  Today, robotics is an auspicious and fast-growing branch of technology that
involves the manufacturing, design, and maintenance of robot machines that can
operate in an autonomous fashion and can be used in a wide variety of
applications including space exploration, weaponry, household, and
transportation. More particularly, in space applications, a common type of
robots has been of widespread use in the recent years. It is called planetary
rover which is a robot vehicle that moves across the surface of a planet and
conducts detailed geological studies pertaining to the properties of the
landing cosmic environment. However, rovers are always impeded by obstacles
along the traveling path which can destabilize the rover's body and prevent it
from reaching its goal destination. This paper proposes an ANN model that
allows rover systems to carry out autonomous path-planning to successfully
navigate through challenging planetary terrains and follow their goal location
while avoiding dangerous obstacles. The proposed ANN is a multilayer network
made out of three layers: an input, a hidden, and an output layer. The network
is trained in offline mode using back-propagation supervised learning
algorithm. A software-simulated rover was experimented and it revealed that it
was able to follow the safest trajectory despite existing obstacles. As future
work, the proposed ANN is to be parallelized so as to speed-up the execution
time of the training process.
