D-Point Trigonometric Path Planning based on Q-Learning in Uncertain
  Environments
  Finding the optimum path for a robot for moving from start to the goal
position through obstacles is still a challenging issue. This paper presents a
novel path planning method, named D-point trigonometric, based on Q-learning
algorithm for dynamic and uncertain environments, in which all the obstacles
and the target are moving. We define a new state, action and reward functions
for the Q-learning by which the agent can find the best action in every state
to reach the goal in the most appropriate path. The D-point approach minimizes
the possible number of states. Moreover, the experiments in Unity3D confirmed
the high convergence speed, the high hit rate, as well as the low dependency on
environmental parameters of the proposed method compared with an opponent
approach.
