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 …

Aiming at the formation and path planning of multirobot systems in an unknown
environment, a path planning method for multirobot formation based on improved Q …

This paper proposes a noble multi-robot path planning algorithm using Deep q learning
combined with CNN (Convolution Neural Network) algorithm. In conventional path planning …

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 …

For path planning of mobile robots in static environment, the traditional Q-learning
algorithms suffer from slow convergence speed, difficulty in converging to the optimal …

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 …

Abstract The Q-Learning (QL) algorithm is widely used for path planning. As the scene in
which the mobile robot is located becomes complex, the algorithm suffers from the …

Mobile robots with artificial intelligence are more and more popular on the rescue and
human-service in complex environment. Path planning techniques for robots become the …

Recently, path planning of multi-autonomous mobile robot systems is one of the interesting
topics in scientific research due to its complexity and its wide use in many fields, such as …

In traditional path planning methods based on Deep Q-Network (DQN) algorithm for mobile
robots, the target Q value was usually obtained by a greedy algorithm, and the estimation of …