D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments
This addresses path planning for robots in dynamic settings, but it appears incremental as it builds on existing Q-learning methods with modifications.
The paper tackles robot path planning in dynamic, uncertain environments with moving obstacles and targets by introducing a D-point trigonometric method based on Q-learning, resulting in high convergence speed and hit rate with low dependency on environmental parameters compared to an opponent approach.
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.