Nowadays autonomous robots are becoming more popular, for example, in the industry automated forklifts and loaders are used. Most of these use motion-planning algorithms to execute their task. In the last decades numerous algorithms have been developed, which proved to be efficient in solving the problems of motion-planning.
In this paper we developed two known motion-planning algorithms to a differential robot. The first is the Rapidly Exploring Random Trees (RRT) algorithm, which searches for a path to the given target on a geometrical map. The other is the A star search, which uses a grid map to plan the path. We examined the two algorithms using simulation.
We implemented a state-space regulation, so the robot could move more precisely.
We also wrote an exploring method next to the motion-planning algorithms, and modified a line extraction algorithm (Split and Merge), so it would work better in noisy environment.