  As the demands of autonomous mobile robots are increasing in recent years,
the requirement of the path planning/navigation algorithm should not be content
with the ability to reach the target without any collisions, but also should
try to achieve possible optimal or suboptimal path from the initial position to
the target according to the robot's constrains in practice. This report
investigates path planning and control strategies for mobile robots with
machine learning techniques, including ground mobile robots and flying UAVs.
  In this report, the hybrid reactive collision-free navigation problem under
an unknown static environment is investigated firstly. By combining both the
reactive navigation and Q-learning method, we intend to keep the good
characteristics of reactive navigation algorithm and Q-learning and overcome
the shortcomings of only relying on one of them. The proposed method is then
extended into 3D environments. The performance of the mentioned strategies are
verified by extensive computer simulations, and good results are obtained.
Furthermore, the more challenging dynamic environment situation is taken into
our consideration. We tackled this problem by developing a new path planning
method that utilizes the integrated environment representation and
reinforcement learning. Our novel approach enables to find the optimal path to
the target efficiently and avoid collisions in a cluttered environment with
steady and moving obstacles. The performance of these methods is compared with
other different aspects.
