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

AI based Algorithms of Path Planning, Navigation and Control for Mobile Ground Robots and UAVs