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 …

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 …

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) …

To address the issues of slow convergence speed and poor path planning performance in
dynamic obstacle environments. This paper proposes an improved Q-Learning path …

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 …

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 …

This paper presents the computation of feasible paths for mobile robots in known and
unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement …

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 …

Purpose The purpose of the paper is to propose and demonstrate a novel approach for
addressing the challenges of path planning and obstacle avoidance in the context of mobile …

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 …