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 …

In fact, optimizing path within short computation time still remains a major challenge for
mobile robotics applications. In path planning and obstacles avoidance, Q-Learning (QL) …

As science and technology advance rapidly, the scope of mobile robot applications
continues to expand. In mobile robotics, path planning, which has formed a relatively …

Mobile robot path planning is a key technology in the field of robotic research and
applications. Q-learning algorithm is one of the most effective methods to solve the problem …

Q-learning, a type of reinforcement learning, has gained increasing popularity in
autonomous mobile robot path planning recently, due to its self-learning ability without …

The path‐planning approach plays an important role in determining how long the mobile
robots can travel. To solve the path‐planning problem of mobile robots in an unknown …

Autonomous mobile robot path planning in unknown and dynamic environment is a crucial
task for successful mobile robot navigation. This study proposes an improved Q-learning …

Path planning is a major challenging problem for mobile robots, as the robot is required to
reach the target position from the starting position while simultaneously avoiding conflicts …

A path planning method based on Q-learning is proposed for robot arm. As reinforcement
learning, Q-learning is widely used in the field of mobile robot navigation due to its simple …

How to generate the path planning of mobile robots quickly is a problem in the field of
robotics. The Q-learning (QL) algorithm has recently become increasingly used in the field of …