Path planning is a fundamental method in solving mazes or moving robots traversing
through open fields with obstacles. Q-learning method is a model-independent …

This paper deals with a new approach based on Q-learning for solving the problem of
mobile robot path planning in complex unknown static environments. As a computational …

This paper studies a path planning method for multiple robots in unknown environment.
Multiple robots adopt the leader-following formation method. For the Q-learning algorithm …

Driven by the remarkable developments we have observed in recent years, path planning
for mobile robots is a difficult part of robot navigation. Artificial intelligence applied to mobile …

Path planning in an environment with obstacles is an ongoing problem for mobile robots. Q-
learning algorithm increases its importance due to its utility in interacting with the …

Path planning is an essential algorithm in autonomous mobile robots, including agricultural
robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm …

This paper proposes an improved Q-learning algorithm for the path planning of a mobile
robot in a free-space environment. Existing Q-learning methods for path planning focus on …

The path planning method based on prior knowledge and Q-learning algorithm proposed in
this paper solves the problems of low operating efficiency and slow learning speed in …

Classical Q-learning takes huge computation to calculate the Q-value for all possible actions
in a particular state and takes large space to store its Q-value for all actions, as a result of …

Path planning is one of the key technologies of mobile robots. This paper applies the Q-
learning algorithm to path planning of mobile robots. To solve the blindness of the mobile …