] and [40] where
the APF and the optimal search algorithms were used separately, in this research, an
integrative methodology involving the APF inside the optimal search with a newly pro-
posed Safety Factor (SF) is explored. Experiment results indicate that the APF-A*
Search with the SF can reduce the number of state expansions and therefore also the
running time up to 19.61%, while maintaining the safeness of the path, as compared
to APF-A* when not using the SF. Furthermore, this research also explores how the
proposed hybrid algorithms can be used in developing multi-objective behaviours of
single robot. In this regard, a robot soccer simulation platform with a physics engine is
developed as well to support the exploration. Lastly, the performance of the proposed
algorithms is examined under varying environment conditions. Evidences are provided
showing that the method can be used in constructing the intelligence for a robot goal
keeper and a robot attacker (ball shooter). A multitude of AI robot behaviours using
the proposed methods are integrated via a finite state machine including: defensive
positioning/parking, ball kicking/shooting, and target pursuing behaviours.
Keywords : Artificial Potential Field, Optimal Searches, Robot Navigation, Multi-
objective Behaviours.
