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 …

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) …

Path planning is an essential element in mobile robot navigation. One of the popular path
planners is Q-learning–a type of reinforcement learning that learns with little or no prior …

In this paper, a new approach is developed for solving the problem of mobile robot path
planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms …

The Q-Learning algorithm is a reinforcement learning technique widely used in various
fields such as path planning, intelligent transportation, penetration testing, among others. It …

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 …

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 …

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 …

Mobile robot path planning in an unknown environment is a fundamental and challenging
problem in the field of robotics. Dynamic window approach (DWA) is an effective method of …

In order to solve the problem of slow convergence speed and long planned path when the
robot plans a path in unknown environment by using Q-learning algorithm, we propose the …