Abstract
Mobile robots are robots that can move around in the physical
environment. The mobile robot must know where it is, where it should go
and how to reach the goal position without any collisions. Navigation is
very essential in many robotic applications such as autonomous cars,
agricultural robots, and space exploration missions. The main
constraints facing by researchers in mobile robot navigation are
execution time, finding shortest path, and obstacle avoidance. This
paper presents a comparison of algorithms like A*, Bidirectional Rapidly
Exploring Random Tree, Artificial Potential field, Probabilistic Road
Map (PRM) and Rapidly Exploring Random Tree (RRT) used for path
planning. The shortest path and the processing time for each algorithm
is determined in a MATLAB environment. Among the sampling-based
algorithms, the A* algorithm gives the shortest path and PRM takes the
least processing time when compared to other algorithms.